CN-121993976-A - Intelligent defrosting control method and system for refrigerator

CN121993976ACN 121993976 ACN121993976 ACN 121993976ACN-121993976-A

Abstract

The invention discloses an intelligent defrosting control method and system for a refrigerator, and relates to the technical field of operation control and intelligent energy-saving management of refrigeration equipment; the intelligent defrosting device comprises a goods temperature sensor array module, an evaporator state sensor, a business information acquisition module, a controller, a remote cloud server and a thermal energy storage module, wherein the control method is based on multi-source operation state data, and achieves intelligent decision of defrosting time and mode by constructing a multi-objective optimization function, dynamically adjusting weight coefficients and a closed loop iteration optimization strategy.

Inventors

LIU JING
ZHANG ZHENYA
YAN HUILI
Feng Xiangxiong
WANG SONG
ZHANG BING
MENG ZHAOFENG
DUAN HUIRONG

Assignees

郑州凯雪冷链股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260120

Claims (10)

1. The intelligent defrosting control method for the refrigerator is characterized by comprising the following steps of: S1, acquiring data, namely acquiring running state data of a refrigerator through a preset sensing device and an information acquisition module, wherein the running state data comprise goods temperature data of a plurality of shelf layers in the refrigerator, frosting state data of an evaporator, real-time energy consumption data of the refrigerator, passenger flow data of an environment where the refrigerator is located and real-time electricity price information data; s2, constructing a model, namely executing the following operations based on the operation state data acquired in the step S1: S21, calculating key optimization indexes, calculating defrosting energy consumption based on the real-time energy consumption data, calculating delivery temperature fluctuation deviation based on the delivery temperature data, and influencing degree of customer flow reflected based on the passenger flow data; S22, constructing a multi-objective optimization function by taking the defrosting energy consumption, the goods temperature fluctuation deviation and the customer flow influence degree calculated in the step S21 as core optimization targets, and further constructing a defrosting optimization model; S3, decision judgment, namely carrying out optimization training on the defrosting optimization model, and judging whether to start a defrosting program of the refrigerator or not based on an evaluation result output by the trained model and a preset judgment condition; s4, defrosting is carried out, namely if the step S3 judges that a defrosting program needs to be started, an optimal defrosting scheme is determined from the modes of electric heating defrosting, hot gas bypass defrosting, waste heat recovery defrosting and thermal energy storage auxiliary defrosting based on the evaluation result of the multi-objective optimization function and the real-time electricity price information data acquired in the step S1, and defrosting operation is triggered.
2. The intelligent defrost control method of claim 1, wherein the multi-objective optimization function is objective computable by quantifying the operating state data of step S1, wherein: The goods temperature fluctuation deviation is a difference value accumulation value between the goods temperature data and the preset standard goods temperature in the step S1; the defrosting energy consumption is calculated based on the real-time energy consumption data of the step S1 and the estimated defrosting time length; The customer flow influence is a weighted sum of the customer access frequency and the stay time in unit time.
3. The intelligent defrosting control method for a refrigerator according to claim 1 or 2, wherein the multi-objective optimization function is Wherein is The weight coefficient of the weight of the sample, In order to defrost the energy consumption, In order to compensate for the fluctuation of the cargo temperature during defrosting, Influence the degree for customer traffic.
4. The intelligent defrosting control method for a refrigerator according to claim 3, wherein the multi-objective optimization function takes the running state data of the step S1 as a core input, the weight coefficient is dynamically adjusted according to a defrosting scene, and a specific adjustment rule is as follows: peak customer flow scenario when customer access frequency per unit time >10 times/min, setting up =0.4, =0.2, =0.4; Low electricity price valley scenario when real-time electricity price is less than 0.3 yuan/kWh, setting =0.1, =0.5, =0.4; Severe frosting scenario when the evaporator frosting thickness is >5mm, set =0.6, =0.2, =0.2; Other scenes default weight coefficient settings to =0.3, =0.4, =0.3。
5. The intelligent defrost control method of claim 1, wherein the evaporator frosting status data comprises evaporator coil frosting thickness and heat exchange efficiency, and the customer passenger flow data is customer access frequency or residence time of the refrigerator per unit time.
6. The intelligent defrosting control method for the refrigerator according to claim 1, wherein in the step S3, the optimization training takes historical running state data acquired in the step S1 as training samples, and model parameters are adjusted through iteration to enable the matching degree of an evaluation result output by a model and actual defrosting requirements to be higher than a preset threshold, wherein the evaluation result is a defrosting necessity score calculated based on a normalization value of a multi-objective optimization function J.
7. The intelligent defrost control method according to claim 6, wherein the evaluation result includes a model predicted defrost necessity score, and the defrost program is judged to be started when the defrost necessity score is higher than a set threshold and the evaporator frosting thickness collected in the S1 step exceeds 3mm while the evaporator heat exchange efficiency is lower than a rated value of 70%.
8. The intelligent defrosting control method for a refrigerator according to claim 1, wherein the selection of the optimal defrosting scheme in the step S4 is based on the real-time electricity price information data acquired in the step S1, and the specific rules are as follows in combination with scene characteristics: The electricity price low-valley period is that when the real-time electricity price is less than 0.3 yuan/kWh, an electric heating defrosting mode is preferentially selected; The electricity price peak time is when the real-time electricity price is more than 0.8 yuan/kWh, the waste heat recovery defrosting mode is selected preferentially; the passenger flow peak period is that a hot gas bypass defrosting mode is preferentially selected; In all cases, the defrosting process is matched with the thermal energy storage module 6 to provide cold compensation so as to reduce the fluctuation range of the cargo temperature in the defrosting process.
9. The intelligent defrost control method of a refrigerator according to claim 1, further comprising: The refrigerator controller (4) synchronizes the real-time running state data acquired in the step S1 to the remote cloud server (5), the remote cloud server (5) generates an optimization strategy based on the historical running state data and the real-time data, the optimization strategy comprises a weight coefficient adjustment parameter and a threshold adjustment parameter, the remote cloud server (5) issues the optimization strategy to the controller (4), and the controller (4) updates the scheduling parameter based on the optimization strategy to realize self-adaptive optimization of defrosting control.
10. An intelligent defrost dispatch system for a refrigerator implementing the control method of any one of claims 1-9, comprising: The goods temperature sensor array module (1) is configured to collect goods temperature data of a plurality of goods shelf layers in the refrigerator in the step S1 and provide a data base for goods temperature fluctuation deviation calculation in the step S21; the evaporator state sensor (2) is configured to detect frosting state data of the evaporator in the step S1, output a detection result to the controller (4) and provide support for training of a defrosting optimization model; The business information acquisition module (3) is configured to acquire the passenger flow volume data and the real-time electricity price information data in the step S1, and synchronously upload the acquired data to the controller (4) and the remote cloud server (5); The remote cloud server (5) is configured to establish communication connection with the controller (4), store the historical running state data acquired in the step S1, provide data support for the defrosting optimization model training in the step S22, and send an optimization strategy to the controller (4); A thermal energy storage module (6) configured to provide cold compensation during defrosting and reduce the fluctuation range of the goods temperature during defrosting, wherein the thermal energy storage module (6) comprises a phase-change energy storage material and a cold energy releasing device, and the cold energy is released during defrosting by pre-storing cold energy before defrosting to maintain the stability of the goods temperature in the refrigerator, so that the fluctuation deviation of the goods temperature during defrosting is caused Controlled within + -1 deg.C.

