Digital-twin-based quick-freezing equipment energy efficiency optimization system

Abstract

The application discloses a quick-freezing equipment energy efficiency optimization system based on digital twinning, and particularly relates to the technical field of quick-freezing equipment energy saving, and comprises an energy efficiency optimization physical equipment layer, an energy efficiency optimization multi-source data acquisition layer, an energy efficiency optimization multi-physical field digital twinning layer, a quick-freezing equipment whole-process energy efficiency optimization layer and an energy efficiency optimization execution and feedback layer; the energy efficiency optimization physical equipment layer is used for constructing a quick-freezing equipment digital twinning body, comprising a pre-treatment stage equipment group, a quick-freezing stage equipment group and a refrigeration equipment group, and each equipment is provided with a monitoring point for installing a sensor to collect energy efficiency optimization multi-source data; the energy efficiency optimization multi-physical field digital twinning layer is used for constructing a geometric model and a physical mechanism model, and based on the logistics mechanism model, key factors influencing energy efficiency can be more accurately found out, thereby providing a strong basis for formulating scientific and reasonable energy efficiency optimization strategies, so that the energy efficiency of the quick-freezing equipment can be deeply optimized.

Description

Technical Field

[0001] This invention relates to the field of energy-saving technology for quick-freezing equipment, specifically to an energy efficiency optimization system for quick-freezing equipment based on digital twins. Background Technology

[0002] Quick-freezing equipment is a key piece of equipment in the food processing industry. Its energy consumption throughout the entire process (pretreatment-quick-freezing-packaging and refrigeration) accounts for about half of the total energy consumption of food processing enterprises. Among them, the heat and humidity management of pretreatment, the matching of cold energy in quick-freezing, and the distribution of cold energy in refrigeration are the key links in energy consumption control.

[0003] Currently, existing energy efficiency optimization technologies for quick-freezing equipment mostly focus on optimizing the refrigeration equipment in the quick-freezing process, neglecting the energy waste caused by uneven initial conditions of food in the pretreatment stage (such as the waste heat recovery rate of blanching wastewater and humidity in the draining section), as well as the secondary cooling energy consumption caused by cold leakage in the packaging and refrigeration stage. This makes it impossible to achieve coordinated optimization across the entire process. Traditional optimization only uses "quick-freezing energy consumption" or "quick-freezing time" as a single evaluation indicator, without establishing a phased and multi-dimensional optimization evaluation system. This makes it difficult to accurately quantify the causes of energy waste in each stage, leading to a blind optimization direction. Existing quick-freezing equipment technologies involving digital twins only build geometric models of the equipment or physical models of a single stage, without integrating the physical mechanisms of multiple devices throughout the entire process and real-time data from each stage. This results in low mapping accuracy between the virtual model and the physical equipment, failing to provide reliable decision support for overall process optimization. Existing control methods rely on manual experience or simple PID control, which cannot adjust parameters in real time according to dynamic changes in each stage of the entire process (such as fluctuations in the moisture content of pretreated food and temperature fluctuations in the storage room), leading to energy redundancy and food quality risks.

[0004] Although digital twin technology has been applied in the field of intelligent manufacturing, a systematic solution that covers multiple stages, integrates stage-by-stage evaluation indicators, and has collaborative optimization capabilities has not yet been formed for the whole process energy efficiency optimization of quick-freezing equipment. Therefore, there is an urgent need for a digital twin-based whole process energy efficiency optimization system for quick-freezing equipment to solve the problems of lack of whole process collaborative optimization, inaccurate evaluation, and lagging control in existing technologies. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a digital twin-based energy efficiency optimization system for quick-freezing equipment to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an energy efficiency optimization system for quick-freezing equipment based on digital twins, comprising:

[0007] Energy efficiency optimization physical equipment layer: quick-freezing equipment used to build digital twins, including pre-treatment stage equipment group, quick-freezing stage equipment group and refrigeration equipment group. Each piece of equipment is equipped with monitoring points for installing sensors to collect multi-source data on energy efficiency optimization.

[0008] Energy efficiency optimization multi-source data acquisition layer: includes a phased sensor module and a data preprocessing module. The phased sensor module is used to collect energy efficiency optimization data in the preprocessing stage, quick-freezing stage and cold storage stage. The data preprocessing module is used to preprocess the collected energy efficiency optimization data and transmit the preprocessed energy efficiency optimization data of each stage to the energy efficiency optimization multi-physics digital twin layer.

[0009] Energy efficiency optimization multi-physics digital twin layer: Receives equipment groups at each stage of the energy efficiency optimization physical equipment layer, constructs three-dimensional geometric models of each stage, receives preprocessed energy efficiency optimization data at each stage from the multi-source data acquisition layer, constructs physical mechanism models of each stage, and transmits them to the full-process energy efficiency optimization layer of the quick-freezing equipment.

