Intelligent electric oven temperature control adjustment management system and method

Abstract

This invention discloses an intelligent electric oven temperature control management system and method, belonging to the field of temperature control management technology. It addresses the problems of reduced temperature control accuracy and increased risk of overheating. The system uses temperature sensors to detect the temperature of the oven's outer shell and exhaust vent, and selects different temperature control modes based on the detection results. In the first mode, fan sound data is collected to detect the ambient ventilation, and a first control value is generated based on the fan sound. In the second mode, the insulation layer at the wire connections is monitored, and the extent of breakage is determined based on the results. Electrical power is also detected, and a second control value is generated. Control rules are set, and based on the first, second, and third control values, a control signal is generated and the control ratio is calculated. The system combines the temperature of the outer shell and exhaust vent to perform temperature control operations on the electric oven, improving cooling efficiency and effectively reducing the risk of overheating during oven operation.

Description

Technical Field

[0001] This invention relates to the field of temperature control and regulation management technology, and more specifically, to an intelligent electric oven temperature control and regulation management system and method. Background Technology

[0002] Existing electric oven temperature control systems rely on a single temperature sensor and control the heating power through switches or PID to bring the temperature close to the desired value. However, this is easily limited by the sensor location and environmental conditions, resulting in temperature control lag and the potential for localized overheating or undercooling. Furthermore, it cannot monitor the insulation layer status and changes in electrical power in real time, posing risks of overheating and safety.

[0003] The existing technology has the following shortcomings:

[0004] Currently, traditional systems cannot dynamically switch modes based on shell temperature, exhaust port temperature, and ambient ventilation conditions, cannot assess the insulation layer status in real time, and lack multi-parameter comprehensive control capabilities, intelligent temperature control strategies, and precise cooling adjustment mechanisms, resulting in reduced temperature control accuracy and increased risk of equipment overheating. Therefore, an intelligent electric oven temperature control and regulation management system and method are proposed.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent electric oven temperature control management system and method, which solves the problems mentioned in the background art by employing a multi-mode temperature control strategy, a multi-parameter comprehensive control model, and real-time insulation safety monitoring technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for temperature control and management of an intelligent electric oven, comprising the following steps;

[0008] Step S1: The temperature of the outer shell and exhaust port of the electric oven is detected by the temperature sensor. Based on the temperature detection results of the outer shell and exhaust port, the first temperature control mode or the second temperature control mode is selected.

[0009] Step S2: When entering the first temperature control adjustment mode, collect the sound data of the oven fan, process the sound data to generate the first control value, detect the ventilation of the environment where the electric oven is located, and generate the second control value based on the ventilation.

[0010] Step S3: When entering the second temperature control mode, monitor the insulation layer at the wire connection, determine the insulation layer rupture based on the monitoring results, detect the electric power of the electric oven, and generate a third control value by combining the insulation layer rupture and the electric power.

[0011] Step S4: Set the control rules, determine whether to generate a control signal based on the first control value, the second control value and the third control value, and calculate the control ratio. Combine the temperature of the outer shell of the electric oven and the temperature of the exhaust port to perform temperature control operation on the electric oven.

[0012] In a preferred embodiment, in step S1, a detection time is set, and the temperature of the outer shell and the exhaust port of the electric oven is detected within the detection time. The minimum temperature of the outer shell and the exhaust port of the electric oven within the detection time is taken as the detection result of the outer shell temperature and the detection result of the exhaust port temperature, respectively.

[0013] In a preferred embodiment, in step S1, different adjustment analysis modes are selected based on the detection results of the outer casing temperature and the exhaust port temperature. The specific steps are as follows:

[0014] Temperature comparison: Select the minimum value between the measured results of the outer casing temperature and the measured results of the exhaust port temperature as the judgment temperature and pass it into the judgment rule.

[0015] Threshold selection: When the temperature is determined by the detection result of the outer casing temperature, if the determined temperature is lower than the preset outer casing temperature threshold, the first temperature control adjustment mode is entered; when the temperature is determined by the detection result of the exhaust port temperature, if the determined temperature is lower than the preset exhaust port temperature threshold, the second temperature control adjustment mode is entered.

