Food temperature measurement method, apparatus, intelligent refrigeration device, and storage medium

Abstract

The present disclosure relates to a food temperature measurement method, device, intelligent refrigeration equipment and storage medium. The above method is applied to an intelligent refrigeration equipment, including obtaining wavelength intensity information corresponding to a target food in the intelligent refrigeration equipment based on an infrared optical temperature measurement module, the wavelength intensity information including first radiation intensity corresponding to a first wavelength and second radiation intensity corresponding to a second wavelength; performing temperature backstepping processing based on the first radiation intensity and the second radiation intensity by using a colorimetric method to obtain an initial predicted temperature; determining a target emissivity corresponding to the target food based on a preset emissivity table; and determining a target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature. By using wavelength selection and algorithm optimization, the present disclosure effectively suppresses interference such as ambient light and reflection of the inner wall of the intelligent refrigeration equipment, is suitable for complex storage environments, optimizes the initial predicted temperature according to the emissivity of different foods, and is suitable for precise temperature measurement of various foods.

Description

Technical Field

[0001] This disclosure relates to the field of refrigeration equipment control, and in particular to food temperature measurement methods, devices, intelligent refrigeration equipment, and storage media. Background Technology

[0002] Currently, temperature measurement technology in refrigeration equipment mainly relies on single-band infrared sensors or contact temperature probes, such as in refrigerators. Single-band infrared sensors measure temperature by scanning the surface of food in the storage compartment, but they are significantly affected by differences in the emissivity of food surfaces, leading to large measurement errors. For example, the emissivity of meat is approximately 0.95, while that of metal packaging is only about 0.1. On the other hand, while contact sensors can directly measure temperature, their limitation lies in the requirement for physical contact with the food. This not only makes it impossible to achieve real-time temperature monitoring at multiple points within the storage compartment but also makes it difficult to cover the entire storage space. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, this disclosure provides a food temperature measurement method, apparatus, intelligent refrigeration device, and storage medium.

[0004] According to one aspect of this disclosure, a food temperature measurement method is provided, applied to an intelligent refrigeration device, the intelligent refrigeration device including an infrared optical temperature measurement module, which comprises: Based on the infrared optical temperature measurement module, wavelength intensity information corresponding to the target food in the intelligent refrigeration device is obtained, and the wavelength intensity information includes a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. Based on the first radiation intensity and the second radiation intensity, a colorimetric method is used to back-calculate the temperature to obtain the initial predicted temperature. The target emissivity of the target food is determined based on a preset emissivity table. The target temperature corresponding to the target food is determined based on the target emissivity and the initial predicted temperature.

[0005] In some possible implementations, the intelligent cooling device further includes a temperature monitoring module, and the method further includes: If the intelligent refrigeration device meets the dynamic calibration conditions, the temperature monitoring module obtains multiple actual measured temperatures of the target calibrated food at multiple times during the target time period, and the infrared optical temperature measurement module obtains multiple calibration wavelength intensity information corresponding to the multiple actual measured temperatures of the target calibrated food. Based on the multiple calibration wavelength intensity information, determine multiple initial calibration prediction temperatures corresponding to the multiple actual measured temperatures; The target emissivity of the target calibrated food is obtained by fitting multiple initial calibration predicted temperatures and multiple actual measured temperatures. The emissivity of the target calibrated food in the preset emissivity table is updated based on the target emissivity.

[0006] In some possible implementations, determining the target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature includes: The target temperature is obtained by taking the ratio of the initial predicted temperature and the target emissivity.

[0007] In some possible implementations, the intelligent cooling device further includes a pressure sensor, and the method further includes: If the cooling stop time corresponding to the intelligent refrigeration device is greater than or equal to the first preset time, the door opening time corresponding to the intelligent refrigeration device is greater than or equal to the second preset time, or the continuous triggering time of the pressure sensor is greater than or equal to the third preset time, it is determined that the intelligent refrigeration device meets the dynamic calibration conditions.

[0008] In some possible implementations, the infrared optical temperature measurement module includes a beam splitter, a first filter, a second filter, a first detector, a second detector, and a signal processing unit. The step of acquiring wavelength intensity information corresponding to the target food in the intelligent refrigeration device based on the infrared optical temperature measurement module includes: The radiation signal corresponding to the target object is split by the beam splitter to obtain reflected light and transmitted light. The reflected light and the transmitted light are respectively subjected to wavelength filtering processing based on the first filter and the second filter to obtain the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength; Based on the first detector and the second detector, the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength are respectively converted into signals to obtain a first electrical signal corresponding to the first wavelength and a second electrical signal corresponding to the second wavelength. The signal processing unit performs signal processing on the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

[0009] According to a second aspect of this disclosure, an intelligent cooling device is provided, including an infrared optical temperature measurement module, a temperature monitoring module, a pressure sensor, and a processor; The infrared optical temperature measurement module is used to detect the wavelength intensity information corresponding to the food in the intelligent refrigeration device. The wavelength intensity information includes the first radiation intensity corresponding to the first wavelength and the second radiation intensity corresponding to the second wavelength. The temperature monitoring module is used to detect the actual measured temperature of the food. The pressure sensor is used to detect the trigger pressure of the food; The processor is used to execute the food temperature measurement method as described in any one of the first aspects.