[0010] The energy efficiency optimization layer for the entire process of quick-freezing equipment compares the parameters obtained from the physical mechanism model at each stage with the corresponding thresholds. Based on the abnormal comparison results, the corresponding energy efficiency optimization mechanism is triggered and transmitted to the energy efficiency optimization execution and feedback layer.

[0011] Energy efficiency optimization execution and feedback layer: Executes the corresponding energy efficiency optimization mechanism transmitted by the energy efficiency optimization layer of the entire process of quick-freezing equipment, and feeds back the energy efficiency optimization indicators obtained from the evaluation of each stage after the execution of the energy efficiency optimization mechanism to the management terminal for human-computer interaction.

[0012] The technical effects and advantages of this invention are as follows:

[0013] 1. This invention optimizes the physical equipment layer for energy efficiency by setting monitoring points and installing sensors for the pretreatment stage equipment group, quick-freezing stage equipment group and refrigeration equipment group respectively. It can collect energy efficiency-related data of each link of the quick-freezing equipment in all aspects and fully reflect the energy efficiency status of the quick-freezing equipment in the whole operation process.

[0014] 2. This invention optimizes energy efficiency through a multi-physics digital twin layer. It receives equipment group information from each stage of the physical equipment layer to construct a geometric model. At the same time, it combines the pre-processed data from the multi-source data acquisition layer to construct a physical mechanism model. This enables more accurate identification of key factors affecting energy efficiency and provides a strong basis for formulating scientific and reasonable energy efficiency optimization strategies, thereby achieving in-depth optimization of the energy efficiency of quick-freezing equipment.

[0015] 3. This invention uses a full-process energy efficiency optimization layer for quick-freezing equipment to compare the parameter indicators obtained from the physical mechanism model at each stage with the corresponding thresholds. This allows for timely detection of energy efficiency problems that occur in different operating stages of the equipment. It not only focuses on the performance of individual equipment but also considers the impact of the coordinated operation of various equipment in the entire quick-freezing process on energy efficiency, thus achieving full-process energy efficiency optimization and evaluation from local to overall. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0017] Figure 2 This is a schematic diagram of the energy efficiency optimization multiphysics digital twin layer of the present invention. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1 As shown, the present invention provides an energy efficiency optimization system for quick-freezing equipment based on digital twins, including an energy efficiency optimization physical equipment layer, an energy efficiency optimization multi-source data acquisition layer, an energy efficiency optimization multi-physical field digital twin layer, a quick-freezing equipment full-process energy efficiency optimization layer, and an energy efficiency optimization execution and feedback layer.

[0020] This embodiment specifically illustrates that the present invention studies an energy efficiency optimization system for quick-freezing equipment, including a pretreatment stage equipment group, a quick-freezing stage equipment group, and a refrigeration stage equipment group. Pretreatment is the "input" of quick-freezing, and refrigeration is the "continuation" of quick-freezing. If the temperature of the pretreated product is higher (e.g., insufficient cooling after blanching), the quick-freezing machine needs to consume a huge amount of extra energy to lower it from a higher temperature (e.g., +25°C) to below the freezing point, which is much more energy-intensive than starting to freeze from a low temperature (e.g., +4°C). The quick-freezing machine consumes a lot of energy to freeze the product to -18°C. If the packaging is poor or the transfer process is inefficient, the product temperature may rise back to -12°C before it is put into storage. In this case, the "cold energy" consumed by this rise is a pure waste, and the cold storage needs to consume additional electricity to cool it down again.

[0021] The energy efficiency optimization physical device layer is connected to the energy efficiency optimization multi-source data acquisition layer. The energy efficiency optimization multi-physics field digital twin layer is connected to the quick-freezing equipment full-process energy efficiency optimization layer and the energy efficiency optimization multi-source data acquisition layer, respectively. The energy efficiency optimization execution and feedback layer is connected to the quick-freezing equipment full-process energy efficiency optimization layer.

[0022] Energy efficiency optimization physical equipment layer: quick-freezing equipment used to build digital twins, including pre-treatment stage equipment group, quick-freezing stage equipment group and refrigeration equipment group. Each piece of equipment is equipped with monitoring points for installing sensors to collect multi-source data on energy efficiency optimization.