Description

Intelligent defrosting control method and system for refrigerator Technical Field The invention relates to the technical field of refrigeration equipment operation control and intelligent energy-saving management, in particular to an intelligent defrosting control method and system for a refrigerator. Background In the long-term operation process of the refrigerator, frost is gradually accumulated on the surface of the evaporator, so that the heat exchange efficiency is reduced, the evaporation temperature is increased, and the energy consumption is increased. In order to maintain the refrigerating performance, the conventional refrigerator generally adopts a timing defrosting or temperature control defrosting mode, but has the following problems: The defrosting time is fixed and the energy consumption is high; The timing defrosting cycle is fixed, environmental conditions, cargo temperature or passenger flow change are not considered, the situation of 'frostless removal immediately' or 'frosting removal after serious' is easy to occur, the energy consumption is increased, and the service life of a compressor is shortened; The existing system does not consider electricity price fluctuation or customer passenger flow distribution, defrosting in peak time can influence goods taking experience, and defrosting in high electricity price time increases operation cost. Therefore, the intelligent defrosting scheduling system capable of integrating the cargo temperature, the frost state, the passenger flow and the electricity price information is provided, and the cooperative optimization of energy conservation and food safety is realized. Disclosure of Invention The invention aims to overcome the defects of the prior art, and provides an intelligent defrosting control method and system for a refrigerator, which are used for cooperatively acquiring multi-source data through a plurality of modules, constructing a dynamic optimization model, realizing accurate decision of defrosting time and mode, taking energy conservation, stable cargo temperature and low business interference into consideration, and improving the intellectualization and economy of the operation of the refrigerator. The technical scheme adopted by the invention for solving the problems is as follows: An intelligent defrosting control method for a refrigerator comprises the following steps: S1, acquiring data, namely acquiring running state data of a refrigerator through a preset sensing device and an information acquisition module, wherein the running state data comprise goods temperature data of a plurality of shelf layers in the refrigerator, frosting state data of an evaporator, real-time energy consumption data of the refrigerator, passenger flow data of an environment where the refrigerator is located and real-time electricity price information data; s2, constructing a model, namely executing the following operations based on the operation state data acquired in the step S1: S21, calculating key optimization indexes, calculating defrosting energy consumption based on the real-time energy consumption data, calculating delivery temperature fluctuation deviation based on the delivery temperature data, and influencing degree of customer flow reflected based on the passenger flow data; S22, constructing a multi-objective optimization function by taking the defrosting energy consumption, the goods temperature fluctuation deviation and the customer flow influence degree calculated in the step S21 as core optimization targets, and further constructing a defrosting optimization model; S3, decision judgment, namely carrying out optimization training on the defrosting optimization model, and judging whether to start a defrosting program of the refrigerator or not based on an evaluation result output by the trained model and a preset judgment condition; s4, defrosting is carried out, namely if the step S3 judges that a defrosting program needs to be started, an optimal defrosting scheme is determined from the modes of electric heating defrosting, hot gas bypass defrosting, waste heat recovery defrosting and thermal energy storage auxiliary defrosting based on the evaluation result of the multi-objective optimization function and the real-time electricity price information data acquired in the step S1, and defrosting operation is triggered. Compared with the prior art, the method has the beneficial effects that a multi-objective optimization model is built based on multi-dimensional running state data, the accuracy and the dynamics of a defrosting decision are realized, the problems of energy consumption waste and large fluctuation of goods temperature in the prior art are solved, the real-time electricity price and the frosting state of an evaporator are combined, the defrosting energy consumption and the electricity consumption are reduced, the service life of equipment is prolonged, the goods fresh-keeping effect is improved, the goods loss rate is reduc