[0010] In some possible implementations, the infrared optical temperature measurement module includes a beam splitter, a first filter, a second filter, a first detector, a second detector, and a signal processing unit; The beam-splitting prism is used to split the radiation signal corresponding to the food to obtain reflected light and transmitted light. The first filter and the second filter are used to perform wavelength filtering on the reflected light and the transmitted light, respectively, to obtain reflected light corresponding to the first wavelength and transmitted light corresponding to the second wavelength; The first detector and the second detector are respectively used to convert the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength into signals to obtain a first electrical signal corresponding to the first wavelength and a second electrical signal corresponding to the second wavelength. The signal processing unit is used to process the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

[0011] According to a third aspect of this disclosure, a food temperature measuring device is provided, applied to an intelligent refrigeration device, the intelligent refrigeration device including an infrared optical temperature measurement module, the device comprising: An intensity information acquisition module is used to acquire wavelength intensity information corresponding to the target food in the intelligent refrigeration device based on the infrared optical temperature measurement module. The wavelength intensity information includes a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. The initial predicted temperature determination module is used to perform temperature back-calculation based on the first radiation intensity and the second radiation intensity using a colorimetric method to obtain the initial predicted temperature. An emissivity determination module is used to determine the target emissivity of the target food based on a preset emissivity table. The target temperature determination module is used to determine the target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature. According to a fourth aspect of this disclosure, an electronic device is provided, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the food temperature measurement method as described in any one of the first aspects by executing the instructions stored in the memory.

[0012] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided that stores at least one instruction or at least one program, said instruction or program being loaded and executed by a processor to implement the food temperature measurement method as described in any of the first aspects.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0014] Implementing this disclosure will have the following beneficial effects: The infrared optical temperature measurement module acquires wavelength intensity information corresponding to the target food in the intelligent refrigeration equipment. This wavelength intensity information includes a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. Based on the first and second radiation intensities, a colorimetric method is used to back-calculate the temperature, yielding an initial predicted temperature. Dual-band infrared data fusion eliminates the influence of emissivity. Wavelength selection and algorithm optimization effectively suppress interference from ambient light and reflections from the inner wall of the intelligent refrigeration equipment, making it suitable for complex storage environments and improving temperature measurement accuracy. The target emissivity of the target food is determined based on a preset emissivity table. The target temperature of the target food is then determined based on the target emissivity and the initial predicted temperature. The initial predicted temperature detected by the infrared optical temperature measurement module is optimized according to the emissivity of different foods to obtain the target temperature, making it suitable for accurate temperature measurement of various foods.

[0015] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 A schematic flowchart of a food temperature measurement method according to an embodiment of the present disclosure is shown; Figure 2 A flowchart illustrating a dynamic emissivity update method according to an embodiment of the present disclosure is shown. Figure 3 A schematic flowchart of a method for determining radiation intensity according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of the structure of an intelligent refrigeration device according to an embodiment of the present disclosure is shown; Figure 5A schematic diagram of the structure of an infrared optical temperature measurement module according to an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of the structure of a food temperature measuring device according to an embodiment of the present disclosure is shown; Figure 7 A block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0018] Figure Labels 100. Infrared optical temperature measurement module; 200. Temperature monitoring module; 300. Pressure sensor; 400. Processor; 110. Beam splitter; 120. First filter; 130. Second filter; 140. First detector; 150. Second detector; Detailed Implementation The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0021] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0022] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0023] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0024] Figure 1 This diagram illustrates a flow chart of a food temperature measurement method according to an embodiment of the present disclosure, as shown below. Figure 1 As shown, the above method is applied to an intelligent refrigeration device, which includes an infrared optical temperature measurement module, comprising: S101. Based on the infrared optical temperature measurement module, obtain the wavelength intensity information corresponding to the target food in the intelligent refrigeration equipment. The wavelength intensity information includes the first radiation intensity corresponding to the first wavelength and the second radiation intensity corresponding to the second wavelength. The intelligent refrigeration device also includes a processor. The processor is the execution subject of this application. The processor collects the first radiation intensity corresponding to the first band and the second radiation intensity corresponding to the second band through the infrared optical temperature measurement module. The infrared optical temperature measurement module can detect the infrared radiation emitted by the target food. After the infrared radiation is split, reflected light and transmitted light are obtained. After the reflected light and transmitted light are filtered and signal processed respectively, the first radiation intensity corresponding to the first wavelength and the second radiation intensity corresponding to the second wavelength are obtained.

[0025] In some embodiments, the intelligent refrigeration device can be a household refrigerator. The infrared optical temperature measurement module includes an infrared detector. The processor includes a digital signal processing (DSP) unit and a field-programmable gate array (FPGA). The first wavelength can be a long-band filtered wavelength, with the core wavelength range of the long-band being 8-14 µm, and the second wavelength can be a mid-band filtered wavelength, with the core wavelength range of the mid-band being 3-5 µm.

[0026] In some embodiments, the intelligent refrigeration device further includes an image acquisition module, wherein the detection axes of the image acquisition module and the infrared optical temperature measurement module are coincident or parallel, so that the image acquisition module and the infrared optical temperature measurement module can synchronously detect the target food, the image acquisition module acquires a target image of the target food, and the type of the target food is determined by the target image.

