Distributed intelligent cooperative energy-saving control method and device for industrial air conditioner, electronic equipment and computer readable storage medium

Abstract

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

Description

Technical Field

[0001] The present invention relates to the field of data processing applications, and in particular to a distributed intelligent collaborative energy-saving control method, device, electronic device and computer-readable storage medium for industrial air conditioners. Background Art

[0002] In modern industrial production, industrial air conditioning systems are crucial for maintaining a suitable production environment. With the continuous expansion of industrial scale and increasing demands for energy conservation and emission reduction, the energy consumption of industrial air conditioning systems has become increasingly prominent. Currently, most industrial air conditioning systems suffer from defects such as high energy consumption and inaccurate control.

[0003] On the one hand, traditional industrial air conditioners have significant shortcomings in data collection and transmission. For example, in large-scale industrial scenarios, thousands of devices need to be connected, but conventional communication technologies struggle to achieve low-latency interconnection and data synchronization between devices. Common protocols like MODBUS and BACNET are prone to communication bottlenecks in complex environments, and conventional network technologies cannot meet the requirements for stable data transmission from a large number of devices simultaneously. This results in the inability to obtain timely and accurate device status data and environmental parameters (such as supply and return water temperatures, indoor temperature and humidity), affecting the timeliness and accuracy of subsequent control decisions.

[0004] On the other hand, the control mechanism lacks intelligent coordination. Traditional control strategies are mostly based on simple rules, such as starting and stopping equipment based solely on a single temperature threshold, which makes it impossible to achieve dynamic regional load distribution. In the regulation of chilled water and cooling systems, fixed grading models are also often used, without real-time adjustments based on actual load changes, making it difficult to form a global optimization framework. This results in low overall system operating efficiency and serious energy waste. At the same time, traditional systems lack the ability to generate dynamic control strategies based on real-time data, making it impossible to optimize energy-saving plans in real time based on operating conditions. The lack of in-depth integration and analysis of energy consumption data makes it difficult to achieve functions such as energy consumption tracing and participation in grid frequency regulation, and cannot meet the needs of industrial enterprises for refined energy management and efficient utilization.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore includes information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0006] The purpose of this application is to provide a distributed intelligent collaborative energy-saving control method, device, electronic device, and computer-readable storage medium for industrial air conditioners. This method, at least to a certain extent, overcomes the problems of existing technologies. Through distributed intelligent collaboration, it upgrades industrial air conditioners from "stand-alone operation" to "global optimization," achieving refined energy management and control. Furthermore, it enables reliable traceability of energy consumption data, aggregates distributed air conditioning systems to participate in grid frequency modulation, and provides virtual power plant functionality.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to one aspect of the present application, a distributed intelligent collaborative energy-saving control method for industrial air conditioners is provided, comprising: obtaining data related to the operation of the industrial air conditioner, including environmental parameters and equipment status data, wherein the environmental parameters include supply and return water temperature, pipeline pressure, and refrigerant flow; transmitting and processing the data related to the operation of the industrial air conditioner, using industrial protocols including MODBUS and BACNET and 5G / Wi-Fi6 technology to achieve low-latency interconnection and data synchronization of thousands of devices, and transmitting the collected data to a decision-making layer; constructing an intelligent collaborative control mechanism based on the needs of multi-device collaboration and hierarchical adjustment, including a multi-device linkage strategy for dynamically allocating refrigeration resources based on regional cooling and heating loads, and a hierarchical adjustment algorithm for the chilled water system and the cooling system, to form a control framework for achieving global optimization; processing the environmental parameters and equipment status data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis results and the dynamic control strategy; integrating and processing various operating data and control effects generated based on the target energy-saving control information, displaying the energy consumption ranking of each region through a data visualization platform, and generating an optimization plan for high-energy-consuming links to achieve reliable traceability of energy consumption data and aggregate distributed air conditioning systems to participate in grid frequency regulation.

[0009] Another aspect of the present application is a distributed intelligent collaborative energy-saving control device for industrial air conditioners, comprising: an acquisition module for acquiring industrial air conditioner operation-related data, including environmental parameters and equipment status 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 achieve low-latency interconnection and data synchronization of thousands of devices, and transmit the collected data to the decision-making layer; 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 refrigeration resources based on regional cold and hot loads, and a hierarchical adjustment algorithm for chilled water systems and cooling systems, to form a control framework for achieving global optimization; environmental parameters and equipment status data are processed to generate dynamic control strategies; target energy-saving control information is generated based on data analysis results and dynamic control strategies; various operating data and control effects generated based on the target energy-saving control information are integrated and processed, and the energy consumption rankings of each region are displayed through a data visualization platform, and optimization plans for high-energy-consuming links are generated to achieve reliable traceability of energy consumption data and aggregate distributed air conditioning systems to participate in grid frequency regulation.

[0010] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned distributed intelligent collaborative energy-saving control method for industrial air conditioners is implemented.

[0011] This application provides a distributed intelligent collaborative energy-saving control method, device, electronic device, and computer-readable storage medium for industrial air conditioners. By acquiring environmental and device data, they utilize MODBUS and BACNET protocols and 5G / Wi-Fi 6 to achieve low-latency interconnection among thousands of devices. A collaborative mechanism, including load distribution and system adjustment algorithms, is established to generate dynamic control strategies and energy-saving information. Through data integration and visualization, high-energy consumption optimization solutions are generated, enabling energy consumption traceability and grid frequency regulation.

[0012] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart showing a distributed intelligent collaborative energy-saving control method for industrial air conditioners provided by one embodiment of the present application is shown;

[0014] Figure 2 A structural diagram of a distributed intelligent collaborative energy-saving control device for industrial air conditioners provided in one embodiment of the present application is shown. DETAILED DESCRIPTION

[0015] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0016] The following combination Figure 1 To describe the distributed intelligent collaborative energy-saving control method for industrial air conditioners according to an exemplary embodiment of the present application. Figure 1 As shown:

[0017] S101, obtaining industrial air conditioning operation-related data, including environmental parameters and equipment status data.

[0018] Among them, environmental parameters include supply and return water temperature, pipeline pressure, and refrigerant flow rate.

[0019] In one implementation, obtaining data related to industrial air conditioning operation is a fundamental part of the entire control method and system, aiming to provide data support for subsequent intelligent collaborative control by comprehensively collecting environmental and equipment information. The details are as follows:

[0020] Environmental parameters focus on the external and pipeline environmental conditions in which the air-conditioning system operates, and mainly include three types of core data: Supply and return water temperature: refers to the temperature of the chilled water or cooling water at the supply and return ends of the air-conditioning system, and is a key indicator reflecting the efficiency of cold transmission. Pipeline pressure: refers to the pressure value in the pipeline that transports refrigerant (such as chilled water and cooling water) in the air-conditioning system. It is used to monitor whether the pipeline is operating normally and avoid system failures caused by excessively high or low pressure. Refrigerant flow: refers to the volume or mass of refrigerant (cold water, hot water, refrigerant) passing through the pipeline per unit time, which is directly related to the cold transmission capacity. Equipment status data mainly reflects the operating conditions of the various components of the industrial air-conditioning equipment, including the operating frequency of the compressor, the speed of the water pump, the start and stop status of the fan, the operating time of the unit, etc., and is used to evaluate whether the equipment is operating normally and its energy efficiency level.

[0021] For example, in a large-scale machining workshop, the temperature sensor for the air conditioning chilled water system indicates a supply temperature of 8°C, while the return temperature sensor indicates a return temperature of 13°C, a 5°C difference. This reflects the cooling capacity consumption of the heat generated by the workshop's equipment. The pressure transmitter in the chilled water pipeline measures a pressure of 0.35 MPa, which is within the normal operating pressure range of the system (0.3-0.4 MPa), indicating no blockages or leaks. The refrigerant flow meter indicates a flow rate of 60 m³ / h. Combined with the supply and return water temperatures, the current cooling capacity can meet the cooling needs of the workshop's core processing areas, such as the CNC machine tool cluster. Of the three air conditioning units in the workshop, Unit 1 has accumulated 800 hours of operation at a 45Hz compressor frequency; Unit 2 has accumulated 750 hours of operation at a 40Hz compressor frequency; and Unit 3 is in standby mode (accumulated 600 hours of operation). The speed of the chilled water pump is 1200r / min, the cooling fan is in operation, and the wind speed level is level 3, both of which meet the current cooling capacity distribution requirements.

