System and method for measuring and analyzing electric quantity of energy-saving oven in real time based on artificial intelligence

Abstract

The invention belongs to the technical field of oven electric quantity measurement and analysis, and particularly discloses an energy-saving oven electric quantity real-time measurement and analysis system and method based on artificial intelligence, and the system comprises an operation stage judgment module, an electric quantity abnormity analysis module, an oven abnormity analysis module, an abnormal factor judgment module, and a grading early warning feedback terminal. Through multi-dimensional fusion analysis of the equipment state abnormality, the operation abnormality and the external environment abnormality, the limitation of traditional single-dimensional diagnosis is broken through, equipment faults, operation errors and environment interference are accurately distinguished, the accuracy of abnormality positioning is remarkably improved, the misjudgment risk of manual troubleshooting is further reduced, and the fault diagnosis efficiency is improved. And meanwhile, the corresponding standard electricity consumption interval is matched according to the energy consumption characteristics of the drying oven in each operation stage to carry out electricity consumption abnormity judgment, so that accurate management of staged energy consumption is realized, energy waste caused by stage division errors is avoided, and the pertinence of electricity consumption abnormity judgment is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oven power measurement and analysis, and relates to an artificial intelligence-based real-time measurement and analysis system and method for energy-saving oven power. Background Art

[0002] In industrial production, ovens are core heat treatment equipment. Their energy consumption management and operational status monitoring are directly related to production efficiency, energy costs, and equipment reliability. With the development of intelligent manufacturing technology, the limitations of traditional monitoring methods in dynamic analysis and anomaly diagnosis are becoming increasingly prominent, necessitating real-time measurement and analysis of oven power consumption.

[0003] Currently, traditional oven power monitoring can only achieve basic energy consumption measurement and has the following shortcomings: 1. It currently relies on fixed time nodes to divide the oven operation stages, and lacks dynamic analysis of the real-time temperature change rate, resulting in delayed judgment of the actual operation stage of the equipment and significant errors, making it difficult to dynamically adapt to the energy consumption requirements of the production process, thereby causing energy waste and failing to accurately match the power consumption benchmarks of different stages.

[0004] 2. When the current power consumption is abnormal, it is impossible to conduct correlation analysis based on multi-dimensional data such as equipment status, operating behavior, and external environment. Abnormality investigation mainly relies on manual experience, resulting in low investigation efficiency, making it difficult to achieve refined control of energy consumption throughout the entire cycle. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, an energy-saving oven power real-time measurement and analysis system and method based on artificial intelligence is proposed.

[0006] The objectives of the present invention can be achieved through the following technical solutions: The present invention provides an artificial intelligence-based real-time measurement and analysis system for energy-saving oven power, the system comprising: an operation stage determination module, which analyzes the rate of change of the temperature of the oven at each monitoring time point within a preset time period to determine the oven operation stage.

[0007] The power anomaly analysis module compares the power consumption of the oven with the standard power consumption range corresponding to its operating stage to determine whether the power consumption of the oven is abnormal.

[0008] The oven abnormality analysis module collects the equipment abnormality data and operation data of the oven during the abnormal time period when the power consumption of the oven is abnormal. Based on the comparison and analysis of the equipment abnormality data and its preset threshold, the equipment status abnormality degree of the oven is obtained, and the operation data is integrated and analyzed to obtain the operation abnormality degree of the oven.

[0009] The abnormal factor determination module analyzes the deviation of the external temperature and external humidity of the oven during the abnormal time period from their preset values to obtain the abnormality of the external environment of the oven. At the same time, it combines the abnormality of the equipment status and the abnormality of the operation of the oven to determine the abnormal factors of the oven.

[0010] The graded warning feedback terminal triggers graded warnings based on abnormal factors of the oven and provides corresponding feedback.

[0011] The present invention also provides an artificial intelligence-based real-time measurement and analysis method for energy-saving oven power consumption, the specific steps of which are as follows: S1, analyzing the change rate of the oven temperature at each monitoring time point within a preset time period to determine the oven operation stage.

[0012] S2. Compare the power consumption of the oven with the standard power consumption range corresponding to its operation stage to determine whether the power consumption of the oven is abnormal.