[0023] This embodiment requires specific explanation of the following: the pretreatment stage equipment group includes a scalding machine (with waste heat recovery device), a draining device (including humidity sensor), and a pretreatment conveyor line; the quick-freezing stage equipment group includes a variable frequency screw compressor, a condenser (with spray cooling system), an evaporator (with intelligent defrosting module), a multi-zone variable frequency fan, a quick-freezing tunnel (including zone temperature and wind speed sensors), and a fan unit (stepless speed regulation); the refrigeration equipment group includes a cold storage (with load and temperature sensors), and a variable frequency refrigeration unit.

[0024] Energy efficiency optimization multi-source data acquisition layer: includes a phased sensor module and a data preprocessing module. The phased sensor module is used to collect energy efficiency optimization data in the preprocessing stage, quick-freezing stage and cold storage stage. The data preprocessing module is used to preprocess the collected energy efficiency optimization data and transmit the preprocessed energy efficiency optimization data of each stage to the energy efficiency optimization multi-physics digital twin layer.

[0025] This embodiment requires specific explanation of the following: the pretreatment stage includes, but is not limited to, temperature sensors, humidity sensors, flow sensors, and power sensors; the refrigeration stage includes, but is not limited to, temperature sensors, wind speed sensors, and power sensors; and the refrigeration stage includes, but is not limited to, temperature sensors, load sensors, and power sensors.

[0026] This embodiment requires specific explanation of the energy efficiency optimization data for the pretreatment stage, including the temperature of the scalding wastewater, the air temperature in the draining section, the air humidity and wind speed in the draining section, the flow rate of the scalding wastewater, and the flow rate of the waste heat recovery water; the energy efficiency optimization data for the quick-freezing stage includes the temperature of each zone in the freezing area, the mass flow rate of the refrigerant, the power of the fan unit, the temperature and pressure on the refrigerant inlet and outlet pipes of the evaporator, and the frost thickness on the evaporator surface; the energy efficiency optimization data for the refrigeration stage includes the temperature of the monitoring points in the cold storage, the load weight in the cold storage and the corresponding constant pressure specific heat capacity, and the power of the refrigeration compressor.

[0027] In this embodiment, it is necessary to specifically explain that the data preprocessing module is used to preprocess the collected energy efficiency optimization data, including using wavelet transform algorithm to reduce noise in the original data, removing outliers (such as sensor instantaneous fault data) based on the 3σ criterion, and normalizing the phased data through the Min-Max function, and storing it in a time sequence of "preprocessing-quick freezing-refrigeration" to provide high-quality data input for the digital twin layer.

[0028] Energy Efficiency Optimization Multiphysics Digital Twin Layer: This layer receives equipment groups from each stage of the energy efficiency optimization physical equipment layer, constructs three-dimensional geometric models for each stage, receives preprocessed energy efficiency optimization data from each stage obtained from the multi-source data acquisition layer, constructs physical mechanism models for each stage, and transmits these models to the full-process energy efficiency optimization layer of the quick-freezing equipment. This includes the following steps:

[0029] Please see Figure 2 As shown, this implementation requires specific explanation of the energy efficiency optimization multiphysics digital twin layer, which includes geometric model construction units for each stage and physical mechanism model construction units for each stage. The geometric model construction units for each stage are used to construct the model based on the equipment groups for each stage, and the physical mechanism model construction units for each stage are used to construct the model based on the preprocessed energy efficiency optimization data, including the physical mechanism model for the preprocessing stage, the physical mechanism model for the quick-freezing stage, and the physical mechanism model for the refrigeration stage.

[0030] Step 1: Using CAD drawings and laser scanning technology, construct 1:1 three-dimensional geometric models of the pretreatment stage equipment group, quick-freezing stage equipment group, and refrigeration equipment group based on the equipment groups of each stage of the energy efficiency optimization physical equipment layer. This includes the geometric structure and spatial relationship (e.g., distance relationship, angle relationship) of equipment components (e.g., blanching machine, quick-freezing tunnel, etc.), including:

[0031] The calculated geometric accuracy fit (GAC) is obtained by calculating the average distance deviation between the point cloud data (from laser scanning) and the surface of the CAD model. J represents the total number of effective laser scanning point clouds used for comparison, and d(q) I M) represents the i-th scan point q I The shortest distance L to surface M of the CAD model dia The diagonal length of the entire device bounding box is given; the average geometric accuracy fit μ (GAC) is obtained by traversing all devices in the three stages.

