Refrigeration control method and system for range hood

Abstract

The invention discloses a refrigeration control method and system for a range hood. The method comprises the steps that temperature data and humidity data in the range hood are obtained through a sensor arranged on the range hood; obtaining a first judgment result according to the temperature data and a preset temperature judgment rule; obtaining a second judgment result according to the humidity data and a preset humidity judgment rule; and according to the first judgment result and the second judgment result, a corresponding refrigeration instruction is generated and sent to a refrigeration module of the range hood to be executed. Therefore, accurate regulation and control of the temperature and the humidity in the range hood based on multi-mode environment perception can be achieved, the pertinence and the efficiency of operation of the refrigeration module are improved, and the risk of equipment aging or purification effect reduction caused by the fact that the temperature and the humidity are abnormal and are not adjusted in time is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a refrigeration control method and system for range hoods. Background Technology

[0002] With the rapid increase in demand for intelligent and long-life range hoods, users and manufacturers are paying more and more attention to ensuring equipment stability and purification performance through precise internal temperature and humidity control. A key technical issue is how to achieve targeted cooling adjustment based on multimodal real-time sensing to prevent equipment aging or decreased purification efficiency. Current technologies typically use a single temperature or humidity sensor inside the range hood, employing fixed threshold triggering or timed operation to control the cooling module to maintain a basic working environment. However, existing solutions lack real-time independent judgment and comprehensive decision-making regarding temperature and humidity data, as well as a dynamic generation mechanism for cooling commands. This makes it difficult to achieve truly targeted temperature and humidity control. Commonly used coarse or delayed response control strategies cannot adapt to the transient environment inside the range hood, leading to low operating efficiency of the cooling module. Failure to adjust to abnormal temperature and humidity in time can cause electronic component aging, motor failure, or decreased purification efficiency, limiting the reliability and long-term performance of the range hood. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a cooling control method and system for range hoods, which can realize precise internal temperature and humidity control of range hoods based on multimodal environmental perception, improve the targeting and efficiency of the cooling module operation, and reduce the risk of equipment aging or reduced purification effect caused by abnormal temperature and humidity not being adjusted in time.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a cooling control method for a range hood, the method comprising: Temperature and humidity data inside the range hood are acquired by sensors installed in the range hood. Based on the temperature data and the preset temperature judgment rules, a first judgment result is obtained; Based on the humidity data and the preset humidity judgment rules, a second judgment result is obtained; Based on the first and second judgment results, a corresponding cooling command is generated and sent to the cooling module of the range hood for execution.

[0005] As an optional implementation, in the first aspect of the present invention, obtaining the first judgment result based on the temperature data and a preset temperature judgment rule includes: Based on the data acquisition time point corresponding to the temperature data, determine the reference temperature data for the temperature data; Calculate the difference between the temperature data and the reference temperature data; Determine whether the difference value is greater than a preset first judgment threshold to obtain a first judgment result.

[0006] As an optional implementation, in the first aspect of the present invention, determining the reference temperature data of the temperature data based on the data acquisition time point corresponding to the temperature data includes: Based on the data acquisition time point, multiple historical temperature data points with the same time point are determined in the historical temperature database; The data sequence is obtained by sorting the multiple historical temperature data from morning to night according to the acquisition date; Based on a stable data identification model, the stable sequence portion in the data sequence is identified. The average value of all the historical temperature data in the stable sequence portion is calculated to obtain the reference temperature data.

[0007] As an optional implementation, in the first aspect of the present invention, the stable data identification model is a deep learning model, which is trained on a training dataset including multiple training temperature data sequences and corresponding stable sequence parts labeled.

[0008] As an optional implementation, in the first aspect of the present invention, obtaining the second judgment result based on the humidity data and a preset humidity judgment rule includes: Determine the data monitoring location inside the range hood corresponding to each humidity data point; Based on the data monitoring location, select the humidity data of interest from all the humidity data; The second judgment result is obtained based on the humidity data of concern and the second judgment threshold corresponding to the range hood.