[0022] Based on the system's perception and network layer architecture, this data is collected and transmitted through the following methods. The perception layer deploys temperature sensors, pressure transmitters, flow meters, and other sensing components at key points in the air conditioning system (such as water supply and return pipes, and equipment inlets and outlets) to capture environmental parameters and equipment status data in real time. The network layer utilizes industrial protocols such as MODBUS and BACNET, as well as 5G / Wi-Fi 6 technologies, to interconnect and synchronize collected data from thousands of devices with low latency, ultimately transmitting it to the decision-making layer for processing.

[0023] S102: Process the data related to the operation of the industrial air conditioner and transmit the collected data to the decision-making layer.

[0024] In one implementation, industrial protocols including MODBUS and BACNET are used to standardize the data format of industrial air conditioning operation data, ensuring that data collected by different types of sensors and devices conforms to unified transmission specifications. 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 recognized and processed by the system.

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

[0026] Leveraging 5G / Wi-Fi 6 technology, communication frequency bands are adapted and network access points are deployed based on the density of industrial equipment and data transmission bandwidth requirements to ensure data transmission channel compatibility. Based on the density of workshop equipment (e.g., densely populated machine tool clusters and sparsely populated storage 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 were deployed in the workshop machine tool cluster area (equipment density reaches 50 units / 100 square meters), leveraging its wide coverage to meet the data transmission needs of high-density equipment. Wi-Fi 6 access points were deployed in the warehouse area (equipment density is low), using the 5.8GHz frequency band to provide high-bandwidth connections. The network access points in these two areas are interconnected via fiber optic backhaul links, forming a comprehensive communication network.

[0028] A protocol conversion gateway is used to adapt the interfaces between industrial protocols and communication technologies, establishing a transmission link adaptation mechanism for environmental parameters and device status data. Deployment of a protocol conversion gateway ensures interface compatibility between industrial protocols (MODBUS / BACNET) and communication technologies (5G / Wi-Fi 6), establishing data transmission links. This addresses the adaptation issues between industrial device 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 the temperature data of the MODBUS protocol into the IP data packet format supported by the 5G network, and converts the pressure data of the BACNET protocol into the IEEE802.11ax frame format supported by Wi-Fi6. 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-Fi6 when the 5G network is congested).

[0030] For multi-type data from thousands of devices, we coordinate transmission protocols and communication standards to ensure consistent format and stable transmission. For multi-type data (environmental parameters, device status) from thousands of devices (such as hundreds of sensors and dozens of air conditioning units), we coordinate transmission protocol and communication standard parameter configuration to ensure consistent data format and stable transmission. This prevents data corruption or transmission interruptions caused by protocol and standard mismatches.

[0031] Specifically, among the 1,000 devices in the workshop, chilled water pump status data (updated frequently, every 1 second) is transmitted using the MODBUS protocol and 5G URLLC (ultra-low latency); historical refrigerant flow data (updated less frequently, every 10 seconds) is transmitted using the BACNET protocol and Wi-Fi 6 (high throughput). The system uses pre-set rules to ensure that both types of data are synchronized and formatted uniformly during transmission, such as using JSON encapsulation.

[0032] Low-latency transmission optimization mechanisms enable interconnection and data synchronization for thousands of devices, building data transmission channels between them. Low-latency transmission optimization mechanisms (such as local caching on edge nodes and data sharding) enable interconnection and real-time data synchronization for thousands of devices. This ensures the real-time delivery of control commands and feedback data, meeting the timeliness requirements of dynamic adjustment for industrial air conditioners.

[0033] Specifically, the workshop deploys 10 edge computing nodes, each responsible for managing 100 devices. When the supply and return water temperatures in a certain area suddenly rise, the edge nodes first cache local sensor data and then transmit the data in slices to adjacent nodes using 5G slicing technology. This synchronizes data across the entire workshop within 50ms, ensuring that decision-makers can quickly respond to load changes.

[0034] Using these transmission channels, collected environmental parameters and equipment status data are transmitted to the decision-making layer, completing the data flow from the sensing end to the processing end. Using these transmission channels, environmental parameters (such as supply and return water temperature and pipeline pressure) and equipment status data (such as compressor frequency and pump speed) collected by the sensing layer are transmitted to the decision-making layer (edge ​​computing and cloud computing platforms). This completes the data flow from the collection end to the processing end, providing input for the subsequent AI algorithm to generate control strategies.

[0035] Real-time data collected by the workshop's sensor layer (such as 8°C water supply temperature, 0.35MPa pipeline pressure, and 50Hz compressor frequency) is aggregated through transmission channels and sent to the cloud computing platform and regional edge servers in the workshop's central control room. High-frequency pressure data is preferentially transmitted to the edge servers for rapid local adjustments, 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 regulation, builds an intelligent collaborative control mechanism, including a multi-device linkage strategy that dynamically allocates cooling resources based on regional cooling and heating loads, as well as a hierarchical regulation algorithm for the chilled water system and cooling system, forming a control framework that achieves global optimization.

[0037] In one embodiment, the regional cooling and heating load data and the equipment cooling capacity parameters are dynamically matched to generate a multi-device linkage resource allocation model, wherein the multi-device linkage resource allocation model is composed of load data calculated through a supply and demand balance algorithm and equipment capacity parameters. The cooling and heating load data of each area of ​​the workshop are dynamically matched with the cooling capacity parameters of the air-conditioning equipment, and the resource allocation plan is calculated through the supply and demand balance algorithm to generate a multi-device linkage resource allocation model. Specifically, the multi-device linkage resource allocation model is a supply and demand balance dynamic matching model, and its model structure is as follows: input layer (regional load data, equipment cooling capacity parameters) → calculation layer (supply and demand balance algorithm) → output layer (cooling capacity allocation plan). The 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 current cooling capacity of the equipment to the 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 (storage area, cooling load 300kW). The three air conditioning units in the workshop have cooling capacities of 600kW, 600kW, and 500kW, respectively. Using a supply-demand balancing algorithm, Area A is allocated Unit 1 (600kW) plus a partial load of Unit 3 (200kW). Area B is allocated Unit 2 (500kW), and Area C is covered by the remaining 300kW of Unit 3. This ensures a precise match between load and equipment capacity, avoiding redundant equipment operation.

[0039] 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, wherein the temperature difference-pressure difference composite control algorithm is generated by a cooperative regulation logic and a water pump frequency correlation model. The supply and return water temperature difference and pipeline pressure difference signals of the chilled water system are analyzed and processed to generate a control algorithm for adjusting the water pump frequency through a cooperative regulation logic and a water pump frequency correlation model. The algorithm takes the temperature difference signal (reflecting the cooling demand) as the main factor and the pressure difference signal (reflecting the pipeline resistance) as the constraint to dynamically calculate the optimal frequency of the water pump. The target temperature difference is 5℃±0.5℃, the minimum safe pressure difference is 0.2MPa, and the water pump frequency adjustment step is 1Hz / time.

[0040] The current supply and return water temperature difference of the workshop chilled water system is 3℃ (lower than the target 5℃), indicating that the cooling transmission is insufficient; the pipeline pressure difference is 0.3MPa (higher than the minimum safe pressure difference 0.2MPa). The algorithm determines through the cooperative regulation logic that the water pump frequency needs to be increased to increase the flow and improve the cooling transmission efficiency. Combined with the water pump frequency correlation model, the frequency is adjusted from 30Hz to 35Hz, and the final temperature difference rises to 4.8℃, and the pressure difference stabilizes at 0.28MPa, meeting the optimization target.

