Cold chain storage multi-temperature-zone cooperative energy-saving control method and system

Abstract

The invention discloses a cold chain storage multi-temperature-zone cooperative energy-saving control method and system. Belongs to the technical field of intelligent storage and energy management, can solve the problem of energy waste caused by independent control and neglect of thermal coupling in the prior art, realizes optimization of overall energy consumption of storage, and comprises the following steps: S1, collecting environment and energy consumption data such as temperature, humidity and equipment power in real time through a sensor network deployed in each temperature zone; s2, based on historical and real-time data, analyzing a thermal interference relation of a temperature interval, and calculating to obtain a quantized coupling interference coefficient; s3, carrying out rolling time domain optimization in a fixed period, and solving an optimal set parameter sequence of each refrigeration device in a future time period by taking the minimum total energy consumption of the system as a target and the allowable temperature range of each temperature zone as a constraint; and S4, issuing the sequence to an equipment controller for execution, monitoring the deviation between the execution effect and the optimization target in real time, and if the deviation exceeds the limit, triggering regulation or early warning.

Description

Technical Field

[0001] This invention relates to the field of smart warehousing and energy management technology, specifically a method and system for coordinated energy-saving control of multiple temperature zones in cold chain warehousing. Background Technology

[0002] A method and system for coordinated energy-saving control of multiple temperature zones in cold chain warehousing. Modern cold chain warehousing centers typically include multiple temperature zones, such as cryogenic (below -25℃), frozen (-18℃), refrigerated (0-4℃), and cool (10-15℃), to meet the storage requirements of different commodities. Currently, each temperature zone generally adopts an independent control strategy, that is, based on the feedback from sensors in this zone, the start-up, shutdown, or power of equipment such as refrigeration units and fans is adjusted through algorithms such as PID.

[0003] However, this control method has significant drawbacks: First, unavoidable heat exchange exists between adjacent temperature zones. The lower-temperature zone requires additional energy to resist heat intrusion from the higher-temperature zone, and the independent control model cannot identify and compensate for this interference, leading to increased energy consumption or temperature fluctuations. Second, to cope with extreme situations or ensure a safety margin, the system often operates in an "overcooled" state, resulting in energy waste. Third, frequent start-ups and shutdowns of equipment to maintain a narrow temperature range not only consume high amounts of energy but also reduce equipment lifespan. Finally, managers struggle to grasp the overall energy consumption distribution and energy-saving potential, lacking scientific data support for scheduling decisions. Therefore, there is an urgent need to develop a multi-temperature zone collaborative energy-saving control method and system for cold chain warehousing to address the problems in existing technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for coordinated energy-saving control of multiple temperature zones in cold chain warehousing, so as to solve the problem of energy waste caused by independent control and neglect of thermal coupling in the prior art, and to optimize the overall energy consumption of warehousing.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Its characteristic is that it includes the following steps: S1: Real-time collection of environmental and energy consumption data, including temperature, humidity, and equipment power, through a sensor network deployed in various temperature zones; S2: Based on historical and real-time data, analyze the thermal interference relationship between any two temperature zones and quantify the coupling interference coefficient between temperature zones; S3: Perform rolling time-domain optimization with a preset period, take minimizing the total operating energy consumption of the cold chain storage system as the objective function, and take the allowable temperature fluctuation range of each temperature zone as the constraint condition, and solve the optimal set parameter sequence of each refrigeration equipment in a future time period. S4: Send the optimal set parameter sequence to the equipment controller of the corresponding temperature zone for execution, and monitor the deviation between the execution effect and the optimization target in real time. If the deviation exceeds the allowable range, trigger regulation or warning.

[0006] By adopting the above technical solutions, an integrated energy-saving framework was constructed, encompassing global perception, coupled analysis, dynamic optimization, and closed-loop control. This method overcomes the limitations of traditional independent control, treating the entire warehouse as an organic thermal system. It can systematically tap the energy-saving potential generated by temperature zone coupling, significantly reducing the overall system energy consumption while ensuring the storage quality of each temperature zone.