[0009] As an optional implementation, in the first aspect of the invention, the step of filtering out humidity data of interest from all the humidity data based on the data monitoring location includes: The data monitoring locations where the humidity data is greater than a preset humidity threshold are selected to obtain multiple high humidity locations; Calculate the geometric center point of all the high humidity locations to obtain the reference monitoring location; For each humidity data point, calculate the location distance between the data monitoring location corresponding to that humidity data and the reference monitoring location; Calculate the humidity weight that is inversely proportional to the distance to the location; The attention level corresponding to the humidity data is obtained by multiplying the data value of the humidity data and the humidity weight. The humidity data with a level of attention greater than a preset attention threshold are filtered out to obtain multiple humidity data sets of interest.

[0010] As an optional implementation, in the first aspect of the present invention, obtaining the second judgment result based on the humidity data of interest and the second judgment threshold corresponding to the range hood includes: Calculate the weighted average of all the humidity data of interest to obtain humidity characterization data; wherein, the calculation weight corresponding to each humidity data of interest is proportional to the degree of interest. Determine whether the humidity characterization data is greater than a preset second judgment threshold to obtain a second judgment result.

[0011] As an optional implementation, in the first aspect of the present invention, the step of generating a corresponding cooling command based on the first judgment result and the second judgment result and sending it to the cooling module of the range hood for execution includes: When both the first and second judgment results show that the temperature data or the humidity data exceeds the corresponding judgment threshold. Based on the difference between the temperature data or humidity data and the corresponding judgment threshold, the corresponding refrigeration operating parameters are predicted according to the preset refrigeration parameter prediction model. A start command including the cooling operating parameters is generated and sent to the cooling module of the range hood for execution.

[0012] A second aspect of this invention discloses a refrigeration control system for a range hood, the system comprising: The acquisition module is used to acquire temperature and humidity data inside the range hood through sensors installed in the range hood; The first judgment module is used to obtain a first judgment result based on the temperature data and the preset temperature judgment rules; The second judgment module is used to obtain a second judgment result based on the humidity data and the preset humidity judgment rules; The control module is used to generate a corresponding cooling command based on the first judgment result and the second judgment result, and send it to the cooling module of the range hood for execution.

[0013] As an optional implementation, in a second aspect of the present invention, the specific method by which the first judgment module obtains the first judgment result based on the temperature data and a preset temperature judgment rule includes: Based on the data acquisition time point corresponding to the temperature data, determine the reference temperature data for the temperature data; Calculate the difference between the temperature data and the reference temperature data; Determine whether the difference value is greater than a preset first judgment threshold to obtain a first judgment result.

[0014] As an optional implementation, in a second aspect of the present invention, the first determining module determines the reference temperature data of the temperature data based on the data acquisition time point corresponding to the temperature data in a specific manner, including: Based on the data acquisition time point, multiple historical temperature data points with the same time point are determined in the historical temperature database; The data sequence is obtained by sorting the multiple historical temperature data from morning to night according to the acquisition date; Based on a stable data identification model, the stable sequence portion in the data sequence is identified. The average value of all the historical temperature data in the stable sequence portion is calculated to obtain the reference temperature data.

[0015] As an optional implementation, in the second aspect of the present invention, the stable data identification model is a deep learning model, which is trained on a training dataset including multiple training temperature data sequences and corresponding stable sequence parts labeled.

[0016] As an optional implementation, in a second aspect of the present invention, the specific method by which the second judgment module obtains the second judgment result based on the humidity data and a preset humidity judgment rule includes: Determine the data monitoring location inside the range hood corresponding to each humidity data point; Based on the data monitoring location, select the humidity data of interest from all the humidity data; The second judgment result is obtained based on the humidity data of concern and the second judgment threshold corresponding to the range hood.

[0017] As an optional implementation, in a second aspect of the invention, the second determining module filters out the humidity data of interest from all the humidity data based on the data monitoring location, including: The data monitoring locations where the humidity data is greater than a preset humidity threshold are selected to obtain multiple high humidity locations; Calculate the geometric center point of all the high humidity locations to obtain the reference monitoring location; For each humidity data point, calculate the location distance between the data monitoring location corresponding to that humidity data and the reference monitoring location; Calculate the humidity weight that is inversely proportional to the distance to the location; The attention level corresponding to the humidity data is obtained by multiplying the data value of the humidity data and the humidity weight. The humidity data with a level of attention greater than a preset attention threshold are filtered out to obtain multiple humidity data sets of interest.