[0016] In a preferred embodiment, in step S2, the sound data of the fan inside the electric oven is the sound intensity of the fan inside the electric oven. When processing the fan sound intensity, multiple detection time points are set, and the sound intensity of the fan inside the electric oven is detected at each detection time point. The detection results of each detection time point are merged into a sound intensity dataset.

[0017] Historical data is used to calculate the oven fan sound intensity benchmark. The historical data consists of the sound intensity test results of the electric oven fan at multiple time points in the past. The median of the sound intensity test results of the electric oven fan at multiple time points in the historical data is taken as the oven fan sound intensity benchmark.

[0018] The minimum value in the sound intensity dataset is selected as the calibration sound intensity, and the ratio of the calibration sound intensity to the fan sound intensity reference is used as the first control value.

[0019] In a preferred embodiment, in step S2, the ventilation condition of the environment where the electric oven is located is the air flow speed of the environment where the electric oven is located.

[0020] The airflow velocity in the environment of the electric oven was measured at each detection time point. The detection results were merged into a wind speed dataset, which was then processed using the percentile method. The specific steps are as follows:

[0021] The data in the wind speed dataset are labeled from back to front according to the sorted order until the number of labels reaches the target filtering number, and then the labeling stops to obtain multiple target data.

[0022] The ratio of the average value in the target data to the preset wind speed benchmark is used as the second protection manifestation value.

[0023] In a preferred embodiment, in step S3, when entering the second temperature control adjustment mode, the insulation layer at the wire connection is detected by an online detection device.

[0024] The insulation layer rupture is determined based on the monitoring results generated by the insulation testing device. The rupture status of the insulation layer includes the percentage of ruptured area and the total rupture length.

[0025] The image of the wire insulation surface is obtained by using infrared thermal imaging information as the total surface area of ​​the insulation layer, and the image of the cracked area is extracted as the cracked area of ​​the insulation layer. The ratio of the cracked area of ​​the insulation layer to the total surface area of ​​the insulation layer is calculated to obtain the cracked area ratio.

[0026] Skeletonization is performed on the fracture recognition image to extract the fractured connected line segments. The length of each fractured line segment is calculated, and the lengths of each fractured line segment are summed to obtain the total fracture length.

[0027] In a preferred embodiment, in step S3, the proportion of fracture area and the sum of fracture lengths are standardized.

[0028] The result obtained by weighting the standardized fracture area ratio and the total fracture length is taken as the insulation layer fracture status.

[0029] By installing current and voltage sensors in each circuit, current and voltage signals are collected in real time, and the instantaneous power of each circuit is calculated by multiplying them with the power factor. The instantaneous power of each circuit is then summed to obtain the electric power of the electric oven.

[0030] After standardizing the electrical power and insulation layer rupture conditions, the third control value is obtained by inputting them into the product model.

[0031] In a preferred embodiment, in step S4, a control rule is set. If a first control value and a second control value are detected, it is considered that the first temperature control mode is entered. The cooling treatment coefficient is obtained by substituting the first control value and the second control value into a polynomial regression calculation.

[0032] The cooling treatment coefficient is multiplied by the preset control coefficient to obtain the control ratio of the first temperature control mode.

[0033] The target temperature value of the outer shell is calculated by multiplying the control ratio of the first temperature control mode with the outer shell temperature of the electric oven.

[0034] The electric oven is temperature-controlled according to the target temperature value of the outer shell, and the outer shell temperature of the electric oven is cooled down to the target temperature value.

[0035] In a preferred embodiment, in step S4, if a third control value is detected, it is considered that the second temperature control mode has been entered. The control ratio of the second temperature control mode is obtained by multiplying the third control value with the preset control coefficient.

[0036] The target exhaust port temperature is calculated by multiplying the control ratio of the second temperature control mode with the outer shell temperature of the electric oven.

[0037] The electric oven is temperature controlled according to the target temperature value of the exhaust port, and the exhaust port temperature of the electric oven is cooled down to the target temperature value of the exhaust port.