[0041] The temperature difference data and environmental temperature parameters of the cooling system are logically integrated and processed to generate a temperature difference triggered fan speed regulation and anti-freezing logic fusion algorithm, wherein the temperature difference triggered fan speed regulation and anti-freezing logic fusion algorithm is composed of a safety and energy efficiency priority determination rule fusion based on the temperature difference signal. The inlet and outlet water temperature difference and environmental temperature parameters of the cooling system are logically integrated to generate a control algorithm that fuses fan speed regulation and anti-freezing protection based on safety and energy efficiency priority determination rules.

[0042] When the environmental temperature is >5℃, the energy efficiency is prioritized: the fan speed is adjusted according to the temperature difference (target temperature difference 8℃), and the speed is increased by 10% for every 1℃ of temperature difference; when the environmental temperature is ≤5℃, the safety is prioritized: the anti-freezing logic is triggered to limit the minimum speed of the fan (not less than 30% of the rated speed) to prevent pipeline icing. The target temperature difference is 8℃, the anti-freezing trigger threshold is environmental temperature ≤5℃, and the fan speed adjustment range is 30%-100%.

[0043] Under summer conditions, the inlet and outlet water temperature difference of the cooling system is 10℃ (higher than the target 8℃), and the environmental temperature is 25℃. The algorithm prioritizes energy efficiency, adjusts the fan speed from 50% to 70%, and the temperature difference decreases to 8.2℃; under winter conditions, the environmental temperature is 3℃, and the temperature difference is 6℃ (lower than the target). The algorithm triggers the anti-freezing logic, and even if the temperature difference is not up to standard, the fan speed remains at 30% to prevent pipeline freezing.

[0044] The multi-device linkage resource allocation model, temperature difference-pressure difference composite control algorithm, temperature difference triggered fan speed control and anti-freezing logic fusion algorithm are integrated to realize the organic connection of "device collaborative scheduling" and "single system precise regulation" through global optimization of the objective function. The process takes "minimizing the total energy consumption cost + equipment maintenance coefficient x running time" as the core target, breaks the limitations of traditional industrial air conditioning "single machine independent control", and forms a global regulation mode of "regional linkage + system collaboration".

[0045] The core function of the device linkage strategy is to realize the dynamic scheduling of cross-regional cold energy. Based on the real-time calculation results of the multi-device linkage resource allocation model, the refrigeration resources in the cold energy redundant area are allocated to the load gap area to avoid idle or redundant operation of the equipment under the premise of meeting the load demand of each area. The scheduling basis is the real-time difference of regional cold and heat load (such as the current cold energy demand of area A is 500kW, the actual output is 700kW, and the redundancy is 200kW), and the pipeline transmission loss coefficient (the loss rate of cold energy transmission in the pipeline, usually 5%-8%). In the cold energy scheduling process, the supply and return water temperature difference should be ≥3℃ (to ensure effective transmission of cold energy), and the pipeline pressure should be stable at 0.3-0.5MPa (to avoid pressure fluctuation affecting system safety).

[0046] For the refrigerated water system, cooling system and other subsystems, parameter optimization is realized through independent algorithm models to ensure the maximum operating efficiency of the single system, and to provide a stable cold energy output basis for the device linkage strategy.

[0047] The refrigerated water system is based on the temperature difference-pressure difference composite control algorithm. When the cold energy scheduling leads to a change in flow, the water pump frequency is automatically adjusted (such as an increase of 10% in cold energy transmission, and an increase of 8%-10% in water pump frequency), to maintain the coordinated balance of temperature difference and pressure difference.

[0048] The cooling system uses the temperature difference triggered fan speed control and anti-freezing logic fusion algorithm to dynamically adjust the fan speed according to the change in cold energy output (such as a 20% reduction in cold energy output, a 15%-20% reduction in fan speed), and to start the anti-freezing logic when the ambient temperature is ≤5℃ to prioritize equipment safety.

[0049] In the objective function Min(total energy consumption cost + equipment maintenance coefficient x running time), the specific meaning and value of each parameter are as follows: the total energy consumption cost is the real-time electricity price (such as 1.2 yuan / degree during peak hours and 0.5 yuan / degree during valley hours) x actual power consumption of equipment (cumulative power consumption of compressors, pumps, fans, etc.), which reflects the cost control goal of "time-of-use electricity price strategy to reduce peak electricity consumption". The equipment maintenance coefficient is set according to the type of equipment (compressor 1.2, pump 0.8, fan 0.6), and the longer the running time, the higher the maintenance cost, which matches the energy-saving function of "based on equipment running time balancing algorithm to rotate on-off units to prolong equipment life".

[0050] Taking the actual scene of a large mechanical processing workshop as an example, the running process of the intelligent collaborative control mechanism is described: the initial state is that the cold load of area A is 800kW (borne by 1# unit 600kW + 3# unit 200kW), the cold load of area B is 500kW (borne by 2# unit), and the cold load of area C is 300kW (borne by the remaining 300kW of 3# unit). The frequency of the chilled water system pump is 35Hz, the fan speed of the cooling system is 70%, the total energy consumption is 120kW / h, and the cumulative running time of the equipment is: 1# unit 800h, 2# unit 750h, 3# unit 600h. The trigger condition is that the cold load of area A suddenly drops to 500kW due to equipment shutdown, and 1# unit (600kW) has 100kW of cold redundancy (considering a pipeline transmission loss of 5%, the actual schedulable cold capacity is 95kW).

[0051] The current load of area B is 500kW, and if it receives 95kW of cold capacity scheduled by area A, the load of 2# unit 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 area B is maintained at 5°C (meeting the cold transmission requirement), the pipeline pressure is 0.38MPa (within the safe range), so the scheduling is executed.

[0052] The chilled water system causes the pipeline flow to change due to cold scheduling, and the temperature difference-pressure difference compound control algorithm detects that the supply and return water temperature difference rises from 5°C to 5.5°C, and the pressure difference rises from 0.35MPa to 0.38MPa, automatically adjusting the water pump frequency from 35Hz to 32Hz, so that the temperature difference falls to 5.2°C, and the pressure difference stabilizes at 0.36MPa. In the cooling system, the output load of 1# unit is reduced from 600kW to 500kW, and the cooling temperature difference is reduced from 8°C to 7°C. The temperature difference triggers the fan speed regulation algorithm to adjust the fan speed from 70% to 60%; at the same time, the ambient temperature is 15°C (higher than the anti-freezing threshold of 5°C), and the anti-freezing logic does not need to be started.

[0053] The intelligent collaborative control mechanism is framed and encapsulated to generate a control framework that achieves global optimization, wherein the framed and encapsulated mechanism positions the functions of each module and adapts the interfaces through hierarchical control logic. The intelligent collaborative control mechanism is framed and encapsulated to position the functions of each module and adapt the interfaces through hierarchical control logic, forming a control framework that achieves global optimization.

[0054] The control framework comprises the following. Decision layer: edge computing node (responsible for regional level optimization) + cloud computing platform (responsible for global strategy generation); execution layer: device controller (receives instructions and adjusts the unit, water pump, fan); interface adaptation: standardized communication interface (supports data interaction with the perception layer and the network layer). The functional positioning is as follows, decision layer: update the global strategy every 10 seconds, handle cross-regional coordination requirements; execution layer: respond to adjustment instructions every 1 second to ensure real-time optimization of parameters.

[0055] After the encapsulation of the framework, the central control room of the workshop monitors the global through a data visualization platform: the decision layer cloud computing platform generates a “peak shifting operation” strategy (avoiding the peak electricity price period of 18:00-22:00), and the edge node fine-tunes the regional parameters according to the real-time load; the execution layer unit controller, water pump frequency converter, etc. receive instructions through the standardized interface to realize the closed-loop control of “global strategy → regional execution → parameter feedback”, and finally realize a 20% reduction in energy consumption during the peak period.