[0032] The calculation of component completeness rate involves comparing the number of components already built in the digital twin layer with the required number N of components in the equipment list. model and N list The component completeness rate (CCR) is obtained, where CCR = N. model / N list ;

[0033] The accuracy of spatial relationships is calculated by comparing the relative errors between the spatial relationship parameters (such as distance and angle relationships) between the calculated devices and the design values, resulting in the spatial relationship accuracy SRA. r k and r k,0 These are the measured value and the design baseline value of the k-th spatial relationship in the model, respectively, where K is the total number of spatial relationships;

[0034] The evaluation metric MFI for constructing a three-dimensional geometric model. 3D MFI 3D=a1×μ(GAC)+a2×CCR+a3×SRA, where a1, a2, and a3 are the corresponding thresholds. The weights a1 of the average geometrical accuracy fit μ(GAC) and a3 of the spatial relationship accuracy SRA are greater than the weights a2 of the component completeness rate CCR. For example, a1=0.4, a2=0.2, and a3=0.4; if MFI 3D <Corresponding threshold MFI 3D th If the result is negative, it indicates that the 3D geometric model is abnormally constructed. Repeatedly verify the geometric accuracy, component completeness, spatial relationship accuracy, and 3D geometric model construction evaluation indicators. Conversely, if the result is positive, it indicates that the 3D geometric model is well constructed, and the 3D geometric models at each stage are obtained.

[0035] Step 2: Construct physical mechanism models for each stage, including:

[0036] Step 2.1: Based on the energy efficiency optimization data of the pretreatment stage, construct the power model Q for waste heat recovery of the blanching wastewater in the pretreatment stage. hot (Unit: kW) and the air humidity gradient model G for the draining section RH Q hot =c 水 ×ρ 水 ×Q 废水 ×(T 废水入 -T 废水出 ), c 水 and ρ 水 These are the specific heat capacity of water (in kJ / (kg×℃)) and the density of water (in kg / m³). 3 ), T 废水入 and T 废水出 These are the temperatures (in °C) of the wastewater inlet and outlet waste heat recovery devices, respectively. 废水 The flow rate of scalding and bleaching wastewater (usually measured in m³). 3 / s (cubic meters per second), etc., which represents the volume of wastewater passing through the waste heat recovery device per unit time; G RH =ΔRH / Δh, where ΔRH is the change in humidity in the vertical direction of the draining section, and Δh is the change in height in the vertical direction of the draining section. ΔRH = RH in -RH out -k×v, RH in and RH out The relative humidity of the air at the inlet and outlet of the drain are respectively. The relative humidity of the air is the ratio of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature. v is the wind speed in the drain section, in m / s. k is the water evaporation rate influencing factor, in s / m, which reflects the influence of wind speed on the water evaporation rate and can be obtained by fitting historical data.

[0037] This embodiment requires specific explanation regarding the calculation of humidity change per unit height in the vertical direction (i.e., humidity gradient G). RH This allows us to understand the vertical distribution of air humidity in the draining section, thus providing a basis for optimizing the draining process and reducing the energy consumption of subsequent quick-freezing machines.

[0038] Step 2.2: Based on the preprocessed energy efficiency optimization data of the quick-freezing stage, construct the temperature uniformity model U of the freezing zone during the quick-freezing stage. 温度 The coupling coefficient model η between the fan and the heat exchange efficiency 换热 , , σ(T 冻区 ) and μ(T 冻区 The values ​​are the standard deviation and average temperature of the frozen zone, respectively. The frozen zone is divided into i = 1, 2, ..., n partitions. The temperature of each partition is obtained through a temperature sensor, and the standard deviation and average temperature of the frozen zone are calculated. Q is the heat exchange (unit: kW, 1kW = 1kJ / s), Q = m r ×(h out -h in ), m r The mass flow rate of the refrigerant (unit: kg / s) can be obtained using a mass flow meter, h out and h in These are the specific enthalpy (in kJ / kg) of the refrigerant at the evaporator inlet and outlet, respectively. fan λ is the actual power of the fan (in kW), hh is the frost thickness on the evaporator surface, which can be measured by optical equipment such as laser displacement sensor, and λ is the influence coefficient of frost thickness on heat transfer efficiency, which is obtained by fitting experimental data of evaporator frost thickness and heat transfer coefficient, with a value range of 0.02-0.051 / mm.

[0039] This embodiment requires specific explanation of how the inlet temperature T is obtained through temperature and pressure sensors installed on the refrigerant inlet and outlet pipes of the evaporator. in and pressure P in Outlet temperature T out and pressure P out Then, it calls the preset refrigerant property database and automatically retrieves the specific enthalpy value h for that state point. out and h in The refrigerant property database is an existing technology and the cornerstone for engineering design, system simulation, fault diagnosis, and optimization control in fields such as refrigeration, heating, ventilation and air conditioning (HVAC), chemical industry, and energy. It contains thermodynamic and transport property parameters of various refrigerants.