[0018] As an optional implementation, in a second aspect of the present invention, the specific method by which the second judgment module obtains the second judgment result based on the humidity data of interest and the second judgment threshold corresponding to the range hood includes: Calculate the weighted average of all the humidity data of interest to obtain humidity characterization data; wherein, the calculation weight corresponding to each humidity data of interest is proportional to the degree of interest. Determine whether the humidity characterization data is greater than a preset second judgment threshold to obtain a second judgment result.

[0019] As an optional implementation, in a second aspect of the present invention, the specific method by which the control module generates a corresponding cooling command based on the first judgment result and the second judgment result and sends it to the cooling module of the range hood for execution includes: When both the first and second judgment results show that the temperature data or the humidity data exceeds the corresponding judgment threshold. Based on the difference between the temperature data or humidity data and the corresponding judgment threshold, the corresponding refrigeration operating parameters are predicted according to the preset refrigeration parameter prediction model. A start command including the cooling operating parameters is generated and sent to the cooling module of the range hood for execution.

[0020] A third aspect of the present invention discloses another refrigeration control system for a range hood, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the cooling control method for a range hood disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the cooling control method for a range hood disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention collects temperature and humidity data in real time through sensors inside the range hood, generates first and second judgment results based on preset temperature and humidity judgment rules, and generates a cooling command based on these results, which is then sent to the cooling module for execution. This enables precise temperature and humidity control inside the range hood based on multimodal environmental perception, improves the targeting and efficiency of the cooling module's operation, and reduces the risk of equipment aging or decreased purification effect due to failure to adjust abnormal temperature and humidity in time. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a cooling control method for a range hood disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a refrigeration control system for a range hood disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another cooling control system for a range hood disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a cooling control method and system for range hoods. It collects temperature and humidity data in real time using sensors inside the range hood, generates first and second judgment results based on preset temperature and humidity judgment rules, and then generates cooling commands to be sent to the cooling module for execution. This enables precise temperature and humidity control inside the range hood based on multimodal environmental perception, improving the targeting and efficiency of the cooling module's operation and reducing the risk of equipment aging or decreased purification effect due to failure to adjust for abnormal temperature and humidity in a timely manner. Detailed descriptions follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a cooling control method for a range hood disclosed in an embodiment of the present invention. Figure 1 The described cooling control method for range hoods can be applied to data processing systems / data processing equipment / data processing servers (wherein the server includes local processing servers or cloud processing servers). For example... Figure 1 As shown, the cooling control method for a range hood may include the following operations: 101. Temperature and humidity data inside the range hood are obtained through sensors installed in the range hood.

[0032] Optionally, the sensor can be a DS18B20 digital temperature sensor, an SHT35 high-precision temperature and humidity sensor, or a DHT22 sensor array arranged at multiple points; the present invention does not limit the type of sensor.

[0033] Optionally, the temperature and humidity data can be real-time sampled values ​​with a sampling frequency of 1Hz-10Hz, which is not limited in this invention.

[0034] 102. Based on the temperature data and the preset temperature judgment rules, the first judgment result is obtained.

[0035] Optionally, the first judgment result can be "normal temperature" or "abnormal temperature", and the present invention does not limit it.

[0036] 103. Based on the humidity data and the preset humidity judgment rules, the second judgment result is obtained.

[0037] Optionally, the second judgment result can be "normal humidity" or "abnormal humidity", and the present invention does not limit it.

[0038] 104. Based on the first and second judgment results, generate the corresponding cooling command and send it to the cooling module of the range hood for execution.

[0039] Optionally, the refrigeration module can be a thermoelectric cooler (TEC), a compressor condenser module, or an air-cooled heat exchanger; the present invention does not limit the specific type of cooler.

[0040] As can be seen, the above-mentioned embodiments of the invention collect temperature and humidity data in real time through sensors inside the range hood, generate first and second judgment results based on preset temperature and humidity judgment rules, and generate cooling commands accordingly, which are then sent to the cooling module for execution. This enables precise temperature and humidity control inside the range hood based on multimodal environmental perception, improves the targeting and efficiency of the cooling module's operation, and reduces the risk of equipment aging or decreased purification effect due to failure to adjust abnormal temperature and humidity in time.