[0027] S102. Based on the first and second radiation intensities, the initial predicted temperature is obtained by back-calculation using colorimetry. The initial preset temperature is obtained by inversely calculating the temperature using the first and second radiation intensities combined with Planck's law.

[0028] In some embodiments, after preprocessing, the electrical signal is acquired in real time by a data processing unit combining a digital signal processor (DSP) and a field-programmable gate array (FPGA) to obtain a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. The intensity ratio of the first and second radiation intensities is calculated. Based on Planck's law and the principle of dual-wavelength colorimetry, this intensity ratio has a definite relationship with the target temperature. The ratio is converted into a temperature value using the linear formula (1 / T = A·ln(R) + B), where T is the temperature, R is the intensity ratio, and A and B are calibration constants, thus calculating the initial preset temperature.

[0029] In some embodiments, the infrared detector acquires a first wavelength λ1 and a second wavelength λ2. For example, λ1 can be 4.2 μm and λ2 can be 8.5 μm. The infrared optical temperature measurement module also includes a detector and a signal processing unit. The detector performs photoelectric signal conversion processing. The signal processing unit can be located in the processor. The signal processing unit calculates the ratio of the radiation intensity of the two bands in real time and uses Planck's law to invert the initial predicted temperature T, as shown in the following formula:

[0030] Where ε1 and ε2 are the emissivity corresponding to the first wavelength λ1 and the second wavelength λ2, respectively, η1 and η2 are the detector responsivity, and L is the blackbody radiance.

[0031] S103. Determine the target emissivity of the target food based on the preset emissivity table; The preset emissivity table is used to store the emissivity corresponding to different foods.

[0032] In some embodiments, a target image corresponding to the target food is acquired by the image acquisition module, the type of the target food is identified based on the target image, and the target emissivity corresponding to the target food is queried according to the type of the target food and a preset emissivity table.

[0033] S104. Determine the target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature.

[0034] Emissivity is defined as the ratio of the ability of an object in nature to emit infrared radiation to that of an ideal blackbody. The emissivity of an ideal blackbody is defined as 1, so the emissivity of objects in nature ranges from 0 to 1. Stefan Boltzmann's law states that radiant power is directly proportional to emissivity and to the fourth power of temperature. Therefore, by accurately measuring the emissivity of the target food and combining it with an initial predicted temperature, the actual temperature of the target food, i.e., the target temperature, can be calculated more accurately, thereby improving the accuracy and reliability of temperature measurement.

[0035] In some embodiments, the initial predicted temperature and the target temperature have a certain mapping relationship, which can be considered as a linear or non-linear relationship. The ratio of the initial predicted temperature to the target temperature is used as the target emissivity, that is, the target temperature is the ratio of the initial predicted temperature to the target emissivity.

[0036] The above technical solution eliminates the influence of emissivity through dual-band infrared data fusion and effectively suppresses interference from ambient light and reflections from the inner walls of intelligent refrigeration equipment by utilizing wavelength selection and algorithm optimization. It is suitable for complex storage environments and improves temperature measurement accuracy. The initial predicted temperature detected by the infrared optical temperature measurement module is optimized based on the emissivity of different foods to obtain the target temperature, making it suitable for accurate temperature measurement of various foods.

[0037] Please see Figure 2 In some embodiments, the intelligent cooling device further includes a temperature monitoring module, and the method further includes: S201. If the intelligent refrigeration equipment meets the dynamic calibration conditions, the temperature monitoring module obtains multiple actual measured temperatures of the target calibrated food at multiple times during the target time period, and the infrared optical temperature measurement module obtains multiple calibration wavelength intensity information corresponding to the multiple actual measured temperatures of the target calibrated food. S202. Determine multiple initial calibration prediction temperatures corresponding to multiple actual measured temperatures based on multiple calibration wavelength intensity information; S203. Based on multiple initial calibration predicted temperatures and multiple actual measured temperatures, the target emissivity corresponding to the target calibrated food is obtained through fitting. S204. Update the emissivity of the target calibrated food in the preset emissivity table based on the target emissivity.

[0038] The temperature monitoring module is used to measure the actual temperature of the target calibration food. If the intelligent refrigeration equipment meets the dynamic calibration conditions, it is determined that the emissivity of the target calibration food needs to be updated. At this time, the target calibration food and the temperature monitoring module are in contact. The temperature monitoring module measures multiple actual temperatures of the target calibration food at multiple moments during the target time period. The infrared optical thermometry module obtains multiple calibration wavelength intensity information corresponding to the same moment. The multiple calibration wavelength intensity information includes the third radiation intensity corresponding to the first wavelength of the target calibration food and the fourth radiation intensity corresponding to the second wavelength of the target calibration food. Based on the multiple calibration wavelength intensity information and Planck's law, temperature inversion processing is performed to determine multiple initial calibration prediction temperatures corresponding to the multiple actual measured temperatures.