[0040] Step 2.3: Based on the pre-processed energy efficiency optimization data of the refrigeration stage, construct the total heat capacity model C_load (in kJ / ℃) and the refrigeration energy efficiency coefficient model η for the refrigeration stage. dis , m j Let c be the weight of the i-th type of load (in kg, obtained from a load cell). p,i is the isobaric specific heat capacity of the i-th load freezing state (unit: kJ / (kg×℃)). T peak and T tar P represents the highest temperature after the cold storage disturbance and the target temperature set for the cold storage, respectively. co For compressor power, t re For from T peak Restore to T tar The time spent;

[0041] This embodiment specifically explains that the mass and type of each load are automatically obtained through inbound identification technology (scanning, manual input, etc.), and then the specific heat capacity at constant pressure corresponding to each load is automatically obtained through the central physical property database.

[0042] The energy efficiency optimization layer for the entire process of quick-freezing equipment compares the parameters obtained from the physical mechanism model at each stage with the corresponding thresholds. Based on any anomalies in the comparison results, it triggers the corresponding energy efficiency optimization mechanism and transmits the data to the energy efficiency optimization execution and feedback layer. This includes the following steps:

[0043] Step 1: Power model Q for waste heat recovery from scalding and rinsing wastewater in the pretreatment stage hot Calculate the waste heat recovery rate η of scalding and bleaching wastewater. hot η hot =Q hot / [c 水 ×ρ 水 ×Q 废水 ×(T 废水入 -T 环境 )],c 水 and ρ 水 T represents the specific heat capacity and density of water, respectively. 废水入 T represents the temperature of the wastewater inlet heat recovery device. 环境 Q represents the ambient temperature. 废水 Let η be the flow rate of the scalding and rinsing wastewater. hot < the corresponding threshold η hot th (For example, 0.65), triggering an energy efficiency optimization mechanism for abnormal waste heat recovery in rinsing wastewater, including increasing the valve opening of the plate heat exchanger, etc.; then determining the air humidity gradient G in the draining section. RH Does it fall within the threshold range (G)? RH minG RH max (e.g., G) RH min =-0.7,G RH max =-0.3, obtained by analyzing historical load quality data for each type of load, when G RH Below G RH min When the energy consumption during the rapid freezing stage exceeds the corresponding set value, when G... RH Higher than G RH min When the product defect rate rises above the corresponding set value, it reaches the threshold range (G). RH min G RH max )), G RH min and G RH max G RH The lower and upper limits of G, if G RH <G RH min This triggers an energy efficiency optimization mechanism that optimizes air humidity levels in the drying section, including reducing fan frequency and damper opening; if G RH >G RH max This triggers an energy efficiency optimization mechanism for an abnormally high air humidity gradient in the asphalt drying section, including increasing fan frequency and increasing damper opening; if G RH ∈(G RH min G RH max If the humidity gradient in the draining section is good, it indicates that the air humidity gradient is good.

[0044] This embodiment specifically explains how waste heat recovery and humidity gradient control reduce frosting energy consumption and improve pretreatment energy efficiency; G RH A low reading indicates excessive moisture evaporation, leading to an excessive dehumidification load in the subsequent quick-freezing section, increasing energy consumption, and accelerating frost formation. RH A high relative humidity (RH) indicates poor draining efficiency and excessive residual moisture on the product surface, which can lead to ice formation and sticking during subsequent quick-freezing, affecting appearance and quality. The relative humidity (RH) after the air flows through the draining section is also a factor. out It will be significantly higher than the relative humidity (RH) at the inlet. in .

[0045] Step 2: Based on the temperature uniformity model U of the freezing zone during the quick-freezing stage 温度 The coupling coefficient model η between the fan and the heat exchange efficiency 换热 If the temperature uniformity of the frozen zone U 温度 <corresponding threshold U温度 th (e.g., 0.9), triggering an energy efficiency optimization mechanism for abnormal freezing zone temperatures, identifying the temperature T of each freezing zone. i If T i <corresponding lower temperature limit T min (For example, -35℃), then the optimization mechanism for abnormally low temperatures is triggered, including reducing the corresponding fan frequency and compressor frequency, etc. If T i > The corresponding upper temperature limit T mmax (For example, -30℃), then the optimization mechanism for abnormally high temperatures is triggered, including increasing the fan frequency and compressor frequency, etc.; if the fan-heat exchange efficiency coupling coefficient η 换热 A value ≥0.9 indicates good fan-heat exchange efficiency; if 0.7≤η 换热 A value <0.9 indicates a decrease in fan-heat exchange efficiency, usually caused by thickening frost, triggering the defrosting process and energy efficiency optimization mechanism; if η 换热 <0.7, triggering the fan-heat exchange efficiency abnormally low energy efficiency optimization mechanism, triggering the immediate defrosting program energy efficiency optimization mechanism;