[0041] As an optional embodiment, the step above, obtaining the first judgment result based on temperature data and a preset temperature judgment rule, includes: Based on the data acquisition time point corresponding to the temperature data, determine the reference temperature data for the temperature data; Calculate the difference between the temperature data and the reference temperature data; Determine whether the difference value is greater than a preset first judgment threshold, and obtain the first judgment result.

[0042] Optionally, the data acquisition time point is accurate to the minute (e.g., 19:45), but this invention does not limit it.

[0043] Optionally, the difference value can be the current temperature minus the reference temperature, and positive or negative values ​​are supported; this invention does not limit this.

[0044] Optionally, the first judgment threshold can be dynamically adjusted according to the season and region, such as a summer threshold of +10℃ and a winter threshold of +6℃. This invention does not limit this.

[0045] As can be seen, through the above optional embodiments, by determining the reference temperature data based on the time point of temperature data acquisition and calculating the difference value with the current temperature data, and generating the first judgment result based on the first judgment threshold, accurate anomaly detection based on dynamic temperature benchmark is achieved, improving the accuracy and timeliness of temperature anomaly judgment, and reducing the risk of temperature misjudgment caused by fixed threshold or time point deviation.

[0046] As an optional embodiment, the step above, determining the reference temperature data based on the data acquisition time point corresponding to the temperature data, includes: Based on the data acquisition time point, multiple historical temperature data points with the same time point are identified in the historical temperature database; A data sequence is obtained by sorting multiple historical temperature data from morning to night according to the acquisition date; Based on a stable data identification model, the stable sequence portion in a data sequence is identified. The average value of all historical temperature data in the stable sequence is calculated to obtain the reference temperature data.

[0047] Optionally, the historical temperature database may store temperature records at the same time points over the past 90 days; however, this invention does not impose any limitations on this.

[0048] Optionally, the length of the data sequence is typically 90 points (90 days), but this invention does not limit it.

[0049] Optionally, the stable data identification model is a deep learning model, which is trained on a training dataset that includes multiple training temperature data sequences and corresponding stable sequence parts labeled.

[0050] Optionally, the stable data identification model can be a sequence labeling model consisting of 2-layer one-dimensional convolution (kernel=3) + BiLSTM (128 units) + CRF, labeled as "stable / fluctuating / abnormal", trained on 100,000 kitchen temperature sequences, achieving an F1 score of 0.94. This invention does not impose any limitations.

[0051] Optionally, the average of all historical temperature data can be truncated after removing the highest and lowest 5% to improve robustness, but this invention does not limit this.

[0052] As can be seen, through the above optional embodiments, by extracting and sorting data from the historical temperature database at the same time point, identifying the stable sequence part based on the stable data identification model and calculating the average value as the reference temperature data, an adaptive temperature benchmark based on historical data is constructed, which improves the representativeness and stability of the reference temperature and reduces the risk of benchmark distortion caused by historical data fluctuations or noise.

[0053] As an optional embodiment, the step above, obtaining the second judgment result based on humidity data and a preset humidity judgment rule, includes: Determine the data monitoring location inside the range hood corresponding to each humidity data point; Based on the data monitoring location, filter out the humidity data of interest from all humidity data; The second judgment result is obtained based on the humidity data and the second judgment threshold corresponding to the range hood.

[0054] As can be seen, through the above optional embodiments, by filtering the humidity data of interest according to the humidity data monitoring location and generating a second judgment result based on the second judgment threshold, accurate identification of humidity anomalies based on spatial location is achieved, improving the pertinence and spatial relevance of humidity anomaly judgment, and reducing the risk of humidity misjudgment caused by ignoring location differences.

[0055] As an optional embodiment, the step above, filtering out humidity data of interest from all humidity data based on the data monitoring location, includes: The monitoring locations with humidity data exceeding the preset humidity threshold were selected to obtain multiple high humidity locations; Calculate the geometric center point of all high humidity locations to obtain the reference monitoring location; For each humidity data point, calculate the location distance between the data monitoring location and the reference monitoring location corresponding to that humidity data point; Calculate the humidity weight that is inversely proportional to the location distance; The product of the humidity data value and the humidity weight is calculated to obtain the attention level corresponding to the humidity data; Humidity data with a focus level greater than a preset focus level threshold are filtered out to obtain multiple humidity data sets of interest.