[0039] In some embodiments, the data collected by the infrared optical temperature measurement module needs to be preprocessed. Specifically, this includes: 1. Signal conditioning: amplifying the weak infrared detector signal and matching it to the sampling range of the back-end ADC; 2. Noise suppression: eliminating environmental interference and inherent sensor noise, such as 50Hz power supply noise and electromagnetic pulses; 3. Non-uniformity correction: compensating for response differences between pixels in the detector array and improving image uniformity; 4. Data normalization: unifying multi-channel signals to a standard format, such as normalization, to facilitate subsequent algorithm processing; 5. Defect compensation: statistically analyzing the gradient values ​​of adjacent pixels. If the gradient of a pixel exceeds a threshold, it is determined to be a defective pixel. For example, the threshold is 5 times the standard deviation. The array sensor can use the weighted average of 3x3 neighboring pixels to replace the defective pixel value.

[0040] In some embodiments, each actual measured temperature is taken as the x value in the linear equation, and the initial calibration prediction temperature corresponding to each actual measured temperature is taken as the y value in the linear equation y=kx+b. Multiple actual measured temperatures and multiple initial calibration prediction temperatures are linearly fitted using the least squares method to obtain the fitting result y'=k'x'+b'. The slope k' in the fitting result is taken as the target emissivity corresponding to the target calibration food. The emissivity corresponding to the target calibration food in the preset emissivity table is updated based on the target emissivity to achieve dynamic calibration of emissivity.

[0041] In some embodiments, the target food is a beef chunk, which is placed in a PE film package. A contact sensor at the bottom of the box measures a temperature of 3.2°C. The infrared optical temperature measurement module uses a mid-wave signal that penetrates the PE film to measure a meat surface temperature of 3.5°C. A long-wave signal reflects off the PE film, measuring a packaging surface temperature of 2.8°C. The dynamic model output, after fusing the dual-band ratio with the contact temperature and correcting the emissivity, outputs a meat center temperature of 3.1°C, with an error of <0.1°C compared to the laboratory thermocouple measurement.

[0042] Based on accurate temperature prediction, thawing time can also be predicted. For example, if the target food is frozen dumplings, and these dumplings are moved from a -18℃ freezer to a fresh food compartment, after 20 minutes, the contact sensor (temperature monitoring module) still shows -5℃, indicating a delay in heat conduction. The infrared optical temperature measurement module uses a medium-wave signal to detect the melting area of ​​ice crystals on the dumpling surface, with a temperature of -2℃. A long-wave signal identifies the uncold core, with a temperature of -10℃. Through spatiotemporal data fusion, the predicted thawing time is 45 minutes, with an error of less than 3 minutes compared to the actual thawing time.

[0043] The aforementioned technical solution utilizes the non-contact characteristics of an infrared optical temperature measurement module to obtain global radiation features for dynamic emissivity calibration, combined with contact temperature data from a temperature monitoring module to achieve precise local calibration. This forms a closed loop of "macroscopic modeling + microscopic verification." While single-contact temperature measurement schemes using temperature monitoring modules are limited by physical contact conditions and cannot solve problems related to spatial coverage, dynamic response, and interference from complex materials, the aforementioned solution, through multimodal data fusion and intelligent algorithms, breaks through the application boundaries of traditional temperature measurement technologies. For example, it eliminates interference from packaging materials. When food is wrapped in plastic packaging, such as PE film with an emissivity of 0.92 or aluminum foil with an emissivity of 0.05, traditional single-band infrared cannot distinguish the radiation contributions from the packaging and the food itself, resulting in temperature measurement deviations of up to ±5℃. The aforementioned solution utilizes the difference in material penetration across dual bands; for example, mid-wave (3-5μm) penetrates thin plastic, while long-wave (8-14μm) is reflected by the packaging. Combined with the underlying temperature of the contact sensor, it decouples the radiation signals from the packaging and the food, achieving the effect of "measuring the food through the packaging." Furthermore, to reduce reliance on prior data, existing multispectral temperature measurement solutions require the pre-establishment of a massive emissivity database for food items, such as covering 100 common foods. This results in high maintenance costs and difficulty in covering new food categories. The aforementioned technical solution, however, dynamically updates emissivity parameters through real-time contact calibration data. For newly added unknown foods, only one contact temperature measurement is needed to converge the emissivity estimation error from ±20% to within ±3%. Simultaneously, it adapts to complex storage scenarios. Refrigerators contain multiple stacked layers, transparent containers, and dynamic obstructions, which are difficult to cover with a single infrared or contact sensor. The infrared optical temperature measurement module handles non-contact global scanning, identifying the food's location and approximate category. The contact temperature sensor provides a precise calibration point when the food is stably placed and the pressure sensor is triggered. Through spatiotemporal data fusion, such as training with multiple contact measurement data for a certain area, long-term emissivity characteristics of that location are established. Even if the food is obstructed, the current emissivity can still be predicted using historical models. It dynamically calibrates and updates the emissivity of different foods, enabling accurate temperature measurement of various foods with different emissivity. The contact calibration is automated, avoiding the tedious manual placement of calibrators in traditional methods.

[0044] In some embodiments, determining the target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature includes: The target temperature is obtained by comparing the initial predicted temperature with the target emissivity.