[0046] Step 3: Based on the total heat capacity model C_load and refrigeration energy efficiency coefficient η of the refrigeration load during the refrigeration stage. dis If the total heat capacity of the refrigerated load, C_load > C_ref, where C_ref is the reference value of the total heat capacity of the cold storage under rated full load, the energy efficiency optimization mechanism for full load is triggered, such as the system entering a high-efficiency operation mode; if 0.4 × C_ref ≤ C_load ≤ C_ref, the energy efficiency optimization mechanism for medium load is triggered, such as the system entering normal operation mode, maintaining conventional control strategies, and closely monitoring disturbances caused by inbound operations; if C_load < 0.4 × C_ref, the energy efficiency optimization mechanism for near-empty load is triggered, such as issuing a deep freezing command to the quick-freezing equipment (meaning the freezing temperature is less than the set deep freezing temperature, such as -21℃), reducing the amount of goods added at one time, and extending the inbound interval, etc.; if the refrigeration energy efficiency coefficient η dis >η_ref, where η_ref is the baseline energy efficiency coefficient of the refrigeration equipment under theoretical conditions (e.g., clean equipment, no frost, etc.). This indicates that the refrigeration energy efficiency is good, and the current operating strategy can be maintained. If 0.7×η_ref≤η dis If η_ref is less than or equal to η_ref, it indicates a decrease in refrigeration energy efficiency, triggering energy efficiency optimization mechanisms. For example, a digital twin system might begin tracking efficiency degradation trends and checking for recent abnormal disturbances. If η_ref is less than or equal to η_ref, then... dis A value less than 0.7 × η_ref indicates abnormally low refrigeration energy efficiency, triggering energy efficiency optimization mechanisms such as mandatory defrosting, reducing abnormally large disturbances (e.g., prolonged opening of the refrigerator door), and checking for compressor malfunctions.

[0047] Energy Efficiency Optimization Execution and Feedback Layer: This layer executes the corresponding energy efficiency optimization mechanisms transmitted from the full-process energy efficiency optimization layer of the quick-freezing equipment. It also feeds back the energy efficiency optimization indicators obtained from each stage of the energy efficiency optimization mechanism to the management terminal for human-computer interaction. If the energy efficiency optimization indicators obtained from each stage of the execution are within the corresponding set range, it indicates that the energy efficiency optimization of the quick-freezing equipment is good. Otherwise, it prompts the management terminal to take timely measures, such as optimizing the digital twin model, checking for equipment malfunctions, and performing timely equipment maintenance.

[0048] This embodiment requires specific explanation of the energy efficiency optimization mechanism. For example, a drainer uses the vibration of a vibrating motor to drain the moisture from the surface of food. If the optimization layer determines that the wind speed needs to be adjusted based on indicators such as temperature uniformity and fan-heat exchange efficiency coupling coefficient, it will control the frequency converter of the fan unit. When the optimization layer determines that the refrigeration capacity needs to be adjusted to adapt to changes in the refrigeration load based on indicators such as the thermal inertia coefficient of the refrigeration load and the comprehensive energy efficiency ratio of the refrigeration equipment, it will send a command to the frequency converter of the refrigeration unit.

[0049] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0050] In conclusion, the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the invention.

[0051] Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this invention shall be included within the scope of protection of this invention.