[0056] Optionally, the data monitoring location can be in coordinate form (x, y, z) or a textual description, such as "left condenser plate", "top air outlet", or "right water collection box". This invention does not impose any limitations.

[0057] Optionally, the humidity data to be screened can be focused on key areas prone to condensation, but this invention does not limit this.

[0058] Optionally, the second judgment threshold can be a relative humidity of 85%RH, which is not limited in this invention.

[0059] Optionally, the humidity threshold can be set according to the material's tolerance; this invention does not impose any limitation on it.

[0060] Optionally, the humidity weight can be weight = 1 / (distance + ε), where distance is the location distance and ε is a small constant to prevent division by zero. This invention does not limit the weight.

[0061] Optionally, a higher level of attention indicates that the area is closer to the high-humidity core area and the humidity is higher; however, this invention does not impose any limitations on this.

[0062] Optionally, the attention threshold can be an empirical value of 0.75 or a dynamic percentile threshold; this invention does not impose any limitation on it.

[0063] As can be seen, through the above optional embodiments, by screening high humidity locations and calculating the geometric center point, calculating the attention level based on the inverse weight of location distance and screening the attention humidity data, intelligent focusing on high humidity areas based on spatial distribution is achieved, improving the accuracy and representativeness of attention humidity data extraction, and reducing the risk of attention data deviation caused by uneven humidity distribution.

[0064] As an optional embodiment, the step above, obtaining the second judgment result based on the humidity data of interest and the second judgment threshold corresponding to the range hood, includes: Calculate the weighted average of all humidity data of interest to obtain humidity characterization data; optionally, the calculation weight of each humidity data of interest is proportional to the degree of interest. Determine whether the humidity characterization data is greater than a preset second judgment threshold, and obtain the second judgment result.

[0065] As can be seen, through the above optional embodiments, by calculating the weighted average of the humidity data of concern with a weight proportional to the degree of concern as the humidity characterization data and generating a second judgment result based on the second judgment threshold, a comprehensive assessment of humidity anomalies based on weighted fusion is achieved, which improves the comprehensiveness and accuracy of humidity characterization and reduces the risk of misjudgment of humidity anomalies caused by a single data point.

[0066] As an optional embodiment, the step described above, generating a corresponding cooling command based on the first and second judgment results and sending it to the cooling module of the range hood for execution, includes: When both the first and second judgment results show that the temperature or humidity data exceeds the corresponding judgment threshold. Based on the difference between temperature or humidity data and the corresponding judgment threshold, the corresponding refrigeration operating parameters are predicted using a preset refrigeration parameter prediction model. A start command containing refrigeration operating parameters is generated and sent to the refrigeration module of the range hood for execution.

[0067] Optionally, the refrigeration parameter prediction model can be a 3-layer fully connected network (input 2D: temperature difference + humidity difference, hidden layers 64-32, output refrigeration power percentage 0-100%), trained on 80,000 measured condensation events, with a MAE < 3.2%, but this invention does not limit it.

[0068] Optionally, the start command may include parameters such as target power, duration, and duct switching; this invention does not impose any limitations on these parameters.

[0069] As can be seen, through the above optional embodiments, by generating refrigeration operating parameters based on the difference and refrigeration parameter prediction model when both temperature and humidity exceed the threshold and sending a start command, the precise adaptive adjustment of refrigeration parameters based on the degree of anomaly can be achieved, thereby improving the response efficiency and energy saving of the refrigeration module and reducing the risk of insufficient refrigeration or energy waste caused by improper parameter settings.

[0070] The application details of the technical solutions in the embodiments of the present invention are illustrated by a specific case: A brand 18kW commercial range hood was operating during peak cooking hours at restaurant B, with 9 internal SHT35 sensors monitoring it in real time. The temperature of the left condenser plate is 42.3℃ (the historical average for the same period is 33.1℃, and the difference is +9.2℃, which is greater than the 8℃ threshold). Humidity levels at multiple locations reached 94%-98%, with the geometric center located near the top air outlet. The weighted average humidity level was 96.7%, exceeding the 88% threshold. If both temperature and humidity are abnormal, cooling will be triggered.