[0038] An intelligent electric oven temperature control and management system includes a temperature detection module, a mode selection module, a control analysis module, and a control execution module, with each module connected by electrical signals.

[0039] The functions of each module are as follows:

[0040] The temperature detection module is used to collect the temperature of the outer shell and exhaust port of the electric oven and transmit it to the mode selection module;

[0041] The mode selection module selects the temperature control mode based on the temperature detection results of the outer shell and exhaust port of the electric oven, and sets the acquisition signal to be transmitted to the control and analysis module according to different selection results;

[0042] The control and analysis module selects to collect data on the sound of the oven fan and the ventilation of the surrounding environment, or on the insulation cracking of the electric oven's wire connections and the power consumption, based on the collected signals, and then transmits the collected results to the control and execution module.

[0043] The control execution module generates a first control value based on the sound data of the oven's internal fan, a second control value based on the ventilation conditions of the oven's environment, and a third control value based on the insulation layer's crack condition and electrical power. The module then uses control rules to determine whether to generate a control signal based on the three control values ​​and calculates the control ratio. Finally, it performs temperature control on the oven by combining the temperature of the oven's outer shell and exhaust port.

[0044] The technical effects and advantages of this invention are as follows:

[0045] This invention uses temperature sensors to detect the temperature of the outer shell and exhaust vent of an electric oven. Based on the temperature detection results, different temperature control modes are selected. In the first temperature control mode, the sound data of the fan inside the oven is collected, and the ventilation of the environment around the oven is detected. A first control value is generated by combining the fan sound data. In the second temperature control mode, the insulation layer at the wire connection is monitored, and the insulation layer cracking is judged based on the monitoring results. The power of the electric oven is detected, and a second control value is generated by combining the insulation layer cracking and power. Control rules are set, and a control signal is generated based on the first, second, and third control values. The control ratio is calculated, and the temperature of the electric oven is controlled by combining the temperature of the outer shell and exhaust vent. This achieves more precise cooling control, improves cooling control efficiency, effectively reduces the risk of overheating during the operation of the electric oven, extends the service life of the equipment, and improves the safety of use. Attached Figure Description

[0046] Figure 1 This is a flowchart of a method for temperature control and management of an intelligent electric oven according to the present invention.

[0047] Figure 2 This is a schematic diagram of a module of an intelligent electric oven temperature control and regulation management system according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] This invention uses temperature sensors to detect the temperature of the outer shell and exhaust vent of an electric oven. Based on the temperature detection results, different temperature control modes are selected. In the first temperature control mode, the sound data of the fan inside the oven is collected, and the ventilation of the environment around the oven is detected. A first control value is generated by combining the fan sound data. In the second temperature control mode, the insulation layer at the wire connection is monitored, and the insulation layer cracking is judged based on the monitoring results. The power of the electric oven is detected, and a second control value is generated by combining the insulation layer cracking and the power. Control rules are set, and based on the first, second, and third control values, it is determined whether to generate a control signal and calculate the control ratio. The temperature control operation of the electric oven is performed in combination with the temperature of the outer shell and exhaust vent, thereby achieving more precise cooling control and improving cooling control efficiency.

[0050] Example 1: A method for temperature control and management of an intelligent electric oven, such as... Figures 1 to 2 As shown, it includes the following steps:

[0051] Step S1: The temperature of the outer shell and exhaust port of the electric oven is detected by the temperature sensor. Based on the temperature detection results of the outer shell and exhaust port, the first temperature control mode or the second temperature control mode is selected.

[0052] Step S2: When entering the first temperature control adjustment mode, collect the sound data of the oven fan, process the sound data to generate the first control value, detect the ventilation of the environment where the electric oven is located, and generate the second control value based on the ventilation.

[0053] Step S3: When entering the second temperature control mode, monitor the insulation layer at the wire connection, determine the insulation layer rupture based on the monitoring results, detect the electric power of the electric oven, and generate a third control value by combining the insulation layer rupture and the electric power.