[0056] S104, processing the environmental parameter and device state data to generate a dynamic control strategy.

[0057] In an implementation manner, the environmental parameter and device state data are subjected to feature extraction based on an edge computing and cloud computing fusion platform, and historical cold quantity data and real-time load parameters are mapped to a unit start-stop optimization space through a load prediction model in a time sequence weight, wherein the historical cold quantity demand is , the real-time environmental temperature is , and the device heat generation is , so that the cold quantity demand prediction formula at time t is , is a weight coefficient, is a historical data time sequence weight, is a reference temperature. The technical logic of calculating the cold quantity demand at time t through the load prediction model is a core step of the “generating a dynamic control strategy” link, and the core is fusion analysis based on historical data and real-time parameters to accurately predict the cold quantity demand and provide a basis for unit start-stop and load distribution.

[0058] The types of input data are as follows, historical cold quantity demand : air conditioning cold quantity output data of the workshop in the past period of time (such as the last 1 hour, 3 hours, or 8 hours), reflecting the load law at different time periods (such as the cold quantity demand is high from 9:00 to 11:00 in the morning due to the concentration of device start-up). Real-time environmental temperature : the current environmental temperature collected by the temperature sensor deployed in the workshop, for example, the environmental temperature in the workshop rises to 32℃ in the afternoon in summer, which will directly increase the cold quantity demand. Device heat generation Heat generated by machine tools, production lines, and other equipment in a workshop. For example, a batch of heavy-duty machine tools, operating at full load, can generate up to 200kW per hour. Based on a converged edge computing and cloud computing platform, this data is preprocessed (e.g., filtering outliers and aligning time series) to extract key features (e.g., the correlation between ambient temperature and equipment heat generation, and the fluctuation period of historical cooling capacity) to provide high-quality input for the prediction model. The load prediction model weightedly integrates the patterns of historical cooling data, the impact of real-time ambient temperature, and the immediate contribution of equipment heat generation to generate a cooling demand forecast for time t (e.g., 15 minutes or one hour in the future), thus avoiding energy waste or insufficient cooling due to load estimation errors.

[0059] Weight coefficient ( ): respectively represent the impact of historical cooling data, ambient temperature changes, and equipment heat generation on the current cooling demand. For example, in a processing area with dense equipment, (Equipment heat weight) takes a higher value (such as 0.3); in the storage area, (Historical data weight) takes a higher value (such as 0.6). Time series weight :Different weights are assigned to historical cooling data of different periods. Recent data (such as the past hour) have a higher weight (such as 0.7), while long-term data (such as 8 hours ago) have a lower weight (such as 0.1), ensuring that the model is more sensitive to recent trends. Baseline temperature ( ): The standard ambient temperature set in the workshop (such as 25℃), when the real-time temperature Higher than When the temperature difference increases, additional cooling capacity is required to compensate for the load increase caused by the temperature difference.

[0060] The cooling demand forecast output by the model serves as a key basis for subsequent "unit start-up and shutdown optimization" and "dynamic cooling capacity allocation." For example, if cooling demand is predicted to surge by 30% during a certain period, the system can preemptively activate backup units to avoid energy consumption spikes caused by temporary increases in capacity. This prediction accuracy directly supports the "dynamic load matching" function, ensuring temperature fluctuations are controlled within ±0.5°C, meeting workshop production environment requirements while avoiding energy waste caused by overcooling. Through the fusion of historical and real-time data, a precise load benchmark is provided for the "global optimization" of industrial air conditioning systems.

[0061] Through the data preprocessing layer and branch algorithm structure, the COP extreme value search result, the neural network prediction value and the equipment operation time balance result (unit rotation priority) are output. The COP extreme value search result is the frequency adjustment parameter of the variable frequency equipment, and the neural network prediction value is the accurate value of the cooling demand; the frequency of the variable frequency equipment is f, and the cooling capacity is , the power consumption is P , energy efficiency ratio , search for extreme values ​​by gradient ascent , The step coefficient is. The environmental parameters (such as supply and return water temperature, pipeline pressure) and equipment state data (such as compressor frequency, water pump speed) collected by the perception layer are cleaned (abnormal values are filtered), normalized (data magnitude is unified), and time-aligned (time consistency is ensured) to provide high-quality input for subsequent algorithms. For example, instantaneous pressure jump data caused by sensor failure is removed, and the operating frequencies of different devices are converted to a standardized range of 0-100 Hz. The branch algorithm uses a parallel processing architecture, and the preprocessed data is input into three independent algorithm modules (COP extreme value search, neural network prediction, and unit rotation priority calculation) simultaneously. Three types of results are output, and the unified control parameters are integrated through collaborative logic to ensure decision efficiency and accuracy.

[0062] The COP extreme value search algorithm is used for variable frequency devices (such as variable frequency compressors and variable frequency water pumps) in the workshop. By calculating the energy efficiency ratio (COP = cooling capacity Q(f) / power consumption P(f)) in real time, the optimal operating frequency is searched using the gradient ascent method, so that the device maintains the highest energy efficiency under the current load. For example, when the cooling demand in the workshop is stable, the algorithm dynamically adjusts the compressor frequency to avoid energy waste caused by high frequency or insufficient cooling caused by low frequency. The frequency adjustment parameter output by this algorithm is directly used for device control, such as fine-tuning the water pump frequency from 35 Hz to 32 Hz, which meets the cooling transmission demand while reducing power consumption.

[0063] Based on historical cooling data and real-time environmental characteristics (such as device heat output and environmental temperature), a neural network model is used to output accurate prediction values of cooling demand, providing a basis for unit start-stop and load distribution. For example, it is predicted that the cooling demand will increase by 20% in one hour due to full load operation of the machine tool cluster area, so the algorithm reserves cooling resources in advance to avoid temperature fluctuations caused by temporary adjustments.

[0064] For multiple air conditioning units (such as 3 refrigeration units) in the workshop, the rotation priority coefficient is calculated based on the cumulative operating time, and the unit with the shortest operating time is preferentially started to balance equipment wear. For example, when 1# unit has been running for 800 hours, 2# unit for 750 hours, and 3# unit for 600 hours, the algorithm determines that 3# unit has the highest priority and is preferentially started.

[0065] The outputs of these algorithms, including variable frequency equipment adjustment parameters, precise cooling demand values, and unit rotation priorities, form the core of the dynamic control strategy, directly guiding equipment operational adjustments (such as compressor frequency and pump speed) and unit start and shutdown planning. A multi-algorithm collaborative mechanism ensures a balance between energy efficiency optimization and equipment safety. For example, COP extreme value search improves individual equipment energy efficiency, unit rotation reduces maintenance costs, and neural network prediction ensures matching cooling supply and demand. These three elements together support the system's evolution from single-unit regulation to global optimization.

[0066] The cumulative operating time of k units is 、 ,..., , the goal is to minimize the deviation of each duration, the rotation priority coefficient ,(j=1,2,...,k), The larger the value, the higher the priority of the unit to be put into operation. By dynamically calculating the rotation priority of each unit, the unit with the shorter cumulative operating time is activated first, minimizing the deviation of the operating time of each unit, avoiding premature aging of some equipment due to excessive use, thereby reducing maintenance costs and extending the overall equipment life. The cumulative operating time of k air-conditioning units in the workshop ( 、 ,..., For example, a large-scale machining workshop has three refrigeration units deployed, with cumulative operating hours of 800 hours, 750 hours, and 600 hours respectively. ) is used to quantify the priority level for each unit's activation. A larger value indicates a greater discrepancy between the unit's cumulative operating time and its maximum operating time, and the higher the priority for its activation to balance losses. A coefficient is calculated through a standardized calculation based on the maximum and minimum operating times of each unit (e.g., the closer a unit's operating time is to the minimum, the closer the coefficient is to 1; the closer it is to the maximum, the closer the coefficient is to 0).