Claims

1. A digital twin-based energy efficiency optimization system for quick-freezing equipment, characterized in that: include: Energy efficiency optimization physical equipment layer: quick-freezing equipment used to build digital twins, including pre-treatment stage equipment group, quick-freezing stage equipment group and refrigeration equipment group. Each piece of equipment is equipped with monitoring points for installing sensors to collect multi-source data on energy efficiency optimization. Energy efficiency optimization multi-source data acquisition layer: includes a phased sensor module and a data preprocessing module. The phased sensor module is used to collect energy efficiency optimization data in the preprocessing stage, quick-freezing stage and cold storage stage. The data preprocessing module is used to preprocess the collected energy efficiency optimization data and transmit the preprocessed energy efficiency optimization data of each stage to the energy efficiency optimization multi-physics digital twin layer. Energy efficiency optimization multi-physics digital twin layer: Receives equipment groups at each stage of the energy efficiency optimization physical equipment layer, constructs three-dimensional geometric models of each stage, receives preprocessed energy efficiency optimization data at each stage from the multi-source data acquisition layer, constructs physical mechanism models of each stage, and transmits them to the full-process energy efficiency optimization layer of the quick-freezing equipment. The energy efficiency optimization multiphysics digital twin layer constructs three-dimensional geometric models for each stage, including: Geometric accuracy fit (GAC) is calculated by determining the average distance deviation between the point cloud data and the surface of the CAD model. J represents the total number of effective laser scanning point clouds used for comparison, and d(q) I M) represents the i-th scan point q I The shortest distance L to surface M of the CAD model dia The diagonal length of the entire device bounding box is given; the average geometric accuracy fit μ (GAC) is obtained by traversing all devices in the three stages. Calculate the component completeness rate: Compare the number of components N required in the equipment list with the components already built in the model in the digital twin layer. model and N list The component completeness rate (CCR) is obtained, where CCR = N. model / N list ; Spatial Relationship Accuracy: The relative error between the spatial relationship parameters between equipment and the design values ​​is calculated to obtain the spatial relationship accuracy (SRA). r k and r k,0 These are the measured value and the design baseline value of the k-th spatial relationship in the model, respectively, where K is the total number of spatial relationships; Calculate the Model Fit (MFI) metric for 3D geometric model construction. 3D MFI 3D =a1×μ(GAC)+a2×CCR+a3×SRA, where a1, a2, and a3 are the corresponding thresholds. The weights a1 of the average geometrical accuracy fit μ(GAC) and a3 of the spatial relationship accuracy SRA are greater than the weight a2 of the component completeness rate CCR; if MFI 3D <Corresponding threshold MFI 3D th If the result is negative, it indicates that the 3D geometric model is abnormally constructed. Repeatedly verify the geometric accuracy, component completeness, spatial relationship accuracy, and 3D geometric model construction evaluation indicators. Conversely, if the result is positive, it indicates that the 3D geometric model is well constructed, and the 3D geometric models at each stage are obtained. The energy efficiency optimization layer for the entire process of quick-freezing equipment compares the parameters obtained from the physical mechanism model at each stage with the corresponding thresholds. Based on the abnormal comparison results, the corresponding energy efficiency optimization mechanism is triggered and transmitted to the energy efficiency optimization execution and feedback layer. Energy efficiency optimization execution and feedback layer: Executes the corresponding energy efficiency optimization mechanism transmitted by the energy efficiency optimization layer of the entire process of quick-freezing equipment, and feeds back the energy efficiency optimization indicators obtained from the evaluation of each stage after the execution of the energy efficiency optimization mechanism to the management terminal for human-computer interaction.

2. The energy efficiency optimization system for quick-freezing equipment based on digital twins according to claim 1, characterized in that: The energy efficiency optimization multiphysics digital twin layer constructs physical mechanism models for each stage, including: constructing a power model Q for waste heat recovery of scalding and rinsing wastewater in the pretreatment stage based on the energy efficiency optimization data of the pretreatment stage. hot and the air humidity gradient model G of the draining section RH Q hot =c 水 ×ρ 水 ×Q 废水 ×(T 废水入 -T 废水出 ), c 水 and ρ 水 T represents the specific heat capacity and density of water, respectively. 废水入 and T 废水出 These are the temperatures of the wastewater inlet and outlet waste heat recovery devices, Q and Q, respectively. 废水 G is the flow rate of the scalding and rinsing wastewater. RH =ΔRH / Δh, where ΔRH is the change in humidity in the vertical direction of the draining section, and Δh is the change in height in the vertical direction of the draining section. ΔRH = RH in -RH out -k×v, RH in and RH out The relative humidity of the air at the inlet and outlet of the drain are respectively. The relative humidity of the air is the ratio of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature. v is the wind speed in the drain section, in m / s, and k is the water evaporation rate influencing factor, in s / m.

3. The energy efficiency optimization system for quick-freezing equipment based on digital twins according to claim 1, characterized in that: The energy efficiency optimization multiphysics digital twin layer constructs physical mechanism models for each stage, including: constructing a temperature uniformity model U in the frozen zone during the quick-freezing stage based on preprocessed energy efficiency optimization data from the quick-freezing stage. 温度 The coupling coefficient model η between the fan and the heat exchange efficiency 换热 , , σ(T 冻区 ) and μ(T 冻区 The values ​​are the standard deviation and average temperature of the frozen zone, respectively. The frozen zone is divided into i = 1, 2, ..., n partitions. The temperature of each partition is obtained through a temperature sensor, and the standard deviation and average temperature of the frozen zone are calculated. Q represents the heat exchange, Q = m r ×(h out -h in ), m r h is the mass flow rate of the refrigerant. out and h in P represents the specific enthalpy of the refrigerant at the evaporator inlet and outlet, respectively. fan λ represents the actual power of the fan, hh represents the frost thickness on the evaporator surface, and λ represents the influence coefficient of frost thickness on heat exchange efficiency.