[0054] Step S4: Set the control rules, determine whether to generate a control signal based on the first control value, the second control value and the third control value, and calculate the control ratio. Combine the temperature of the outer shell of the electric oven and the temperature of the exhaust port to perform temperature control operation on the electric oven.

[0055] The specific implementation is as follows:

[0056] In step S1, when the current electric oven is being tested, a testing time is set. During the testing time, the temperature of the outer shell and the exhaust port of the electric oven is detected using a temperature sensor. The minimum temperature of the outer shell and the exhaust port of the electric oven during the testing time is taken as the detection result of the outer shell temperature and the detection result of the exhaust port temperature, respectively.

[0057] Based on the detection results of the outer casing temperature and the exhaust port temperature, different adjustment and analysis methods are selected. The specific steps are as follows:

[0058] Temperature comparison: Select the minimum value between the measured results of the outer casing temperature and the measured results of the exhaust port temperature as the judgment temperature and pass it into the judgment rule.

[0059] Threshold selection: When the determined temperature is the detection result of the casing temperature, it is compared with the casing temperature threshold; when the determined temperature is the detection result of the exhaust port temperature, it is compared with the exhaust port temperature threshold.

[0060] Determining the detection method: When the detected temperature is the casing temperature and is lower than the casing temperature threshold, the system enters the first temperature control mode; when the detected temperature is the exhaust port temperature and is lower than the exhaust port temperature threshold, the system enters the second temperature control mode.

[0061] It should be noted that a temperature sensor is a device used to measure temperature. In this example, the temperature sensor is placed on the outer shell and the port of the electric oven to detect the temperature. The outer shell temperature threshold and the exhaust port temperature threshold are set by those skilled in the art according to the actual situation, and will not be elaborated here.

[0062] In step S2, the sound data of the fan inside the electric oven is the sound intensity of the fan inside the electric oven. When the sound intensity of the fan inside the electric oven is higher, it means that the electric oven needs to dissipate more heat and the electric oven needs to lower the temperature. Conversely, when the sound intensity of the fan inside the electric oven is low, it does not need to dissipate a lot of heat and does not need to be cooled down.

[0063] When processing the fan sound intensity, multiple detection time points are set, and the sound intensity of the fan in the electric oven is detected at each detection time point. The detection results of each detection time point are then merged into a sound intensity dataset.

[0064] Historical data is used to calculate the oven fan sound intensity benchmark. The historical data consists of the sound intensity test results of the electric oven fan at multiple time points in the past. The median of the sound intensity test results of the electric oven fan at multiple time points in the historical data is taken as the oven fan sound intensity benchmark.

[0065] The minimum value in the sound intensity dataset is selected as the calibration sound intensity, and the ratio of the calibration sound intensity to the fan sound intensity reference is used as the first control value.

[0066] It should be explained that the higher the calibration sound intensity or the lower the fan sound intensity reference, the higher the first control value, and the more necessary it is to cool down the electric oven.

[0067] The ventilation conditions of the environment where the electric oven is located are detected by a wind speed sensor. The ventilation conditions of the environment where the electric oven is located are the air flow speed. After the wind speed sensor detects the air flow speed of the environment where the electric oven is located, the detection result is digitally displayed to generate a wind speed value.

[0068] The airflow velocity in the environment of the electric oven was measured at each detection time point. The detection results were merged into a wind speed dataset, which was then processed using the percentile method. The specific steps are as follows:

[0069] Sort the data in the wind speed dataset from largest to smallest. Filter out multiple target data points by percentile ratio. Count the total number of data points in the wind speed dataset and multiply the total number by the percentile ratio to obtain the target selection quantity. Mark the data in the wind speed dataset from back to front according to the sort order until the number of marks reaches the target selection quantity, thus obtaining multiple target data points.

[0070] For example, if there are 10 data points in the wind speed dataset, with a percentile of 50%, then the data in the wind speed dataset are sorted from largest to smallest, and each data point is labeled sequentially from the end to the beginning. Five labeled data points are then selected as the target data.