[0067] When the standby unit needs to be started due to the increase of the cold demand in the workshop, the system calculates the priority through the algorithm: if the running time of the three units is 800h, 750h and 600h respectively, the longest running time is 800h and the shortest is 600h, then the unit with the shortest running time (600h) has the largest priority coefficient and will be started first; the unit with the longest running time (800h) has the smallest priority coefficient and will be in standby state. This algorithm directly supports the device life extension goal in the "energy efficiency optimization and cost control", by balancing the running time, reducing the risk of failure caused by overuse of a single device (such as compressor wear and tear, pipeline aging), which is consistent with the function of "reducing maintenance cost" in "whole link energy efficiency management". At the same time, the priority adjustment will trigger the parameter coordination adjustment of the chilled water system and the cooling system (such as water pump frequency, fan speed) at the same time, to ensure the optimal energy efficiency of the whole system. By quantifying the priority, the resource scheduling not only meets the cold demand, but also balances the equipment wear and tear, providing equipment-level protection for the "global optimization" of industrial air conditioning systems.

[0068] The upper and lower limits of the adjustment parameters are checked in combination with the safety constraints of the equipment to avoid overload, and the real-time matching degree of the cold output and the load demand is checked to ensure the coordination of supply and demand, and the dynamic control strategy is generated. Through double checking (device safety constraint checking and supply-demand coordination checking), the reliability of the control parameters is ensured, which is the key step to connect the algorithm output and the actual device control. The device safety constraint checking (avoiding overload) sets upper and lower threshold values for the adjustment parameters (compressor frequency, water pump speed, fan speed) output by the load prediction model and the COP extreme value search algorithm, to prevent safety accidents such as overload and damage of the equipment due to operation beyond the rated working condition.

[0069] Based on the factory parameters of the equipment and the safety standards of the industrial scene, for example, the upper limit of the compressor operating frequency is set to 60Hz (to avoid overheating of the motor); the upper limit of the chilled water pump pressure is set to 0.6MPa (to prevent pipe explosion); the lower limit of the cooling fan speed is set to 30% of the rated speed (required by the winter anti-freezing logic). If the COP extreme value search algorithm outputs an optimized frequency of 65Hz for a compressor (which exceeds the safety upper limit of 60Hz), the system will automatically trigger the safety check and correct the frequency to 60Hz, ensuring that the equipment operates within a safe range.

[0070] Verify whether the cold output plan generated by the algorithm (such as unit start-stop combination, variable frequency device adjustment range) is consistent with the real-time cold demand of each area, to avoid energy waste caused by "oversupply" or temperature fluctuations caused by "undersupply". Based on the cold demand output by the load prediction model, the allowable deviation range is ≤5%, for example: the real-time cold load of A area is 500kW, the cold output planned by the algorithm needs to be within the range of 475-525kW; the supply and return water temperature difference needs to be maintained at 3-7℃ (to ensure effective transmission of cold).

[0071] If the algorithm is B zone planning for 550 kW of cold output, and the actual load demand is 500 kW (10% deviation, exceeding the allowed range), the system will trigger the matching degree check, and by reducing the compressor frequency of the corresponding unit or shutting down part of the equipment, the cold output will be corrected to 510 kW, ensuring coordination with the load demand and meeting the dynamic load matching requirement of "temperature fluctuation control within ±0.5°C" accuracy.

[0072] Double-checking is the "last line of defense" before the dynamic control strategy takes effect, which not only ensures the safety of equipment operation (such as avoiding compressor overload and burning), but also ensures the realizability of energy saving goals (such as cold supply and demand imbalance will directly lead to energy consumption increase). The check results will be fed back to the decision layer, and if the parameter exceeds the limit or the matching degree is insufficient, the system will automatically adjust the weight coefficients (such as α, β, γ) of the load prediction model or the step coefficients (η) of the COP algorithm, forming a closed loop of "algorithm optimization → check feedback → model iteration".

[0073] S105, based on the data analysis results and the dynamic control strategy, generating target energy-saving control information.

[0074] In one implementation, based on the unit start-stop timing and frequency conversion device adjustment parameters in the dynamic control strategy, combined with real-time supply and return water temperature, pipeline pressure, and equipment running time, equipment energy consumption information, preliminary energy-saving control parameters are generated. Based on the unit start-stop plan (such as 3# unit priority start) and frequency conversion device adjustment parameters (such as compressor frequency 45Hz) in the dynamic control strategy, and combining real-time environmental parameters (supply and return water temperature, pipeline pressure) and equipment state data (running time, energy consumption), preliminary device control parameters are generated.

[0075] The unit start-stop timing needs to match the real-time cold load demand (such as predicted A zone cold load 500 kW, start 1 unit 600 kW). The frequency conversion device adjustment parameter needs to be associated with the supply and return water temperature difference (such as temperature difference 5°C, water pump frequency set to 35Hz).

[0076] The workshop dynamic control strategy requires "15:00 start 3# unit (500kW), compressor frequency 40Hz; chilled water pump frequency 32Hz". Combined with real-time data: supply and return water temperature 8°C / 13°C (temperature difference 5°C), pipeline pressure 0.35MPa, 3# unit cumulative running 600 hours, generate preliminary parameters: 3# unit 40Hz running, water pump 32Hz running, plan to run for 2 hours.

[0077] Initial energy-saving control parameters were verified for device safety thresholds, filtering out parameters outside the safety range and generating safe and compliant control parameters. Initial energy-saving control parameters were verified for device safety thresholds, filtering out parameters outside the rated range to ensure equipment operation within the safe range. Safety thresholds were set based on compressor frequency ≤ 50 Hz (to prevent motor overload), pipeline pressure ≤ 0.5 MPa (to prevent pipe burst), and chilled water temperature ≥ 5°C (to prevent freezing). Verification of the initial parameter "cooling fan speed 90%" revealed that, given the current ambient temperature of 35°C, the maximum safe fan speed is 80% (to prevent mechanical wear). Therefore, the setting was corrected to 80% to generate safe and compliant parameters.

[0078] Based on safety and compliance control parameters, combined with time-of-use electricity pricing strategies and cooling capacity scheduling requirements, unit operating modes and equipment adjustment ranges are optimized and adapted to generate adaptive energy-saving control parameters. Based on safety and compliance parameters, combined with time-of-use electricity pricing strategies (e.g., peak electricity prices of 18:00-22:00, 1.2 yuan / kWh, off-peak electricity prices of 00:00-08:00, 0.5 yuan / kWh) and cooling capacity scheduling requirements (e.g., redundant cooling capacity from Area A to Area B), unit operating modes and equipment adjustment ranges are optimized. The adaptive logic prioritizes operation of units with higher energy efficiency ratios during peak hours, reducing the load on variable-frequency equipment (e.g., reducing compressor frequency from 40Hz to 35Hz). During off-peak hours, equipment loads are increased to reserve cooling capacity (e.g., by storing cold water in cold water tanks). Redundant cooling capacity from high-temperature areas (e.g., machine tool areas) is redeployed to low-temperature areas (e.g., storage areas).

[0079] Specifically, the safety compliance parameter is "Unit 3# operates at 40Hz until 18:00". Combined with the peak electricity price after 18:00, it is optimized to "reducing the frequency of Unit 3# to 35Hz at 17:30, suspending operation at 18:00, and having Unit 2# (with a higher energy efficiency ratio) bear the 300kW load". At the same time, the redundant 100kW cooling capacity of Area A is allocated to Area B to reduce energy consumption during peak periods.

[0080] Integrating full-link energy efficiency management goals, weighting and collaboratively adjusting adaptive energy-saving control parameters are performed to generate target energy-saving control information. Integrating full-link energy efficiency management goals (such as a 15% reduction in total energy consumption and a temperature fluctuation of ≤±0.5°C in each area), weighting and collaboratively adjusting adaptive parameters (such as 60% for energy-saving priority, 30% for temperature stability, and 10% for equipment life) are performed to generate final executable control information.