[0045] The initial predicted temperature and the target temperature can have a linear or non-linear relationship. The ratio of the initial predicted temperature to the target temperature is used as the target emissivity, that is, the target temperature is the ratio of the initial predicted temperature to the target emissivity.

[0046] The above technical solution uses the initial predicted temperature as the measurement value obtained by the infrared optical temperature measurement module. For a precise system, the contact temperature obtained by the temperature monitoring module, i.e. the actual measured temperature, is used as the target standard source to calibrate the emissivity. Finally, the precise target temperature is obtained based on the target emissivity and the initial predicted temperature.

[0047] In some embodiments, the intelligent cooling device further includes a pressure sensor, and the method further includes: If the cooling stop time of the intelligent refrigeration equipment is greater than or equal to the first preset time, the door opening time of the intelligent refrigeration equipment is greater than or equal to the second preset time, or the continuous triggering time of the pressure sensor is greater than or equal to the third preset time, the intelligent refrigeration equipment is determined to meet the dynamic calibration conditions.

[0048] A pressure sensor is located at the bottom of the storage compartment of the smart refrigeration device to sense the pressure data of the food. To calibrate the emissivity in the preset emissivity table, the intelligent refrigeration device needs to meet dynamic calibration conditions. These conditions can be any one of the following three: First, the refrigeration stop time of the intelligent refrigeration device is greater than or equal to a first preset time. At this time, the food temperature in the intelligent refrigeration device tends to stabilize, and the emissivity corresponding to the new food temperature needs to be recalibrated. For example, the refrigeration stop time is 30 minutes, which is the time the compressor stops refrigerating. Second, the door opening time of the intelligent refrigeration device is greater than or equal to a second preset time. At this time, ambient light interferes with the stored food in the intelligent refrigeration device, requiring recalibration of the emissivity. For example, the door opening time is greater than 30 seconds, which is the continuous opening time of the intelligent refrigeration device. Third, the continuous trigger time of the pressure sensor in the intelligent refrigeration device is greater than or equal to a third preset time. This indicates that after the third preset time has elapsed, the temperature of the new food tends to stabilize, requiring continuous contact calibration. For example, the continuous trigger time of the pressure sensor reaches 1 hour, where the continuous trigger time characterizes the pressure sensor's sensing data increasing and remaining constant.

[0049] In other embodiments, the dynamic calibration conditions also include emissivity calibration every fourth preset time interval. For example, the actual measured temperature is compared with the initial calibration predicted temperature periodically, such as every 10 minutes, and the emissivity parameter is dynamically adjusted to ensure long-term measurement accuracy.

[0050] The above technical solution updates the emissivity of food in real time when the conditions of the intelligent refrigeration equipment change, making the system more stable, avoiding interference factors such as light interference and temperature changes, and ensuring the accuracy of temperature prediction.

[0051] Please see Figure 3 In some embodiments, the infrared optical temperature measurement module includes a beam splitter, a first filter, a second filter, a first detector, a second detector, and a signal processing unit. Based on the infrared optical temperature measurement module, it acquires wavelength intensity information corresponding to the target food in the intelligent refrigeration device, including: S1011. Based on the beam splitter, the radiation signal corresponding to the target object is split into reflected light and transmitted light. S1012. Based on the first filter and the second filter, the reflected light and the transmitted light are subjected to wavelength filtering processing to obtain the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength. S1013. Based on the first detector and the second detector, the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength are respectively converted into signals to obtain the first electrical signal corresponding to the first wavelength and the second electrical signal corresponding to the second wavelength. S1014. The signal processing unit performs signal processing on the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

[0052] The infrared optical temperature measurement module receives infrared radiation from the target food and decomposes it into reflected and transmitted light using a beam splitter. The wavelength range of the reflected light is 3-5 μm, and the wavelength range of the transmitted light is 8-14 μm. The reflected light enters a first filter (which can be a mid-wave filter) to filter the reflected light, resulting in reflected light corresponding to the first wavelength. The transmitted light enters a second filter to filter the transmitted light, resulting in transmitted light corresponding to the second wavelength. The first and second filters are narrowband filters with different center wavelengths. A first detector converts the reflected light corresponding to the first wavelength into a photoelectric signal, resulting in a first electrical signal corresponding to the first wavelength. A second detector converts the transmitted light corresponding to the second wavelength into a photoelectric signal, resulting in a second electrical signal corresponding to the second wavelength. A signal processing unit then converts the first and second electrical signals into a first radiation intensity and a second radiation intensity, respectively. The above technical solution, using a dual-band colorimetric method, controls temperature measurement error within ±0.5℃, while the single-band measurement error is within ±2℃, significantly outperforming the single-band solution. This is especially true for low-emissivity objects, such as metal packaging, where the error is reduced by more than 50%. Through wavelength selection, interference from ambient light and reflections from the refrigerator's inner wall is effectively suppressed, making it suitable for complex storage environments.

[0053] Please see Figure 4 According to a second aspect of this disclosure, an intelligent cooling device is provided, including an infrared optical temperature measurement module 100, a temperature monitoring module 200, a pressure sensor 300, and a processor 400. The infrared optical temperature measurement module 100 is used to detect the wavelength intensity information of food in the intelligent refrigeration equipment. The wavelength intensity information includes the first radiation intensity corresponding to the first wavelength and the second radiation intensity corresponding to the second wavelength. The temperature monitoring module 200 is used to detect the actual measured temperature of the food. Pressure sensor 300 is used to detect the trigger pressure of food; The processor 400 is used to perform the food temperature measurement method described above.