[0071] The ratio of the average value in the target data to the preset wind speed benchmark is used as the second control value. The faster the air flow speed in the environment where the electric oven is located, the faster the heat dissipation efficiency of the electric oven, the larger the second control value, and the less cooling treatment is needed.

[0072] It should be noted that the wind speed benchmark mentioned above is not unique and can be set according to the actual situation. For example, the wind speed benchmark can be set to 2m / s, etc., which will not be analyzed in detail here.

[0073] In step S3, when entering the second temperature control adjustment mode, the insulation layer at the wire connection is detected by an online detection device;

[0074] It should be noted that the insulation layer at the wire connection is tested using an insulation testing device. The insulation testing device is used to obtain the integrity information of the surface area of ​​the insulation layer, which will not be elaborated here.

[0075] Furthermore, the insulation testing device includes, but is not limited to, resistance testing module, partial discharge sensing module, infrared thermal imaging module, etc. The specific device selection and installation location shall be determined by the personnel of this experiment based on insulation condition assessment and temperature control safety rules, and are not limited here.

[0076] Among them, the insulation layer rupture is determined based on the monitoring results generated by the insulation detection device. The insulation layer rupture includes the percentage of ruptured area and the total rupture length.

[0077] Furthermore, the monitoring results are obtained through multi-channel synchronous data (including resistance distribution, partial discharge signal, infrared thermal imaging information, etc.) collected by the insulation detection device, which are used to comprehensively assess the port insulation layer rupture.

[0078] The logic for obtaining the percentage of broken area is to obtain an image of the wire insulation surface using infrared thermal imaging information as the total surface area of ​​the insulation layer, extract the image of the broken area as the broken area of ​​the insulation layer, and calculate the ratio of the broken area of ​​the insulation layer to the total surface area of ​​the insulation layer to obtain the percentage of broken area.

[0079] To improve recognition accuracy, those skilled in the art may think of using multimodal image acquisition and fusion methods, such as simultaneously using visible light imaging, infrared thermal imaging and laser contour scanning to obtain surface and local information of the wire insulation layer, and combining image preprocessing, denoising, enhancement and deep learning image segmentation algorithms to accurately identify the broken area, etc., which will not be elaborated here.

[0080] The logic for obtaining the total rupture length is to perform skeletonization processing on the rupture recognition image, extract the rupture connected line segments, calculate the length of each rupture line segment, and accumulate the lengths of each rupture line segment to obtain the total rupture length.

[0081] Among them, skeletonization processing is to refine each fracture region in the fracture region image and compress it into a center line with a width of one pixel, while maintaining the topological structure and connectivity of the fracture, so as to accurately calculate the crack length. The specific operation method is common knowledge to those in the art and will not be described in detail here.

[0082] Furthermore, the proportion of fracture area and the sum of fracture lengths are standardized so that the proportion of fracture area and the sum of fracture lengths are under the same dimension and the numerical expression is between 0 and 1.

[0083] It should be noted that the standardization methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization method, or normalization method based on nonlinear mapping function. The application methods of standardization will not be elaborated here.

[0084] The result obtained by weighting the standardized fracture area ratio and the total fracture length is taken as the insulation layer fracture status.

[0085] Specifically, common methods used in weighted calculations include simple weighted average, risk index-based weighted method, or adaptive weight allocation method, which are common knowledge to those skilled in the art. When calculating the insulation layer rupture, weighted calculation is used. The weighting coefficient can be determined based on the importance ratio of the rupture area percentage and the sum of the rupture length, historical fault statistics, or real-time temperature control risk assessment. (Other weighting strategies or parameters can be set by those skilled in the art according to actual applications, and will not be elaborated here.)

[0086] The electric power of an electric oven refers to the rate at which electrical energy is consumed or transmitted through the loads of each circuit during the operation of the electric oven. The acquisition logic is to install current and voltage sensors in each circuit to collect current and voltage signals in real time, and then calculate the instantaneous power of each circuit by multiplying them with the power factor. The instantaneous power of each circuit is then added together to obtain the electric power of the electric oven.