4. The energy efficiency optimization system for quick-freezing equipment based on digital twins according to claim 1, characterized in that: The energy efficiency optimization multiphysics digital twin layer constructs physical mechanism models for each stage, including: based on preprocessed energy efficiency optimization data for the refrigeration stage, constructing a total heat capacity model C_load and a refrigeration energy efficiency coefficient model η for the refrigeration stage. dis , m j Let c be the weight of the i-th type of load. p,i Let be the isobaric specific heat capacity for the i-th load freezing state; T peak and T tar P represents the highest temperature after the cold storage disturbance and the target temperature set for the cold storage, respectively. co For compressor power, t re For from T peak Restore to T tar The time spent.

5. The energy efficiency optimization system for quick-freezing equipment based on digital twins according to claim 1, characterized in that: The energy efficiency optimization layer for the entire process of the quick-freezing equipment includes: a power model Q based on the waste heat recovery of the blanching wastewater in the pretreatment stage. hot Calculate the waste heat recovery rate η of scalding and bleaching wastewater. hot η hot =Q hot / [c 水 ×ρ 水 ×Q 废水 ×(T 废水入 -T 环境 )],c 水 and ρ 水 T represents the specific heat capacity and density of water, respectively. 废水入 T represents the temperature of the wastewater inlet heat recovery device. 环境 Q represents the ambient temperature. 废水 Let η be the flow rate of the scalding and rinsing wastewater. hot < the corresponding threshold η hot th This triggers an energy efficiency optimization mechanism that optimizes the recovery of waste heat from the scalding and rinsing wastewater; then, it determines the air humidity gradient G in the draining section. RH Does it fall within the threshold range (G)? RH min G RH max ), G RH min and G RH max G RH The lower and upper limits of G, if G RH <G RH min This triggers an energy efficiency optimization mechanism that optimizes air humidity levels in the drying section when they are abnormally low; if G RH >G RH max This triggers an energy efficiency optimization mechanism that optimizes air humidity levels in the drying section when they are abnormally high; if G RH ∈(G RH min G RH max If the humidity gradient of the draining section is good, it indicates that the humidity gradient is good.

6. The energy efficiency optimization system for quick-freezing equipment based on digital twins according to claim 1, characterized in that: The full-process energy efficiency optimization layer for the quick-freezing equipment also includes: a temperature uniformity model U of the freezing zone during the quick-freezing stage. 温度 The coupling coefficient model η between the fan and the heat exchange efficiency 换热 If the temperature uniformity of the frozen zone U 温度 <corresponding threshold U 温度 th An energy efficiency optimization mechanism that triggers abnormal temperatures in frozen zones identifies the temperature T in each frozen zone. i If T i <corresponding lower temperature limit T min If T is abnormally low, the optimization mechanism will be triggered. i > The corresponding upper temperature limit T mmax If the temperature is abnormally high, the optimization mechanism will be triggered; if the fan-heat exchange efficiency coupling coefficient η 换热 A value ≥0.9 indicates good fan-heat exchange efficiency; if 0.7≤η 换热 <0.9 indicates a decrease in fan-heat exchange efficiency, triggering the defrosting process and energy efficiency optimization mechanism; if η 换热 <0.7, triggering the fan-heat exchange efficiency abnormally low energy efficiency optimization mechanism, triggering the immediate defrosting program energy efficiency optimization mechanism.

7. The energy efficiency optimization system for quick-freezing equipment based on digital twins according to claim 4, characterized in that: The full-process energy efficiency optimization layer for the quick-freezing equipment also includes: a total heat capacity model C_load based on the refrigeration load during the refrigeration stage and the refrigeration energy efficiency coefficient η. dis If the total heat capacity of the refrigerated load, C_load, is greater than C_ref, where C_ref is the reference value of the total heat capacity of the cold storage under rated full load, the energy efficiency optimization mechanism for full load is triggered; if 0.4 × C_ref ≤ C_load ≤ C_ref, the energy efficiency optimization mechanism for medium load is triggered; if C_load < 0.4 × C_ref, the energy efficiency optimization mechanism for near-empty load is triggered; if the refrigeration energy efficiency coefficient η dis >η_ref, where η_ref is the baseline energy efficiency coefficient of the refrigeration equipment under theoretical conditions. This indicates that the refrigeration energy efficiency is good, and the current operating strategy can be maintained. If 0.7×η_ref≤η dis If η_ref is less than or equal to η_ref, it indicates a decrease in refrigeration energy efficiency, triggering the energy efficiency optimization mechanism for reduced refrigeration energy efficiency. dis <0.7×η_ref indicates abnormally low refrigeration energy efficiency, triggering the energy efficiency optimization mechanism for abnormally low refrigeration energy efficiency.

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