[0007] As a further aspect of the present invention: in step S2, the coupling interference coefficient of the i-th temperature zone to the j-th temperature zone is calculated. The method is as follows: in, Let be the temperature change of the j-th temperature zone per unit time. The power change of the refrigeration equipment in the i-th temperature zone within the same time period is represented by the summation symbol, which indicates the cumulative calculation of multiple historical time segments.

[0008] By employing the above technical solution, a specific mathematical model for quantifying thermal interference is provided. This formula, by analyzing the correlation between equipment power changes and temperature changes in adjacent areas, can objectively and accurately calculate the intensity of the thermal impact of equipment operation on the surrounding environment within a specific temperature range. This makes the implicit thermal coupling relationship explicit and calculable, providing crucial data input for subsequent collaborative optimization and making the formulation of control strategies more scientific and targeted.

[0009] As a further aspect of the present invention: the rolling time-domain optimization in step S3 adopts a model predictive control framework, and its objective function is: in, To predict the time domain, The total number of devices. Let be the predicted power of the m-th device at time k. Its energy consumption weighting coefficient; The total number of temperature zones, Let be the predicted temperature of the i-th temperature zone at time k. Set the temperature for it. This is the temperature deviation weighting coefficient.

[0010] By adopting the above technical solution, the energy-saving problem is transformed into a constrained multi-objective optimization problem. The objective function cleverly unifies and balances the two core requirements of "minimizing total system energy consumption" and "stabilizing temperatures in each temperature zone." Through rolling optimization using a model predictive control framework, it can not only predict the future and seek the global optimum based on the current state and coupled model, but also make corrections based on feedback after each execution, thereby dynamically adapting to internal and external disturbances (such as warehouse door opening and goods entering and exiting), achieving forward-looking, adaptive, and robust energy-saving control.

[0011] As a further aspect of the present invention, step S4 further includes: if the real-time temperature of a certain temperature zone continues to deviate from the set range and the operating parameters of its associated equipment have reached the limit, then it is determined that the adjacent high-temperature zone has excessive thermal interference to it, and auxiliary decision-making suggestions of "strengthening regional isolation" or "adjusting scheduling strategy" are generated.

[0012] By adopting the above technical solutions, the system's fault diagnosis and intelligent decision support capabilities are enhanced. When conventional control methods fail to correct deviations, the system can automatically analyze the root cause of the problem and identify abnormal thermal interference caused by physical isolation failure or unreasonable logistics scheduling. By generating specific auxiliary decision-making suggestions, the system can be upgraded from simple automated control to an intelligent management tool, guiding maintenance personnel to perform equipment maintenance or process optimization, fundamentally eliminating the causes of high energy consumption, and realizing the extension from control to management.

[0013] A multi-temperature zone coordinated energy-saving control system for cold chain warehousing includes: The data acquisition module is used to acquire environmental data and equipment operation data in each temperature zone in real time through IoT sensors; The coupling analysis module is used to calculate and update the thermal coupling interference coefficient between each temperature zone based on the historical and real-time data of the data acquisition module. The optimization decision module is used to periodically solve for the optimal operating parameters of each refrigeration device based on the coupling interference coefficient and with the goal of minimizing the total energy consumption of the system, using a rolling optimization algorithm. The collaborative control module is used to send the optimal operating parameters to the field actuators, monitor the execution status, and provide early warnings and adjustments for abnormal deviations.

[0014] By adopting the above technical solution, a hardware and software platform for implementing the aforementioned method is provided. Through modular design, the system clarifies the responsibilities and data flow of each functional unit, forming a complete closed loop of "perception-analysis-decision-execution." The modules work collaboratively, translating methodological innovations into a deployable and operational system, ensuring the stable and efficient automated execution of energy-saving control strategies.

[0015] As a further aspect of the present invention, the multi-temperature zone collaborative energy-saving control system for cold chain warehousing also includes a digital twin simulation module, which is used to construct a virtual model based on the physical structure of the warehouse, equipment models and historical data, and to perform simulation verification and effect evaluation of the optimization strategy before implementing actual control.

[0016] By adopting the above technical solution and introducing digital twin technology, the system's security and decision-making rigor are significantly enhanced. This module can conduct "sandbox simulations" of complex optimization strategies in virtual space, predicting control effects and potential risks in advance, thus avoiding operational risks or equipment damage that might result from directly applying unverified strategies to the physical system. Simultaneously, it also serves as a powerful platform for solution testing and personnel training, reducing trial-and-error costs and improving the overall system reliability and management level.