[0087] The power output and insulation layer breakage are standardized so that the power output and insulation layer breakage of the electric oven are kept in the same value range, i.e., between 0 and 1.

[0088] Substituting the standardized electrical power and insulation layer breakage status into the product model, we obtain the third control value, expressed by the following formula:

[0089] ;

[0090] In the formula, This is the third control value. This describes the insulation layer cracking condition after standardization treatment. The power after standardization. For nonlinear response adjustment parameters;

[0091] It should be noted that when the insulation layer is cracked and the power is greater, it indicates that the equipment has a higher temperature risk and poor insulation condition. It is necessary to limit the temperature rise to ensure safe operation. Therefore, the higher the third control value, the more the electric oven needs to be cooled down.

[0092] Furthermore, the change in the third control value directly drives the electric oven temperature control system to dynamically adjust the heating power and fan speed. When the third control value is large, the temperature control system will automatically reduce the heating power or increase the fan speed to accelerate heat dissipation and reduce the oven temperature.

[0093] In step S4, control rules are set. If the first control value and the second control value are detected, it is considered that the first temperature control mode has been entered. The cooling treatment coefficient is obtained by substituting the first control value and the second control value into a polynomial regression calculation. The specific formula is expressed as follows:

[0094] ;

[0095] In the formula, The cooling treatment coefficient, The first control value, This is the second control value. To adjust the parameters, as well as These are the weighting coefficients corresponding to the first and second control values;

[0096] It should be noted that the order and form of the polynomial regression function in this invention can be selected according to the actual application requirements. The specific order and function form are not limited here. Those skilled in the art can determine them based on the actual operating characteristics of the electric oven, which will not be elaborated here.

[0097] The larger the cooling coefficient, the higher the internal temperature of the electric oven and the insufficient heat dissipation. In this case, cooling measures need to be increased, including reducing the heating power, increasing the fan speed, or enhancing airflow, to ensure that the equipment temperature is kept within a safe range.

[0098] The cooling treatment coefficient is multiplied by the preset control coefficient to obtain the control ratio of the first temperature control mode.

[0099] It should be noted that the preset control coefficient was set by the experimenters based on their experience operating the electric oven and the safe temperature threshold, and will not be elaborated here.

[0100] The target temperature value of the outer shell is calculated by multiplying the control ratio of the first temperature control mode with the outer shell temperature of the electric oven.

[0101] The electric oven is temperature controlled according to the target temperature value of the outer shell, and the outer shell temperature of the electric oven is cooled down to the target temperature value of the outer shell.

[0102] Specifically, temperature control operations include adjusting heating power, changing fan speed, and starting auxiliary cooling devices. The specific implementation method of temperature control operations is not limited, but is selected by the experimenters based on the heat dissipation performance of the equipment and the real-time temperature control requirements, and will not be elaborated here.

[0103] If the third control value is detected, it is considered that the second temperature control mode has been entered. The control ratio of the second temperature control mode is calculated by multiplying the third control value with the preset control coefficient.

[0104] The target exhaust port temperature is calculated by multiplying the control ratio of the second temperature control mode with the outer shell temperature of the electric oven.

[0105] The electric oven is temperature controlled according to the target temperature value of the exhaust port, and the exhaust port temperature of the electric oven is cooled down to the target temperature value of the exhaust port.

[0106] It should be noted that cooling measures for the exhaust port temperature of the electric oven include increasing the exhaust fan speed, activating the auxiliary cooling device, and adjusting the exhaust port ventilation direction. The specific cooling measures selected by our researchers are based on the real-time exhaust port temperature data and ambient air flow conditions, and will not be elaborated here.

[0107] Example 2

[0108] Please see Figure 2 An intelligent electric oven temperature control and management system includes a temperature detection module, a mode selection module, a control analysis module, and a control execution module, with each module connected by electrical signals.