[0054] The infrared optical temperature measurement module 100 can be a dual-band infrared detection module, which can be installed on the top of the crisper compartment to cover the scanning area of ​​the storage box. A temperature monitoring module 200 and a pressure sensor 300 are located at the bottom of the storage box. The temperature monitoring module 200 detects the contact temperature of the food inside the storage box, while the pressure sensor 300 detects the pressure data of the food in contact with it. A flexible circuit board and a main control module are also located at the bottom of the storage box. The flexible circuit board connects the temperature monitoring module 200, the pressure sensor 300, and the main control board to reduce mechanical vibration interference. The main control module includes a processor 400.

[0055] In some embodiments, the intelligent refrigeration device is a household refrigerator. The infrared optical temperature measurement module 100 is installed at the top center of the refrigerator's crisper compartment, covering the entire storage area. For example, the scanning angle can reach 120°. The temperature monitoring module 200 can be a thin-film temperature sensor, such as a Pt100 thin-film sensor. A 3*3 array of thin-film temperature sensors is embedded at a preset spacing at the bottom of the storage box, making the temperature measurement area greater than 80% of the bottom area of ​​the storage box. A pressure sensor 300 is set at each of the four corners of the storage box. When food is placed inside, the pressure sensor 300 detects the contact state and triggers the data fusion process. The thin-film temperature sensor uses flexible packaging technology to ensure close contact with the food surface, with a response time of <0.5 seconds.

[0056] Please see Figure 5In some embodiments, the infrared optical temperature measurement module 100 includes a beam splitter 110, a first filter 120, a second filter 130, a first detector 140, a second detector 150, and a signal processing unit. The beam splitter 110 is used to split the radiation signal corresponding to the food to obtain reflected light and transmitted light. The first filter 120 and the second filter 130 are used to perform wavelength filtering on the reflected light and the transmitted light, respectively, to obtain the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength. The first detector 140 and the second detector 150 are respectively used to convert the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength into signals to obtain a first electrical signal corresponding to the first wavelength and a second electrical signal corresponding to the second wavelength. The signal processing unit is used to process the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

[0057] The signal processing unit is located in the processor 400. The first detector 140 and the second detector 150 respectively send the first electrical signal and the second electrical signal to the signal processing unit of the processor 400 through the signal conditioning circuit.

[0058] In some embodiments, the beam splitter 110 employs dielectric film beam splitting technology, reflecting mid-wavelengths and transmitting long-wavelengths, with a beam splitting efficiency >90%. The filter is a narrowband interference filter with a full width at half maximum (FWHM) <500nm and a cutoff depth >OD3. The first filter can be a narrowband filter with a center wavelength of 4.2μm, and the second filter can be a narrowband filter with a center wavelength of 8.5μm. The first detector 140 is an InSb detector, capable of responding to wavelengths in the 3-5μm range, and the second detector 150 can be a VO2 (vanadium dioxide) detector, capable of responding to wavelengths in the 8-14μm range. The noise-equivalent temperature difference (NETD) between the two detectors is <50mK. The InSb detector is a photovoltaic detector, operating based on the photon effect, and is sensitive to moisture in the 3-5μm band, effectively identifying changes in surface humidity of food, such as fluctuations in radiation intensity caused by moisture evaporation during thawing. The VO2 detector is a vanadium dioxide microbolometer that operates based on the thermal effect. It has a temperature sensitivity of up to 0.1℃ / μV in the 8-14μm band and can detect minute temperature gradients caused by internal heat conduction in food.

[0059] The above technical solution uses a beam splitter to divide the infrared radiation of the target food into two bands for simultaneous acquisition. A contact temperature sensor is integrated at the bottom of the storage box. When food is placed inside, an emissivity calibration process is automatically triggered. The contact measurement value is used to correct the corresponding temperature measured by the infrared optical temperature measurement module, achieving dynamic emissivity correction and making food temperature measurement more accurate.

[0060] Please see Figure 6 According to a third aspect of this disclosure, a food temperature measuring device is provided, applied to an intelligent refrigeration device. The intelligent refrigeration device includes an infrared optical temperature measurement module, and the device includes: The intensity information acquisition module 10 is used to acquire wavelength intensity information corresponding to the target food in the intelligent refrigeration device based on the infrared optical temperature measurement module. The wavelength intensity information includes the first radiation intensity corresponding to the first wavelength and the second radiation intensity corresponding to the second wavelength. The initial predicted temperature determination module 20 is used to perform temperature back-calculation based on the first radiation intensity and the second radiation intensity using a colorimetric method to obtain the initial predicted temperature. The emissivity determination module 30 is used to determine the target emissivity of the target food based on a preset emissivity table. The target temperature determination module 40 is used to determine the target temperature of the target food based on the target emissivity and the initial predicted temperature. In some possible implementations, the intelligent cooling device further includes a temperature monitoring module, and the device also includes: The temperature acquisition module is used to acquire multiple actual measured temperatures of the target calibrated food in the intelligent refrigeration equipment at multiple times during the target time period based on the temperature monitoring module, and to acquire multiple calibration wavelength intensity information corresponding to the multiple actual measured temperatures of the target calibrated food based on the infrared optical temperature measurement module, if the intelligent refrigeration equipment meets the dynamic calibration conditions. The predicted temperature determination module is used to determine multiple initial calibration predicted temperatures corresponding to multiple actual measured temperatures based on multiple calibration wavelength intensity information. The fitting module is used to perform fitting processing based on multiple initial calibration predicted temperatures and multiple actual measured temperatures to obtain the target emissivity corresponding to the target calibrated food. The update module is used to update the emissivity of the target calibrated food in the preset emissivity table based on the target emissivity.