[0071] Input a temperature difference of 9.2℃ and a humidity difference of 8.7% into the refrigeration parameter prediction model, and the model will output a suggested refrigeration power of 87%. Issue the command: The thermoelectric cooler will operate at 87% power for 30 minutes, while some return air valves will be closed.

[0072] After 15 minutes, the internal temperature dropped to 35.6℃ and the humidity dropped to 79%, successfully preventing condensation from dripping onto the food. There were no more condensation alarms throughout the night, and the power consumption of the cooling module only increased by 11%, achieving precise and efficient dehumidification and cooling.

[0073] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a refrigeration control system for a range hood disclosed in an embodiment of the present invention. Wherein, Figure 2 The described refrigeration control system for range hoods can be applied to data processing systems / data processing equipment / data processing servers (wherein the server includes local processing servers or cloud processing servers). For example... Figure 2 As shown, the refrigeration control system for the range hood may include: The acquisition module 201 is used to acquire temperature and humidity data inside the range hood through sensors installed in the range hood.

[0074] The first judgment module 202 is used to obtain a first judgment result based on temperature data and preset temperature judgment rules.

[0075] The second judgment module 203 is used to obtain a second judgment result based on humidity data and preset humidity judgment rules.

[0076] The control module 204 is used to generate corresponding cooling commands based on the first judgment result and the second judgment result, and send them to the cooling module of the range hood for execution.

[0077] As can be seen, the above-mentioned embodiments of the invention collect temperature and humidity data in real time through sensors inside the range hood, generate first and second judgment results based on preset temperature and humidity judgment rules, and generate cooling commands accordingly, which are then sent to the cooling module for execution. This enables precise temperature and humidity control inside the range hood based on multimodal environmental perception, improves the targeting and efficiency of the cooling module's operation, and reduces the risk of equipment aging or decreased purification effect due to failure to adjust abnormal temperature and humidity in time.

[0078] As an optional embodiment, the specific method by which the first judgment module obtains the first judgment result based on temperature data and preset temperature judgment rules includes: Based on the data acquisition time point corresponding to the temperature data, determine the reference temperature data for the temperature data; Calculate the difference between the temperature data and the reference temperature data; Determine whether the difference value is greater than a preset first judgment threshold, and obtain the first judgment result.

[0079] As can be seen, through the above optional embodiments, by determining the reference temperature data based on the time point of temperature data acquisition and calculating the difference value with the current temperature data, and generating the first judgment result based on the first judgment threshold, accurate anomaly detection based on dynamic temperature benchmark is achieved, improving the accuracy and timeliness of temperature anomaly judgment, and reducing the risk of temperature misjudgment caused by fixed threshold or time point deviation.

[0080] As an optional embodiment, the first determination module determines the specific method of the reference temperature data based on the data acquisition time point corresponding to the temperature data, including: Based on the data acquisition time point, multiple historical temperature data points with the same time point are identified in the historical temperature database; A data sequence is obtained by sorting multiple historical temperature data from morning to night according to the acquisition date; Based on a stable data identification model, the stable sequence portion in a data sequence is identified. The average value of all historical temperature data in the stable sequence is calculated to obtain the reference temperature data.

[0081] As can be seen, through the above optional embodiments, by extracting and sorting data from the historical temperature database at the same time point, identifying the stable sequence part based on the stable data identification model and calculating the average value as the reference temperature data, an adaptive temperature benchmark based on historical data is constructed, which improves the representativeness and stability of the reference temperature and reduces the risk of benchmark distortion caused by historical data fluctuations or noise.

[0082] As an optional embodiment, the stable data identification model is a deep learning model, which is trained on a training dataset that includes multiple training temperature data sequences and corresponding stable sequence parts labeled.

[0083] As can be seen, the model details of the stable data identification model are defined through the above optional embodiments, so as to accurately identify the stable part in the temperature time series data, assist in realizing precise temperature and humidity control inside the range hood based on multimodal environmental perception, improve the targeting and efficiency of the refrigeration module operation, and reduce the risk of equipment aging or reduced purification effect due to abnormal temperature and humidity not being adjusted in time.