[0109] The functions of each module are as follows:

[0110] The temperature detection module is used to collect the temperature of the outer shell and exhaust port of the electric oven and transmit it to the mode selection module;

[0111] The mode selection module selects the temperature control mode based on the temperature detection results of the outer shell and exhaust port of the electric oven, and sets the acquisition signal to be transmitted to the control and analysis module according to different selection results;

[0112] The control and analysis module selects to collect data on the sound of the oven fan and the ventilation of the surrounding environment, or on the insulation cracking of the electric oven's wire connections and the power consumption, based on the collected signals, and then transmits the collected results to the control and execution module.

[0113] The control execution module generates a first control value based on the sound data of the oven's internal fan, a second control value based on the ventilation conditions of the oven's environment, and a third control value based on the insulation layer's crack condition and electrical power. The module then uses control rules to determine whether to generate a control signal based on the three control values ​​and calculates the control ratio. Finally, it performs temperature control on the oven by combining the temperature of the oven's outer shell and exhaust port.

[0114] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0115] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0116] Furthermore, 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.

[0117] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0118] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0119] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for temperature control and management of an intelligent electric oven, characterized in that, Includes the following steps; Step S1: The temperature of the outer shell and exhaust port of the electric oven is detected by the temperature sensor. Based on the temperature detection results of the outer shell and exhaust port, the first temperature control mode or the second temperature control mode is selected. Step S2: When entering the first temperature control adjustment mode, collect the sound data of the oven fan, process the sound data to generate the first control value, detect the ventilation of the environment where the electric oven is located, and generate the second control value based on the ventilation. Step S3: When entering the second temperature control mode, monitor the insulation layer at the wire connection, determine the insulation layer rupture based on the monitoring results, detect the electric power of the electric oven, and generate a third control value by combining the insulation layer rupture and the electric power. Step S4: Set the control rules, determine whether to generate a control signal based on the first control value, the second control value and the third control value, and calculate the control ratio. Combine the temperature of the outer shell of the electric oven and the temperature of the exhaust port to perform temperature control operation on the electric oven.

2. The intelligent electric oven temperature control and management method according to claim 1, characterized in that: In step S1, a detection time is set, and the temperature of the outer shell and the exhaust port of the electric oven is detected within the detection time. The minimum temperature of the outer shell and the exhaust port of the electric oven within the detection time is taken as the detection result of the outer shell temperature and the detection result of the exhaust port temperature, respectively.

3. The intelligent electric oven temperature control and management method according to claim 2, characterized in that: In step S1, based on the detection results of the outer casing temperature and the exhaust port temperature, different adjustment analysis modes are selected. The specific steps are as follows: Temperature comparison: Select the minimum value between the measured results of the outer casing temperature and the measured results of the exhaust port temperature as the judgment temperature and pass it into the judgment rule. Threshold selection: When the temperature is determined by the detection result of the outer casing temperature, if the determined temperature is lower than the preset outer casing temperature threshold, the first temperature control adjustment mode is entered; when the temperature is determined by the detection result of the exhaust port temperature, if the determined temperature is lower than the preset exhaust port temperature threshold, the second temperature control adjustment mode is entered.

4. The intelligent electric oven temperature control and management method according to claim 1, characterized in that: In step S2, the sound data of the fan inside the electric oven is the sound intensity of the fan inside the electric oven. When processing the fan sound intensity, multiple detection time points are set, and the sound intensity of the fan inside the electric oven is detected at each detection time point. The detection results of each detection time point are merged into a sound intensity dataset. Historical data is used to calculate the oven fan sound intensity benchmark. The historical data consists of the sound intensity test results of the electric oven fan at multiple time points in the past. The median of the sound intensity test results of the electric oven fan at multiple time points in the historical data is taken as the oven fan sound intensity benchmark. The minimum value in the sound intensity dataset is selected as the calibration sound intensity, and the ratio of the calibration sound intensity to the fan sound intensity reference is used as the first control value.

5. The intelligent electric oven temperature control and management method according to claim 1, characterized in that: In step S2, the ventilation condition of the environment where the electric oven is located is the air flow speed of the environment where the electric oven is located; The airflow velocity in the environment of the electric oven was measured at each detection time point. The detection results were merged into a wind speed dataset, which was then processed using the percentile method. The specific steps are as follows: The data in the wind speed dataset are labeled from back to front according to the sorted order until the number of labels reaches the target filtering number, and then the labeling stops to obtain multiple target data. The ratio of the average value in the target data to the preset wind speed benchmark is used as the second control value.