[0017] As a further aspect of the present invention: the data acquisition module includes a temperature sensor, a humidity sensor, a power metering unit, and a device communication gateway.

[0018] By adopting the above technical solution, the specific implementation method of data acquisition was clarified. Integrating multiple types of sensors and metering units ensured the comprehensiveness and accuracy of environmental parameter and energy consumption data acquisition. The device communication gateway solved the data access problem for heterogeneous devices, guaranteeing smooth real-time data flow. This solution provides a reliable and rich data foundation for the entire system, which is a prerequisite for the realization of all subsequent advanced analysis, optimization, and control functions.

[0019] As a further aspect of the present invention: the collaborative control module is connected to the warehouse management system and can push energy consumption anomaly warnings and suggested scheduling schemes to the management terminal.

[0020] By adopting the above technical solution, the information barrier between the energy-saving control system and the upper-level management system is broken down. It not only achieves automated control at the equipment level but also proactively pushes key operating statuses, energy consumption anomalies, and optimization suggestions to management personnel. This makes energy management more transparent and manageable, empowering managers to make data-driven scientific decisions and global scheduling, thereby further consolidating and expanding energy-saving achievements at the operational level and realizing synergistic efficiency between the control and management systems.

[0021] Compared with the prior art, the beneficial effects of the present invention are: to solve the problem of energy waste caused by independent control and neglect of thermal coupling in the prior art, and to optimize the overall energy consumption of warehousing.

[0022] Other features and advantages of the present invention will be disclosed in detail in the following detailed description and accompanying drawings. Attached Figure Description

[0023] Figure 1This is a schematic flowchart of an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In this embodiment of the invention, a method and system for coordinated energy-saving control of multiple temperature zones in cold chain warehousing are described, see [link to relevant documentation]. Figure 1 As shown, Includes the following steps: S1: Real-time collection of environmental and energy consumption data, including temperature, humidity, and equipment power, through a sensor network deployed in various temperature zones; S2: Based on historical and real-time data, analyze the thermal interference relationship between any two temperature zones and quantify the coupling interference coefficient between temperature zones; S3: Perform rolling time-domain optimization with a preset period, take minimizing the total operating energy consumption of the cold chain storage system as the objective function, and take the allowable temperature fluctuation range of each temperature zone as the constraint condition, and solve the optimal set parameter sequence of each refrigeration equipment in a future time period. S4: Send the optimal set parameter sequence to the equipment controller of the corresponding temperature zone for execution, and monitor the deviation between the execution effect and the optimization target in real time. If the deviation exceeds the allowable range, trigger regulation or warning.

[0026] By adopting the above technical solutions, an integrated energy-saving framework was constructed, encompassing global perception, coupled analysis, dynamic optimization, and closed-loop control. This method overcomes the limitations of traditional independent control, treating the entire warehouse as an organic thermal system. It can systematically tap the energy-saving potential generated by temperature zone coupling, significantly reducing the overall system energy consumption while ensuring the storage quality of each temperature zone.

[0027] As a further aspect of the present invention: in step S2, the coupling interference coefficient between the i-th temperature zone and the j-th temperature zone is calculated. The method is as follows: in, Let be the temperature change of the j-th temperature zone per unit time. The power change of the refrigeration equipment in the i-th temperature zone within the same time period is represented by the summation symbol, which indicates the cumulative calculation of multiple historical time segments.

[0028] By employing the above technical solution, a specific mathematical model for quantifying thermal interference is provided. This formula, by analyzing the correlation between equipment power changes and temperature changes in adjacent areas, can objectively and accurately calculate the intensity of the thermal impact of equipment operation on the surrounding environment within a specific temperature range. This makes the implicit thermal coupling relationship explicit and calculable, providing crucial data input for subsequent collaborative optimization and making the formulation of control strategies more scientific and targeted.

[0029] As a further aspect of the present invention: the rolling time-domain optimization in step S3 adopts a model predictive control framework, and its objective function is: in, To predict the time domain, The total number of devices. Let be the predicted power of the m-th device at time k. Its energy consumption weighting coefficient; The total number of temperature zones, Let be the predicted temperature of the i-th temperature zone at time k. Set the temperature for it. This is the temperature deviation weighting coefficient.