[0084] As an optional embodiment, the second judgment module obtains the second judgment result based on humidity data and preset humidity judgment rules in the following specific ways: Determine the data monitoring location inside the range hood corresponding to each humidity data point; Based on the data monitoring location, filter out the humidity data of interest from all humidity data; The second judgment result is obtained based on the humidity data and the second judgment threshold corresponding to the range hood.

[0085] As can be seen, through the above optional embodiments, by filtering the humidity data of interest according to the humidity data monitoring location and generating a second judgment result based on the second judgment threshold, accurate identification of humidity anomalies based on spatial location is achieved, improving the pertinence and spatial relevance of humidity anomaly judgment, and reducing the risk of humidity misjudgment caused by ignoring location differences.

[0086] As an optional embodiment, the second judgment module filters out the specific humidity data of interest from all humidity data based on the data monitoring location, including: The monitoring locations with humidity data exceeding the preset humidity threshold were selected to obtain multiple high humidity locations; Calculate the geometric center point of all high humidity locations to obtain the reference monitoring location; For each humidity data point, calculate the location distance between the data monitoring location and the reference monitoring location corresponding to that humidity data point; Calculate the humidity weight that is inversely proportional to the location distance; The product of the humidity data value and the humidity weight is calculated to obtain the attention level corresponding to the humidity data; Humidity data with a focus level greater than a preset focus level threshold are filtered out to obtain multiple humidity data sets of interest.

[0087] As can be seen, through the above optional embodiments, by screening high humidity locations and calculating the geometric center point, calculating the attention level based on the inverse weight of location distance and screening the attention humidity data, intelligent focusing on high humidity areas based on spatial distribution is achieved, improving the accuracy and representativeness of attention humidity data extraction, and reducing the risk of attention data deviation caused by uneven humidity distribution.

[0088] As an optional embodiment, the second judgment module obtains the second judgment result based on the humidity data of interest and the second judgment threshold corresponding to the range hood in the following specific ways: Calculate the weighted average of all humidity data of interest to obtain humidity characterization data; optionally, the calculation weight of each humidity data of interest is proportional to the degree of interest. Determine whether the humidity characterization data is greater than a preset second judgment threshold, and obtain the second judgment result.

[0089] As can be seen, through the above optional embodiments, by calculating the weighted average of the humidity data of concern with a weight proportional to the degree of concern as the humidity characterization data and generating a second judgment result based on the second judgment threshold, a comprehensive assessment of humidity anomalies based on weighted fusion is achieved, which improves the comprehensiveness and accuracy of humidity characterization and reduces the risk of misjudgment of humidity anomalies caused by a single data point.

[0090] As an optional embodiment, the specific method by which the control module generates a corresponding cooling command based on the first and second judgment results and sends it to the cooling module of the range hood for execution includes: When both the first and second judgment results show that the temperature or humidity data exceeds the corresponding judgment threshold. Based on the difference between temperature or humidity data and the corresponding judgment threshold, the corresponding refrigeration operating parameters are predicted using a preset refrigeration parameter prediction model. A start command containing refrigeration operating parameters is generated and sent to the refrigeration module of the range hood for execution.

[0091] As can be seen, through the above optional embodiments, by generating refrigeration operating parameters based on the difference and refrigeration parameter prediction model when both temperature and humidity exceed the threshold and sending a start command, the precise adaptive adjustment of refrigeration parameters based on the degree of anomaly can be achieved, thereby improving the response efficiency and energy saving of the refrigeration module and reducing the risk of insufficient refrigeration or energy waste caused by improper parameter settings.

[0092] Example 3 Please see Figure 3 , Figure 3 This is another refrigeration control system for range hoods disclosed in the embodiments of the present invention. Figure 3The described refrigeration control system for range hoods is applied in data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 3 As shown, the refrigeration control system for the range hood may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the cooling control method for a range hood described in Embodiment 1.

[0093] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the cooling control method for a range hood described in Embodiment 1.

[0094] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the cooling control method for a range hood described in Embodiment 1.