6. The intelligent electric oven temperature control and management method according to claim 1, characterized in that: In step S3, when entering the second temperature control adjustment mode, the insulation layer at the wire connection is detected by an online detection device; The insulation layer rupture is determined based on the monitoring results generated by the insulation testing device. The rupture status of the insulation layer includes the percentage of ruptured area and the total rupture length. The image of the wire insulation surface is obtained by using infrared thermal imaging information as the total surface area of ​​the insulation layer, and the image of the cracked area is extracted as the cracked area of ​​the insulation layer. The ratio of the cracked area of ​​the insulation layer to the total surface area of ​​the insulation layer is calculated to obtain the cracked area ratio. Skeletonization is performed on the fracture recognition image to extract the fractured connected line segments. The length of each fractured line segment is calculated, and the lengths of each fractured line segment are summed to obtain the total fracture length.

7. The intelligent electric oven temperature control and management method according to claim 6, characterized in that: In step S3, the proportion of fracture area and the sum of fracture lengths are standardized. The result obtained by weighting the standardized fracture area ratio and the total fracture length is taken as the insulation layer fracture status. By installing current and voltage sensors in each circuit, current and voltage signals are collected in real time, and the instantaneous power of each circuit is calculated by multiplying them with the power factor. The instantaneous power of each circuit is then summed to obtain the electric power of the electric oven. After standardizing the electrical power and insulation layer rupture conditions, the third control value is obtained by inputting them into the product model.

8. The intelligent electric oven temperature control and management method according to claim 1, characterized in that: In step S4, control rules are set. If the first control value and the second control value are detected, it is considered that the first temperature control mode is entered. The cooling treatment coefficient is obtained by substituting the first control value and the second control value into a polynomial regression calculation. The cooling treatment coefficient is multiplied by the preset control coefficient to obtain the control ratio of the first temperature control mode. The target temperature value of the outer shell is calculated by multiplying the control ratio of the first temperature control mode with the outer shell temperature of the electric oven. The electric oven is temperature-controlled according to the target temperature value of the outer shell, and the outer shell temperature of the electric oven is cooled down to the target temperature value.

9. The intelligent electric oven temperature control and management method according to claim 8, characterized in that: In step S4, if a third control value is detected, it is considered that the second temperature control mode has been entered. The control ratio of the second temperature control mode is obtained by multiplying the third control value with the preset control coefficient. The target exhaust port temperature is calculated by multiplying the control ratio of the second temperature control mode with the outer shell temperature of the electric oven. The electric oven is temperature controlled according to the target temperature value of the exhaust port, and the exhaust port temperature of the electric oven is cooled down to the target temperature value of the exhaust port.

10. An intelligent electric oven temperature control and management system, based on the intelligent electric oven temperature control and management method according to any one of claims 1-9, characterized in that, It includes a temperature detection module, a mode selection module, a control analysis module, and a control execution module, with each module connected by electrical signals; The functions of each module are as follows: The temperature detection module is used to collect the temperature of the outer shell and exhaust port of the electric oven and transmit it to the mode selection module; The mode selection module selects the temperature control mode based on the temperature detection results of the outer shell and exhaust port of the electric oven, and sets the acquisition signal to be transmitted to the control and analysis module according to different selection results; The control and analysis module selects to collect data on the sound of the oven fan and the ventilation of the surrounding environment, or on the insulation cracking of the electric oven's wire connections and the power consumption, based on the collected signals, and then transmits the collected results to the control and execution module. The control execution module generates a first control value based on the sound data of the oven's internal fan, a second control value based on the ventilation conditions of the oven's environment, and a third control value based on the insulation layer's crack condition and electrical power. The module then uses control rules to determine whether to generate a control signal based on the three control values ​​and calculates the control ratio. Finally, it performs temperature control on the oven by combining the temperature of the oven's outer shell and exhaust port.

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