[0081] After the adaptability parameters are weighted, the target information is finally generated: "Unit 3# operates at 35Hz until 17:30, and after 17:30, it switches to Unit 2# operation at 30Hz; the chilled water pump frequency is fine-tuned from 32Hz to 30Hz according to the cooling capacity scheduling; the cooling capacity scheduling from area A to area B is 100kW, ensuring that the temperature in area B is stable at 25℃±0.5℃."

[0082] S106: Integrate and process various operating data and control effects generated based on the target energy-saving control information, display the energy consumption ranking of each area through the data visualization platform, and generate optimization plans for high-energy-consuming links.

[0083] In one implementation, energy consumption data for each area generated by the target energy-saving control information is quantitatively analyzed to generate quantitative energy consumption indicators. Energy consumption data (such as hourly power consumption and cooling loss) for each workshop area (Area A, machine tool cluster, Area B, assembly area, and Area C, storage area) after the implementation of the target energy-saving control information is quantified and statistically analyzed to generate comparable energy consumption indicators. Regional energy consumption per unit area (kWh / m2) reflects regional energy consumption density; cooling capacity utilization rate (actual cooling capacity consumption / total cooling capacity output) measures cooling capacity transmission efficiency; and equipment energy consumption per unit cooling capacity (kWh / kW) assesses the energy efficiency of individual equipment.

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

[0085] The effectiveness of the equipment operating parameter adjustments generated by the target energy-saving control information is evaluated and analyzed to generate an equipment efficiency factor. The equipment efficiency factor represents the contribution of equipment operation optimization to overall energy savings. The energy conservation contribution of each equipment component (compressor, water pump, fan) after parameter optimization is evaluated and generated (ranging from 0 to 1, with higher values ​​indicating higher contributions). The power consumption reduction ratio after frequency adjustment of variable-frequency equipment is measured (for example, reducing the water pump frequency from 35Hz to 32Hz reduces power consumption by 15%). Maintenance cost savings after equalizing equipment operating hours are also measured (for example, the maintenance period for unit 1 is extended by 20% due to the priority operation of unit 3). The chilled water pump efficiency factor is 0.8 (significant energy savings due to frequency optimization), the cooling fan efficiency factor is 0.6 (smaller speed adjustment range), and the compressor efficiency factor is 0.7 (optimized load distribution), reflecting the different contributions of different equipment components to overall energy savings.

[0086] Compare and analyze the historical energy consumption data associated with the target energy-saving control information with the current energy consumption trend to generate an energy consumption trend similarity factor. Compare the energy consumption trends before and after the implementation of the target control information (such as the daily energy consumption curve and the proportion of energy consumption during peak and valley periods) and calculate the trend similarity factor (ranging from 0-1, with smaller values ​​indicating more significant optimization effects). Also consider the reduction in peak energy consumption (e.g., peak energy consumption drops from 120kW to 90kW). Also consider the smoothness of the energy consumption curve (after optimization, the curve fluctuations decrease, indicating a stable control strategy). Before implementation, the energy consumption curve for Area A fluctuated significantly (±20kW) between 10:00 and 16:00. After implementation, the fluctuations decreased to ±5kW, resulting in a trend similarity factor of 0.3, indicating that the energy-saving strategy effectively mitigated energy consumption fluctuations.

[0087] Analyze the impact of the implementation of key control strategies within the target energy-saving control information to generate key strategy impact factors. These factors characterize the degree to which key control strategies affect energy consumption reduction. These factors are quantitative indicators (ranging from 0 to 1) that measure the contribution of core strategies such as cooling capacity scheduling and time-of-use electricity pricing to energy consumption reduction. The calculation process combines single-strategy isolation and combined effect analysis 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 remove 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 operations are as follows: Strategy suspension mechanism: Under the premise of keeping other control parameters unchanged, temporarily shut down the target strategy (such as cooling capacity scheduling) to restore the system to the operating state when the strategy is not enabled (such as independent cooling for each unit, without cross-regional cooling capacity allocation). Calculation of energy consumption recovery ratio: record the energy consumption per unit time (such as kW / h) before and after the strategy is suspended, and calculate the independent energy-saving effect of the strategy through the formula "energy-saving contribution rate = (energy consumption after suspension - energy consumption before suspension) / energy consumption after suspension", and then standardize it to the range of 0-1 as the influencing factor (the higher the energy-saving contribution rate, the larger the factor value).

[0089] Strategy superposition effect analysis is used to assess whether the energy-saving gains from the synergy of multiple strategies are greater than the sum of the effects of individual strategies, avoiding the offsetting effects caused by mutual constraints between strategies. Specifically, it includes: Calculation of superimposed energy-saving rate: Energy consumption is measured under the three modes of "cooling scheduling only," "time-sharing peak shifting only," and "cooling scheduling + time-sharing peak shifting," respectively, and the superimposed energy-saving rate is calculated as (1-superimposed mode energy consumption / baseline energy consumption) - (single scheduling energy-saving rate + single peak shifting energy-saving rate). Interaction impact correction: If the superimposed energy-saving rate is positive (e.g., 10%), it indicates that the strategies are synergistic and the weight of the impact factor of each strategy needs to be increased; if it is negative (e.g., -5%), it indicates that there are constraints, and the weight needs to be reduced and the strategy combination logic needs to be optimized.

[0090] Taking Area B (assembly area) of a large machining workshop and its overall system as an example, the impact factor generation process is detailed. Specifically, the impact factor of the cooling scheduling strategy is calculated as follows: With a single strategy in isolation, when the strategy is activated, Area B's energy consumption is 500 kW / h (due to the 200 kW of redundant cooling capacity from Area A, its own unit load is reduced). After the strategy is suspended, Area B requires independent cooling, and its energy consumption rises to 610 kW / h. The energy saving contribution rate is (610 - 500) / 610, which is approximately 18%. The standardized impact factor is 0.6 (18% corresponds to 0.6, because the maximum energy saving contribution rate is set at 30%, i.e., 0.6 = 18% / 30%). The combined effect is verified by combining the time-of-use and peak-shifting strategy, which reduces Area B's energy consumption to 420 kW / h. The combined energy saving rate is (1 - 420 / 610) - (18% + 12%) ≈ (31%) - 30% = 1%, indicating a positive effect, so the impact factor is maintained at 0.6.

[0091] The impact factor of the time-of-use electricity price staggering strategy was calculated as follows. When the strategy is isolated and activated (avoiding the peak electricity price between 18:00 and 22:00 and pre-installing cold storage during off-peak hours), the system's overall energy consumption is 1200 kW / h. After the strategy is suspended, the system operates at full load during peak hours, with energy consumption rising to 1360 kW / h. The energy saving contribution rate is (1360 - 1200) / 1360, which is approximately 12%. The standardized impact factor is 0.4 (12% / 30%). The combined effect is verified by combining it with cooling capacity scheduling, reducing overall energy consumption to 1050 kW / h. The combined energy saving rate is (1 - 1050 / 1360) - (18% + 12%) ≈ (22.8%) - 30%, which is -7.2%. There is a slight constraint (due to some cooling loss due to shared pipelines between cold storage and cooling capacity scheduling), so the value remains unchanged at 0.4. However, future optimization of pipeline insulation is needed to reduce the constraint.