[0013] S3. When the power consumption of the oven is abnormal, the device abnormality data and operation data of the oven during the abnormal time period are collected, and the device abnormality data and its preset threshold are compared and analyzed to obtain the device status abnormality degree of the oven, and the operation data are fused and analyzed to obtain the operation abnormality degree of the oven.

[0014] S4. Analyze the deviation of the external temperature and external humidity of the oven during the abnormal time period from their preset values to obtain the abnormality of the external environment of the oven. Combined with the abnormality of the equipment status and the abnormality of the operation of the oven, determine the abnormal factors of the oven.

[0015] S5. Trigger graded warnings based on abnormal factors of the oven and provide corresponding feedback.

[0016] Compared with the existing technology, the beneficial effects of the present invention are as follows: (1) The present invention breaks through the limitations of traditional single-dimensional diagnosis through multi-dimensional fusion analysis of equipment status abnormality, operation abnormality and external environment abnormality, accurately distinguishes equipment failure, operation error and environmental interference, significantly improves the accuracy of abnormality positioning, and reduces the cost of manual investigation and the risk of misjudgment.

[0017] (2) The present invention determines abnormal power consumption by matching the energy consumption characteristics of the oven in each operating stage with the corresponding standard power consumption interval, thereby achieving precise management of energy consumption in different stages, effectively avoiding energy waste caused by stage division errors, and thus improving the pertinence of abnormal power consumption determination and energy consumption control efficiency.

[0018] (3) The present invention accurately identifies hidden faults in equipment operation through quantitative analysis of the abnormality of heating element power and the abnormality of the rotating speed, avoiding the risk of misjudgment caused by the exceeding of a single parameter, and at the same time improving the stability and energy efficiency of equipment operation, thereby extending the service life of the equipment.

[0019] (4) The present invention achieves a quantitative evaluation of human operation behavior by extracting the number of non-compliant door openings, duration and corresponding operation stages during the abnormal time period, and combining the weighted calculation of the operation abnormality degree with the abnormal influencing factors of each stage. It implements differentiated abnormality analysis for different operation stages, and effectively reduces energy consumption abnormalities and equipment losses caused by operational negligence.

[0020] (5) The present invention monitors the deviation of ambient temperature and humidity and integrates the calculation of external environmental anomalies to dynamically identify the impact of environmental fluctuations on energy consumption, thereby making up for the current defect of only focusing on the internal status of the equipment and improving the adaptability and energy efficiency stability of the oven in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention.

[0023] Figure 2 Schematic diagram of the connection steps for analyzing the abnormality of the equipment status of the present invention.

[0024] Figure 3 Schematic diagram of the connection of each step of the method of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1 As shown, the present invention provides an artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption, which includes: an operation stage determination module, an electricity consumption abnormality analysis module, an oven abnormality analysis module, an abnormal factor determination module and a graded early warning feedback terminal.

[0027] In the above, the power abnormality analysis module is connected to the operation stage determination module and the oven abnormality analysis module respectively, and the abnormal factor determination module is also connected to the oven abnormality analysis module and the graded warning feedback terminal respectively.

[0028] The operation stage determination module analyzes the rate of change of the temperature of the oven at each monitoring time point within a preset time period to determine the operation stage of the oven.

[0029] It should be added that the preset time period is a specific time window for analyzing the operation stage of the oven, and the time period must cover at least one complete operation stage, such as the heating stage from the start of heating to the end of heating.

[0030] It should be added that the temperature of the oven at each monitoring time point within the preset time period is monitored by a temperature sensor.

[0031] Exemplarily, the method for determining the oven operation stage includes: R1, combining each monitoring time point with its adjacent next monitoring time point in pairs to obtain each monitoring time group, performing deviation analysis on the temperature within each monitoring time group, and obtaining the temperature change rate of each monitoring time group.

[0032] Furthermore, the analysis of the temperature change rate of each monitoring time group includes: R1-1, taking the time interval between adjacent monitoring points in the monitoring time group as the interval duration.

[0033] R1-2. Subtract the temperature between the next monitoring time point and the previous monitoring time point in the monitoring time group to obtain the temperature difference of each monitoring time group.