[0095] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0097] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0098] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0104] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0106] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0107] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0108] Finally, it should be noted that the cooling control method and system for range hoods disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cooling control method for a range hood, characterized in that, The method includes: Temperature and humidity data inside the range hood are acquired by sensors installed in the range hood. Based on the temperature data and the preset temperature judgment rules, a first judgment result is obtained; Based on the humidity data and the preset humidity judgment rules, a second judgment result is obtained; Based on the first and second judgment results, a corresponding cooling command is generated and sent to the cooling module of the range hood for execution.

2. The refrigeration control method for a range hood according to claim 1, characterized in that, The step of obtaining a first judgment result based on the temperature data and a preset temperature judgment rule includes: Based on the data acquisition time point corresponding to the temperature data, determine the reference temperature data for the temperature data; Calculate the difference between the temperature data and the reference temperature data; Determine whether the difference value is greater than a preset first judgment threshold to obtain a first judgment result.

3. The refrigeration control method for a range hood according to claim 2, characterized in that, The step of determining the reference temperature data based on the data acquisition time point corresponding to the temperature data includes: Based on the data acquisition time point, multiple historical temperature data points with the same time point are determined in the historical temperature database; The data sequence is obtained by sorting the multiple historical temperature data from morning to night according to the acquisition date; Based on a stable data identification model, the stable sequence portion in the data sequence is identified. The average value of all the historical temperature data in the stable sequence portion is calculated to obtain the reference temperature data.

4. The refrigeration control method for a range hood according to claim 3, characterized in that, The stable data identification model is a deep learning model, which is trained on a training dataset that includes multiple training temperature data sequences and corresponding stable sequence parts labeled.

5. The refrigeration control method for a range hood according to claim 1, characterized in that, The step of obtaining a second judgment result based on the humidity data and a preset humidity judgment rule includes: Determine the data monitoring location inside the range hood corresponding to each humidity data point; Based on the data monitoring location, select the humidity data of interest from all the humidity data; The second judgment result is obtained based on the humidity data of concern and the second judgment threshold corresponding to the range hood.

6. The refrigeration control method for a range hood according to claim 5, characterized in that, The step of filtering out humidity data of interest from all humidity data based on the data monitoring location includes: The data monitoring locations where the humidity data is greater than a preset humidity threshold are selected to obtain multiple high humidity locations; Calculate the geometric center point of all the high humidity locations to obtain the reference monitoring location; For each humidity data point, calculate the location distance between the data monitoring location corresponding to that humidity data and the reference monitoring location; Calculate the humidity weight that is inversely proportional to the distance to the location; The attention level corresponding to the humidity data is obtained by multiplying the data value of the humidity data and the humidity weight. The humidity data with a level of attention greater than a preset attention threshold are filtered out to obtain multiple humidity data sets of interest.

7. The refrigeration control method for a range hood according to claim 5, characterized in that, The step of obtaining a second judgment result based on the humidity data of concern and the second judgment threshold corresponding to the range hood includes: Calculate the weighted average of all the humidity data of interest to obtain humidity characterization data; wherein, the calculation weight corresponding to each humidity data of interest is proportional to the degree of interest. Determine whether the humidity characterization data is greater than a preset second judgment threshold to obtain a second judgment result.

8. The refrigeration control method for a range hood according to claim 1, characterized in that, The step of generating a corresponding cooling command based on the first judgment result and the second judgment result and sending it to the cooling module of the range hood for execution includes: When both the first and second judgment results show that the temperature data or the humidity data exceeds the corresponding judgment threshold. Based on the difference between the temperature data or humidity data and the corresponding judgment threshold, the corresponding refrigeration operating parameters are predicted according to the preset refrigeration parameter prediction model. A start command including the cooling operating parameters is generated and sent to the cooling module of the range hood for execution.

9. A refrigeration control system for a range hood, characterized in that, The system includes: The acquisition module is used to acquire temperature and humidity data inside the range hood through sensors installed in the range hood; The first judgment module is used to obtain a first judgment result based on the temperature data and the preset temperature judgment rules; The second judgment module is used to obtain a second judgment result based on the humidity data and the preset humidity judgment rules; The control module is used to generate a corresponding cooling command based on the first judgment result and the second judgment result, and send it to the cooling module of the range hood for execution.

10. A refrigeration control system for a range hood, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the cooling control method for a range hood as described in any one of claims 1-8.

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