[0092] Based on energy consumption quantitative indicators, equipment efficiency factors, energy consumption trend similarity factors, and key strategy influencing factors, combined with the energy consumption analysis model architecture, the energy consumption of each area is integrated and processed to generate a comprehensive energy consumption analysis feature. These four factors must first be converted to standardized values ​​in the 0-1 range to eliminate dimensional differences and ensure the rationality of the weighted calculation. The specific conversion rules are as follows: For the energy consumption quantitative indicators, using the energy consumption per unit area of ​​the area as an example, the maximum energy consumption threshold for the workshop is set at 10 kWh / ㎡ (corresponding to 0) and the minimum threshold is set at 2 kWh / ㎡ (corresponding to 1). The linear mapping formula is used to calculate the standardized value: (maximum threshold - actual value) / (maximum threshold - minimum threshold). For Area A's actual energy consumption per unit area of ​​8 kWh / ㎡, the standardized value is (10-8) / (10-2) = 0.25; for Area B's 5 kWh / ㎡, the standardized value is (10-5) / 8 = 0.625.

[0093] The equipment efficiency factor uses its original value (already in the 0-1 range), for example, the pump efficiency factor for Area A is 0.5, and for Area B it is 0.8. The energy consumption trend similarity factor uses an original value of 0-1 (smaller values ​​indicate better optimization results). To achieve uniform positivity, it is converted to "1-original value," for example, a factor of 0.3 in Area A to a standardized value of 0.7; a factor of 0.5 in Area B to a standardized value of 0.5. The key strategy impact factor uses its original value (already in the 0-1 range), for example, a cooling capacity scheduling impact factor of 0.6 in Area A and 0.7 in Area B.

[0094] The weight matrix (energy consumption quantitative 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 impact on energy consumption characteristics. Specifically, energy consumption quantitative index (0.4): has the highest weight, because the absolute value of the region's actual energy consumption is the core basis for judging high-energy-consuming links, and directly reflects the urgency of energy-saving needs. Equipment efficiency factor (0.2): Second most important, equipment operating efficiency is the key cause of energy consumption differences (for example, inefficient water pumps will cause energy consumption to surge). Energy consumption trend similarity factor (0.2): reflects the stability after the implementation of the strategy. The better the trend (the smaller the fluctuation), the more sustainable the energy-saving effect. Key strategy impact factor (0.2): measures the region's responsiveness to energy-saving strategies. The better the response (such as high acceptance of cooling scheduling), the greater the potential for subsequent optimization.

[0095] Comprehensive score = (normalized value of energy consumption quantification index × 0.4) + (equipment effectiveness factor × 0.2) + (normalized 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 in zone A is only 75% (standardized value 0.3), the equipment efficiency is 0.6, the trend similarity is 0.6, and the key strategy is 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 "high energy consumption" feature.

[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 weaknesses of each factor, labels are automatically generated: If the standardized value of the energy consumption quantitative indicator in a certain area is less than 0.3 (high energy consumption) and the standardized value of the cooling capacity utilization rate is less than 0.4 (low utilization), the label is "high energy consumption-low cooling capacity utilization" (such as Area A). If the score is 60-85 points (medium energy consumption) and the key strategy impact factor is greater than 0.6 (good response), the label is "medium energy consumption-good strategy response" (such as Area B).

[0097] Based on the energy consumption analysis model, we conduct in-depth research on comprehensive energy consumption analysis characteristics, generate regional energy consumption rankings, and display these rankings on a data visualization platform. Based on these comprehensive energy consumption analysis characteristics, regions are ranked from high to low by comprehensive score and displayed on a data visualization platform (such as heat maps and bar charts) to visually demonstrate energy consumption differences. Regional energy consumption is ranked by absolute value (Area A > Area B > Area C). Energy saving potential is ranked (Area A: 30% > Area B: 15% > Area C: 5%). The visualization platform shows that Area A ranks first in energy consumption, with a cooling capacity utilization rate of only 75% (below the average of 82%), marking it as a "key optimization area."

[0098] Targeted data extraction and analysis of high-energy-consuming links from the comprehensive energy consumption analysis are conducted. Optimization plans for these links are generated based on energy-saving targets and actual operating conditions. Targeted optimization measures are developed for high-energy-consuming links (e.g., high cooling loss in Area A and low water pump efficiency) based on energy-saving targets (a 15% reduction in total energy consumption) and actual operating data.

[0099] Equipment: Replace the inefficient water pump (efficiency factor 0.5) in Area A with a high-efficiency variable frequency model. Strategy: Increase cooling capacity dispatched from Area A to Area C (currently only 50kW, which can be increased to 80kW). Maintenance: Insulate the piping in Area A to reduce cooling losses (with the goal of increasing cooling capacity utilization to 85%). To address Area A's "high energy consumption and low utilization" characteristics, the solution includes: ① Directly dispatching a partial load (100kW) of Unit 3 to Area A; ② Optimizing the pump frequency to 30Hz, incorporating a temperature-pressure differential algorithm to improve flow stability; ③ Weekly calibrating the pressure in the Area A piping to reduce leakage losses.

[0100] This application achieves global energy consumption optimization through multi-device collaboration and dynamic regulation. First, it obtains environmental parameters (supply and return water temperatures, etc.) and equipment status data, and adopts MODBUS, BACNET protocols and 5G / Wi-Fi6 technology to achieve low-latency interconnection and data synchronization of thousands of devices; then it builds an intelligent collaborative control mechanism, including a multi-device linkage strategy for dynamic distribution of regional loads, and a hierarchical adjustment algorithm for chilled water and cooling systems to form a global optimization framework; then it processes the data to generate a dynamic control strategy, and combines the analysis results to generate target energy-saving control information; finally, it integrates the operating data and control effects, displays the energy consumption ranking through a visualization platform, generates an optimization plan for high-energy-consuming links, and realizes energy consumption traceability and participation in grid frequency regulation.

[0101] In one embodiment, Figure 2 As shown, the present application also provides a distributed intelligent collaborative energy-saving control device for industrial air conditioners, comprising:

[0102] Acquisition module 201, used to acquire industrial air conditioning operation-related data, including environmental parameters and equipment status data;

[0103] Processing module 202 is used to transmit and process data related to the operation of industrial air conditioners. It adopts industrial protocols including MODBUS and BACNET and 5G / Wi-Fi6 technology to achieve low-latency interconnection and data synchronization of thousands of devices, and transmit the collected data to the decision-making layer; based on the needs of multi-device collaboration and hierarchical adjustment, it builds an intelligent collaborative control mechanism, including a multi-device linkage strategy that dynamically allocates refrigeration resources according to regional cooling and heating loads, as well as a hierarchical adjustment algorithm for the chilled water system and the cooling system, to form a control framework for achieving global optimization; processes environmental parameters and equipment status data to generate dynamic control strategies; generates target energy-saving control information based on data analysis results and dynamic control strategies; integrates and processes various operating data and control effects generated based on the target energy-saving control information, displays the energy consumption rankings of each region through a data visualization platform, and generates optimization plans for high-energy-consuming links to achieve reliable traceability of energy consumption data and aggregate distributed air-conditioning systems to participate in grid frequency regulation.

[0104] Each embodiment in this application is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. In particular, the embodiments of the distributed intelligent collaborative energy-saving control device, electronic device, and computer-readable storage medium for industrial air conditioners are generally similar to the above-described embodiments of the distributed intelligent collaborative energy-saving control method for industrial air conditioners. Therefore, the description is relatively simple. For related parts, refer to the partial description of the above-described embodiment of the distributed intelligent collaborative energy-saving control method for industrial air conditioners.

Claims

1. A distributed intelligent collaborative energy-saving control method for industrial air conditioners, characterized in that: include: Obtaining data related to industrial air conditioning operation, including environmental parameters and equipment status data. Environmental parameters include supply and return water temperature, pipeline pressure, and refrigerant flow rate; Transmit and process data related to industrial air conditioning operation, using industrial protocols including MODBUS and BACNET and 5G / Wi-Fi 6 technologies to achieve low-latency interconnection and data synchronization for thousands of devices, and transmit the collected data to the decision-making level; Based on the needs of multi-device collaboration and hierarchical regulation, an intelligent collaborative control mechanism is constructed, including a multi-device linkage strategy that dynamically allocates cooling resources based on regional cooling and heating loads, as well as a hierarchical regulation algorithm for the chilled water system and the cooling system, forming a control framework that achieves global optimization. Process environmental parameters and equipment status data to generate dynamic control strategies; Generate target energy-saving control information based on data analysis results and dynamic control strategies; The various operating data and control effects generated based on the target energy-saving control information are integrated and processed, and the energy consumption rankings of each region are displayed through the data visualization platform. Optimization plans for high-energy-consuming links are generated to achieve reliable traceability of energy consumption data and aggregate distributed air-conditioning systems to participate in grid frequency regulation.