[0061] In some possible implementations, the target temperature determination module 40 includes: The ratio unit is used to ratio the initial predicted temperature and the target emissivity to obtain the target temperature.

[0062] In some possible implementations, the intelligent refrigeration device further includes a pressure sensor, and the device also includes: The dynamic calibration condition judgment module is used to determine if the cooling stop time corresponding to the intelligent refrigeration equipment is greater than or equal to the first preset time, the door opening time corresponding to the intelligent refrigeration equipment is greater than or equal to the second preset time, or the continuous triggering time of the pressure sensor is greater than or equal to the third preset time, and the intelligent refrigeration equipment meets the dynamic calibration conditions.

[0063] In some possible implementations, the infrared optical temperature measurement module includes a beam splitter prism, a first filter, a second filter, a first detector, a second detector, and a signal processing unit; the intensity information acquisition module 10 includes: The beam splitting unit is used to split the radiation signal corresponding to the target object based on the beam splitting prism to obtain reflected light and transmitted light. The filtering unit is used to perform wavelength filtering on the reflected light and the transmitted light based on the first filter and the second filter respectively, so as to obtain the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength. The signal conversion unit is used to convert the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength based on the first detector and the second detector respectively, to obtain a first electrical signal corresponding to the first wavelength and a second electrical signal corresponding to the second wavelength. The signal processing unit is used to process the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

[0064] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0065] This application provides a food temperature measuring device, which can be a terminal or a server. The food temperature measuring device includes a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement the food temperature measuring method provided in the above method embodiments.

[0066] Memory is used to store software programs and modules. The processor executes these stored software programs and modules to perform various functional applications and data processing. Memory can primarily consist of a program storage area and a data storage area. The program storage area stores the operating system, application programs required for functionality, etc.; the data storage area stores data created based on device usage, etc. Furthermore, memory can include high-speed random access memory (RAM) and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory can also include a memory controller to provide the processor with access to the memory.

[0067] The methods and embodiments provided in this application can be executed in electronic devices such as mobile terminals, computer terminals, servers, or similar computing devices. Figure 7 This is a hardware structure block diagram of an electronic device for a food temperature measurement method provided in an embodiment of this application. (See diagram for example.) Figure 7 As shown, the electronic device 900 can vary considerably due to differences in configuration or performance. It may include one or more Central Processing Units (CPUs) 910 (CPUs 910 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 930 for storing data, and one or more storage media 920 (e.g., one or more mass storage devices) for storing application programs 923 or data 922. The memory 930 and storage media 920 may be temporary or persistent storage. The program stored in the storage media 920 may include one or more modules, each module may include a series of instruction operations on the electronic device. Furthermore, the CPU 910 may be configured to communicate with the storage media 920 and execute the series of instruction operations in the storage media 920 on the electronic device 900. Electronic device 900 may also include one or more power supplies 960, one or more wired or wireless network interfaces 950, one or more input / output interfaces 940, and / or one or more operating systems 921, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0068] The input / output interface 940 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 900. In one example, the input / output interface 940 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 940 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0069] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device 900 may also include... Figure 7 The more or fewer components shown, or having the same Figure 7 The different configurations shown.

[0070] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a food temperature measurement method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the food temperature measurement method provided in the above method embodiment.

[0071] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0072] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0073] As can be seen from the embodiments of the food temperature measurement method, apparatus, device, terminal, server, storage medium, or computer program provided in this application, this application obtains wavelength intensity information corresponding to the target food in the intelligent refrigeration equipment based on an infrared optical temperature measurement module. The wavelength intensity information includes a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. Temperature back-calculation is performed using colorimetry based on the first and second radiation intensities to obtain an initial predicted temperature. The influence of emissivity is eliminated through dual-band infrared data fusion. Wavelength selection and algorithm optimization effectively suppress interference from ambient light and reflections from the inner wall of the intelligent refrigeration equipment, making it suitable for complex storage environments and improving temperature measurement accuracy. The target emissivity corresponding to the target food is determined based on a preset emissivity table. The target temperature corresponding to the target food is determined based on the target emissivity and the initial predicted temperature. The initial predicted temperature detected by the infrared optical temperature measurement module is optimized according to the emissivity of different foods to obtain the target temperature, making it suitable for accurate temperature measurement of various foods.