[0030] By adopting the above technical solution, the energy-saving problem is transformed into a constrained multi-objective optimization problem. The objective function cleverly unifies and balances the two core requirements of "minimizing total system energy consumption" and "stabilizing temperatures in each temperature zone." Through rolling optimization using a model predictive control framework, it can not only predict the future and seek the global optimum based on the current state and coupled model, but also make corrections based on feedback after each execution, thereby dynamically adapting to internal and external disturbances (such as warehouse door opening and goods entering and exiting), achieving forward-looking, adaptive, and robust energy-saving control.

[0031] As a further aspect of the present invention, step S4 further includes: if the real-time temperature of a certain temperature zone continues to deviate from the set range and the operating parameters of its associated equipment have reached the limit, then it is determined that the adjacent high-temperature zone has excessive thermal interference to it, and auxiliary decision-making suggestions of "strengthening regional isolation" or "adjusting scheduling strategy" are generated.

[0032] By adopting the above technical solutions, the system's fault diagnosis and intelligent decision support capabilities are enhanced. When conventional control methods fail to correct deviations, the system can automatically analyze the root cause of the problem and identify abnormal thermal interference caused by physical isolation failure or unreasonable logistics scheduling. By generating specific auxiliary decision-making suggestions, the system can be upgraded from simple automated control to an intelligent management tool, guiding maintenance personnel to perform equipment maintenance or process optimization, fundamentally eliminating the causes of high energy consumption, and realizing the extension from control to management.

[0033] A multi-temperature zone coordinated energy-saving control system for cold chain warehousing includes: The data acquisition module is used to acquire environmental data and equipment operation data in each temperature zone in real time through IoT sensors; The coupling analysis module is used to calculate and update the thermal coupling interference coefficient between each temperature zone based on the historical and real-time data from the data acquisition module. The optimization decision module is used to periodically solve for the optimal operating parameters of each refrigeration device based on the coupling interference coefficient and with the goal of minimizing the total energy consumption of the system, using a rolling optimization algorithm. The collaborative control module is used to send the optimal operating parameters to the field actuators, monitor the execution status, and provide early warnings and adjustments for abnormal deviations.

[0034] By adopting the above technical solution, a hardware and software platform for implementing the aforementioned method is provided. Through modular design, the system clarifies the responsibilities and data flow of each functional unit, forming a complete closed loop of "perception-analysis-decision-execution." The modules work collaboratively, translating methodological innovations into a deployable and operational system, ensuring the stable and efficient automated execution of energy-saving control strategies.

[0035] As a further aspect of the present invention, a multi-temperature zone collaborative energy-saving control system for cold chain warehousing also includes a digital twin simulation module, used to construct a virtual model based on the physical structure of the warehouse, equipment models and historical data, and to perform simulation verification and effect evaluation of the optimization strategy before implementing actual control.

[0036] By adopting the above technical solution and introducing digital twin technology, the system's security and decision-making rigor are significantly enhanced. This module can conduct "sandbox simulations" of complex optimization strategies in virtual space, predicting control effects and potential risks in advance, thus avoiding operational risks or equipment damage that might result from directly applying unverified strategies to the physical system. Simultaneously, it also serves as a powerful platform for solution testing and personnel training, reducing trial-and-error costs and improving the overall system reliability and management level.

[0037] As a further aspect of the present invention: the data acquisition module includes a temperature sensor, a humidity sensor, a power metering unit, and a device communication gateway.

[0038] By adopting the above technical solution, the specific implementation method of data acquisition was clarified. Integrating multiple types of sensors and metering units ensured the comprehensiveness and accuracy of environmental parameter and energy consumption data acquisition. The device communication gateway solved the data access problem for heterogeneous devices, guaranteeing smooth real-time data flow. This solution provides a reliable and rich data foundation for the entire system, which is a prerequisite for the realization of all subsequent advanced analysis, optimization, and control functions.

[0039] As a further aspect of the present invention: the collaborative control module is connected to the warehouse management system and can push energy consumption anomaly warnings and suggested scheduling schemes to the management terminal.