2. The method according to claim 1, wherein Transmit and process data related to industrial air conditioning operation, using industrial protocols including MODBUS and BACNET and 5G / Wi-Fi 6 technologies to achieve low-latency interconnection and data synchronization for thousands of devices, and transmit the collected data to the decision-making level, including: Adopting industrial protocols including MODBUS and BACNET, the data format of industrial air-conditioning operation-related data is standardized and converted to ensure that the data collected by different types of sensors and equipment conforms to the unified transmission specifications; Relying on 5G / Wi-Fi 6 technology, communication frequency band adaptation and network access point deployment are carried out according to the distribution density of industrial equipment and data transmission bandwidth requirements to ensure the compatibility of data transmission channels; Achieve interface adaptation between industrial protocols and communication technologies through protocol conversion gateways, and establish a transmission link adaptation mechanism for environmental parameters and equipment status data; For multi-type data of thousands of devices, complete the coordinated matching of transmission protocols and communication standards to ensure the format consistency and transmission stability of data during transmission; Through the low-latency transmission optimization mechanism, the interconnection and data synchronization of thousands of devices are realized, and the data transmission channel between devices is established; Relying on the above-mentioned transmission channels, the collected environmental parameters and equipment status data are directed to the decision-making layer, completing the flow of data from the perception end to the processing end.

3. The method according to claim 1, wherein Based on the needs of multi-device collaboration and hierarchical regulation, an intelligent collaborative control mechanism is constructed, including a multi-device linkage strategy that dynamically allocates cooling resources based on regional cooling and heating loads, as well as a hierarchical regulation algorithm for the chilled water system and cooling system. This forms a control framework for achieving global optimization, including: Dynamically match regional cooling and heating load data with equipment cooling capacity parameters to generate a multi-device linkage resource allocation model. The multi-device linkage resource allocation model is constructed by combining load data with equipment capacity parameters through a supply and demand balance algorithm. Perform compound analysis and processing on the temperature difference and pressure difference signals of the chilled water system to generate a temperature difference-pressure difference compound control algorithm. The temperature difference-pressure difference compound control algorithm is generated by the temperature difference and pressure difference parameters through coordinated adjustment logic and the water pump frequency correlation model. Perform logical integration processing on the temperature difference data and ambient temperature parameters of the cooling system 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 the temperature difference signal through the safety and energy efficiency priority judgment rules. The multi-device linkage resource allocation model, the temperature-pressure differential composite control algorithm, and the temperature-differential 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 the device linkage strategy and the 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 performs functional positioning and interface adaptation on each module of the mechanism through hierarchical control logic.

4. The method according to claim 1, wherein Process environmental parameters and equipment status data to generate dynamic control strategies, including: Based on the edge computing and cloud computing fusion platform, the environmental parameters and equipment status data are extracted, and the historical cooling data and real-time load parameters are mapped to the unit start-stop optimization space with time series weights through the load forecasting model. The historical cooling demand is , the real-time ambient temperature is , the heat generated by the equipment is , then the cooling demand prediction formula at time t is , is the weight coefficient, is the time series weight of historical data, is the reference temperature; Through the data preprocessing layer and branch algorithm structure, the COP extreme value search result, the neural network prediction value and the equipment operation time balance result are output. The COP extreme value search result is the frequency adjustment parameter of the frequency conversion equipment, and the neural network prediction value is the accurate value of the cooling demand; the frequency of the frequency conversion equipment is f, and the cooling capacity is , the power consumption is P , energy efficiency ratio , search for extreme values ​​by gradient ascent , is the step length coefficient; The cumulative operating time of k units is 、 ,..., , the goal is to minimize the deviation of each duration, the rotation priority coefficient ,(j=1,2,...,k), The larger the value, the unit will be put into operation first; Combined with equipment safety constraints, the upper and lower limits of the adjustment parameters are checked to avoid overload, and the real-time matching degree of cooling output and load demand is checked to ensure supply and demand coordination and generate a dynamic control strategy.

5. The method according to claim 1, wherein Based on data analysis results and dynamic control strategies, target energy-saving control information is generated, including: Based on the unit start-stop sequence and variable frequency equipment adjustment parameters in the dynamic control strategy, combined with real-time supply and return water temperature, pipeline pressure, equipment operating time, and equipment energy consumption information, preliminary energy-saving control parameters are generated; Perform equipment safety threshold verification on preliminary energy-saving control parameters, filter out parameters that exceed the safety range, and generate safe and compliant control parameters; Based on safety and compliance control parameters, combined with time-of-use electricity price 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. Integrate the full-link energy efficiency management goals, perform weight allocation and coordinated adjustment of adaptive energy-saving control parameters, and generate target energy-saving control information.

6. The method according to claim 5, wherein The system integrates and processes various operating data and control effects generated based on target energy-saving control information, displays the energy consumption rankings of each region through a data visualization platform, and generates optimization plans for high-energy-consuming links to achieve reliable traceability of energy consumption data and aggregate distributed air-conditioning systems to participate in grid frequency regulation, including: Conduct quantitative statistical analysis on the energy consumption data of each region generated by the target energy-saving control information to generate quantitative energy consumption indicators; Performing performance evaluation and analysis on the effects of adjusting the operating parameters of each device generated by the target energy-saving control information to generate a device performance factor, where the device performance factor is used to characterize the contribution of device operation optimization to overall energy saving; Compare and analyze the historical energy consumption data associated with the target energy-saving control information with the current energy consumption change trend to generate an energy consumption trend similarity factor; Analyze the impact of the implementation of key control strategies in the target energy-saving control information to generate key strategy impact factors, where the key strategy impact factors are used to characterize the impact of key control strategies on energy consumption reduction; Based on energy consumption quantitative indicators, equipment efficiency factors, energy consumption trend similarity factors, key strategy influencing factors, and the architecture of the energy consumption analysis model, the energy consumption of each region is integrated and processed to generate comprehensive energy consumption analysis characteristics; Based on the energy consumption analysis model, we conduct in-depth exploration of comprehensive energy consumption analysis characteristics, generate regional energy consumption ranking information, and display it through the data visualization platform; Targeted extraction and analysis of relevant data on high-energy-consuming links in the comprehensive energy consumption analysis characteristics are carried out, and optimization plans for high-energy-consuming links are generated by combining energy-saving goals with actual operating conditions.

7. A distributed intelligent collaborative energy-saving control device for industrial air conditioners, characterized in that: The device comprises: Acquisition module, used to obtain industrial air conditioning operation-related data, including environmental parameters and equipment status data; The processing module is used to transmit and process data related to the operation of industrial air conditioners. It adopts industrial protocols including MODBUS and BACNET and 5G / Wi-Fi6 technology to achieve low-latency interconnection and data synchronization of thousands of devices, and transmit the collected data to the decision-making layer; based on the needs of multi-device collaboration and hierarchical adjustment, it builds an intelligent collaborative control mechanism, including a multi-device linkage strategy that dynamically allocates refrigeration resources according to regional cooling and heating loads, as well as a hierarchical adjustment algorithm for the chilled water system and the cooling system, to form a control framework for achieving 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 the target energy-saving control information, displays the energy consumption rankings of each region through a data visualization platform, and generates optimization plans for high-energy-consuming links to achieve reliable traceability of energy consumption data and aggregate distributed air-conditioning systems to participate in grid frequency regulation.

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

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