[0074] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0075] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device, equipment, and storage medium embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0076] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing the relevant hardware to implement them. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0077] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A food temperature measurement method, applied to an intelligent refrigeration device, the intelligent refrigeration device including an infrared optical temperature measurement module, characterized in that, The method includes: Based on the infrared optical temperature measurement module, wavelength intensity information corresponding to the target food in the intelligent refrigeration device is obtained, and the wavelength intensity information includes a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. Based on the first radiation intensity and the second radiation intensity, a colorimetric method is used to back-calculate the temperature to obtain the initial predicted temperature. The target emissivity of the target food is determined based on a preset emissivity table. The target temperature corresponding to the target food is determined based on the target emissivity and the initial predicted temperature.

2. The method according to claim 1, characterized in that, The intelligent refrigeration device further includes a temperature monitoring module, and the method further includes: If the intelligent refrigeration device meets the dynamic calibration conditions, the temperature monitoring module obtains multiple actual measured temperatures of the target calibrated food at multiple times during the target time period, and the infrared optical temperature measurement module obtains multiple calibration wavelength intensity information corresponding to the multiple actual measured temperatures of the target calibrated food. Based on the multiple calibration wavelength intensity information, determine multiple initial calibration prediction temperatures corresponding to the multiple actual measured temperatures; The target emissivity of the target calibrated food is obtained by fitting multiple initial calibration predicted temperatures and multiple actual measured temperatures. The emissivity of the target calibrated food in the preset emissivity table is updated based on the target emissivity.

3. The method according to claim 1, characterized in that, Determining the target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature includes: The target temperature is obtained by taking the ratio of the initial predicted temperature and the target emissivity.

4. The method according to claim 2, characterized in that, The intelligent refrigeration device also includes a pressure sensor, and the method further includes: If the cooling stop time corresponding to the intelligent refrigeration device is greater than or equal to the first preset time, the door opening time corresponding to the intelligent refrigeration device is greater than or equal to the second preset time, or the continuous triggering time of the pressure sensor is greater than or equal to the third preset time, it is determined that the intelligent refrigeration device meets the dynamic calibration conditions.

5. The method according to claim 1, characterized in that, The infrared optical temperature measurement module includes a beam splitter, a first filter, a second filter, a first detector, a second detector, and a signal processing unit. The step of acquiring wavelength intensity information corresponding to the target food in the intelligent refrigeration device based on the infrared optical temperature measurement module includes: The radiation signal corresponding to the target object is split by the beam splitter to obtain reflected light and transmitted light. The reflected light and the transmitted light are respectively subjected to wavelength filtering processing based on the first filter and the second filter to obtain the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength; Based on the first detector and the second detector, the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength are respectively converted into signals to obtain a first electrical signal corresponding to the first wavelength and a second electrical signal corresponding to the second wavelength. The signal processing unit performs signal processing on the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

6. An intelligent refrigeration device, characterized in that, Includes an infrared optical temperature measurement module, a temperature monitoring module, a pressure sensor, and a processor; The infrared optical temperature measurement module is used to detect the wavelength intensity information corresponding to the food in the intelligent refrigeration device. The wavelength intensity information includes the first radiation intensity corresponding to the first wavelength and the second radiation intensity corresponding to the second wavelength. The temperature monitoring module is used to detect the actual measured temperature of the food. The pressure sensor is used to detect the trigger pressure of the food; The processor is used to execute the food temperature measurement method as described in any one of claims 1-5.

7. The device according to claim 6, characterized in that, The infrared optical temperature measurement module includes a beam splitter, a first filter, a second filter, a first detector, a second detector, and a signal processing unit; The beam-splitting prism is used to split the radiation signal corresponding to the food to obtain reflected light and transmitted light. The first filter and the second filter are used to perform wavelength filtering on the reflected light and the transmitted light, respectively, to obtain reflected light corresponding to the first wavelength and transmitted light corresponding to the second wavelength; The first detector and the second detector are respectively used to convert the reflected light corresponding to the first wavelength and the transmitted light corresponding to the second wavelength into signals to obtain a first electrical signal corresponding to the first wavelength and a second electrical signal corresponding to the second wavelength. The signal processing unit is used to process the first electrical signal and the second electrical signal respectively to obtain the first radiation intensity and the second radiation intensity.

8. A food temperature measuring device, applied to an intelligent refrigeration equipment, the intelligent refrigeration equipment including an infrared optical temperature measurement module, characterized in that, The device includes: An intensity information acquisition module is used to acquire wavelength intensity information corresponding to the target food in the intelligent refrigeration device based on the infrared optical temperature measurement module. The wavelength intensity information includes a first radiation intensity corresponding to a first wavelength and a second radiation intensity corresponding to a second wavelength. The initial predicted temperature determination module is used to perform temperature back-calculation based on the first radiation intensity and the second radiation intensity using a colorimetric method to obtain the initial predicted temperature. An emissivity determination module is used to determine the target emissivity of the target food based on a preset emissivity table. The target temperature determination module is used to determine the target temperature corresponding to the target food based on the target emissivity and the initial predicted temperature.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the food temperature measurement method as described in any one of claims 1-5.

10. An electronic device, characterized in that, The method includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the food temperature measurement method as described in any one of claims 1-5 by executing the instructions stored in the memory.

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