[0040] By adopting the above technical solution, the information barrier between the energy-saving control system and the upper-level management system is broken down. It not only achieves automated control at the equipment level but also proactively pushes key operating statuses, energy consumption anomalies, and optimization suggestions to management personnel. This makes energy management more transparent and manageable, empowering managers to make data-driven scientific decisions and global scheduling, thereby further consolidating and expanding energy-saving achievements at the operational level and realizing synergistic efficiency between the control and management systems.

[0041] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0042] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for coordinated energy-saving control of multiple temperature zones in cold chain warehousing, characterized in that, Includes the following steps: S1: Real-time collection of environmental and energy consumption data, including temperature, humidity, and equipment power, through a sensor network deployed in various temperature zones; S2: Based on historical and real-time data, analyze the thermal interference relationship between any two temperature zones and quantify the coupling interference coefficient between temperature zones; S3: Perform rolling time-domain optimization with a preset period, take minimizing the total operating energy consumption of the cold chain storage system as the objective function, and take the allowable temperature fluctuation range of each temperature zone as the constraint condition, and solve the optimal set parameter sequence of each refrigeration equipment in a future time period. S4: Send the optimal set parameter sequence to the equipment controller of the corresponding temperature zone for execution, and monitor the deviation between the execution effect and the optimization target in real time. If the deviation exceeds the allowable range, trigger regulation or warning.

2. The method for coordinated energy-saving control of multiple temperature zones in cold chain warehousing according to claim 1, characterized in that, In step S2, the coupling interference coefficient between the i-th temperature zone and the j-th temperature zone is calculated. The method is as follows: in, Let be the temperature change of the j-th temperature zone per unit time. The power change of the refrigeration equipment in the i-th temperature zone within the same time period is represented by the summation symbol, which indicates the cumulative calculation of multiple historical time segments.

3. The method for coordinated energy-saving control of multiple temperature zones in cold chain warehousing according to claim 1, characterized in that, The rolling time-domain optimization in step S3 adopts a model predictive control framework, and its objective function is: in, To predict the time domain, The total number of devices. Let be the predicted power of the m-th device at time k. Its energy consumption weighting coefficient; The total number of temperature zones, Let be the predicted temperature of the i-th temperature zone at time k. Set the temperature for it. This is the temperature deviation weighting coefficient.

4. The method for coordinated energy-saving control of multiple temperature zones in cold chain warehousing according to claim 1, characterized in that, Step S4 further includes: if the real-time temperature of a certain temperature zone continues to deviate from the set range and the operating parameters of its associated equipment have reached the limit, it is determined that the adjacent high temperature zone has excessive thermal interference to it, and auxiliary decision-making suggestions of "strengthening regional isolation" or "adjusting scheduling strategy" are generated.

5. A multi-temperature zone collaborative energy-saving control system for cold chain warehousing, used to execute the method according to any one of claims 1-4, characterized in that, include: The data acquisition module is used to acquire environmental data and equipment operation data in each temperature zone in real time through IoT sensors; The coupling analysis module is used to calculate and update the thermal coupling interference coefficient between each temperature zone based on the historical and real-time data of the data acquisition module. The optimization decision module is used to periodically solve for the optimal operating parameters of each refrigeration device based on the coupling interference coefficient and with the goal of minimizing the total energy consumption of the system, using a rolling optimization algorithm. The collaborative control module is used to send the optimal operating parameters to the field actuators, monitor the execution status, and provide early warnings and adjustments for abnormal deviations.

6. The multi-temperature zone collaborative energy-saving control system for cold chain warehousing according to claim 5, characterized in that, Also includes: The digital twin simulation module is used to build a virtual model based on the physical structure of the warehouse, equipment models, and historical data, and to simulate and verify the optimization strategy and evaluate its effects before implementing actual control.

7. A multi-temperature zone collaborative energy-saving control system for cold chain warehousing according to claim 5, characterized in that, The data acquisition module includes a temperature sensor, a humidity sensor, a power metering unit, and a device communication gateway.

8. A multi-temperature zone collaborative energy-saving control system for cold chain warehousing according to claim 5, characterized in that, The collaborative control module is connected to the warehouse management system and can push energy consumption anomaly warnings and suggested scheduling schemes to the management terminal.

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