[0034] R1-3. The ratio of the temperature difference to the interval time is used as the temperature change rate of each monitoring time group.

[0035] R2. Compare the temperature change rate of each monitoring time group with the preset temperature change range.

[0036] R3. Count the number of monitoring groups whose temperature change rate is less than the lower limit of the preset interval, within the preset interval, and greater than the upper limit of the preset interval, and record them as the number of cooling trend monitoring groups, the number of constant temperature steady-state monitoring groups, and the number of warming trend monitoring groups respectively.

[0037] R4. Compare the number of cooling trend monitoring groups, the number of constant temperature steady-state monitoring groups, and the number of warming trend monitoring groups.

[0038] R5. If the number of cooling trend monitoring groups is the maximum value, the oven is judged to be in the cooling stage. If the number of constant temperature steady-state monitoring groups is the maximum value, the oven is judged to be in the constant temperature stage. If the number of heating trend monitoring groups is the maximum value, the oven is judged to be in the heating stage.

[0039] The power anomaly analysis module compares the power consumption of the oven with the standard power consumption range corresponding to its operation stage to determine whether the power consumption of the oven is abnormal.

[0040] It should be added that the power consumption of the oven is monitored by a power sensor connected to the main power supply circuit of the oven.

[0041] Exemplarily, determining whether the power consumption of the oven is abnormal includes: matching the operation stage of the oven with the standard power consumption interval corresponding to each operation stage to obtain the standard power consumption interval of the oven.

[0042] The power consumption of the oven is compared with the standard power consumption range. If the power consumption of the oven is within the standard power consumption range, it is determined that the power consumption of the oven is normal. Otherwise, it is determined that the power consumption of the oven is abnormal.

[0043] The embodiment of the present invention determines abnormal power consumption by matching the energy consumption characteristics of the oven in each operating stage with the corresponding standard power consumption interval, thereby achieving precise management of energy consumption in different stages, effectively avoiding energy waste caused by stage division errors, and thereby improving the targetedness of abnormal power consumption determination and the efficiency of energy consumption control.

[0044] The oven abnormality analysis module collects equipment abnormality data and operating data of the oven during the abnormal time period when the power consumption of the oven is abnormal, compares and analyzes the equipment abnormality data with a preset threshold value to obtain the equipment status abnormality degree of the oven, and fuses and analyzes the operating data to obtain the operating abnormality degree of the oven.

[0045] It should be noted that the abnormal time period refers to the interval between the start of the operation phase and the abnormal monitoring time point corresponding to the abnormal monitoring time point when abnormal power consumption is determined. This abnormal time period covers the entire process from the start of the operation phase to the detection of the abnormality, allowing for multi-dimensional abnormality analysis based on the full amount of data within the operation phase.

[0046] It should be added that the equipment abnormality data includes: the power value of the heating element at each abnormal monitoring time point within the abnormal time period and the rotational speed of the converter at each abnormal monitoring time point within the abnormal time period. The power value is obtained by monitoring the power sensor connected to the power supply circuit of the heating element, and the rotational speed is obtained by monitoring the rotational speed sensor installed on the transmission shaft or rotating part of the converter.

[0047] It should be noted that the operational data includes the number of non-compliant door openings during the abnormal time period, the duration of each non-compliant door opening, and the corresponding operational phase. The non-compliant door opening count is determined by installing a proximity sensor switch or travel switch at the connection between the sealed door and the oven body to monitor the door's opening and closing status. When the door is opened and not closed within the compliant operating time allowed by the system, it is determined to be a non-compliant door opening, triggering a timing module to record the door opening duration, accumulate the number of non-compliant door openings, and simultaneously record the duration of each non-compliant door opening. The operational phase corresponding to each non-compliant door opening is obtained by associating the operational phase corresponding to each non-compliant door opening with the operational phase judgment result.

[0048] See also Figure 2 As shown, exemplarily, the analysis of the abnormality of the equipment status of the oven includes: W1, extracting the power value of the heating element at each abnormal monitoring time point within the abnormal time period from the equipment abnormality data, and constructing a power change curve of the heating element with the abnormal monitoring time point as the horizontal axis and the power value as the vertical axis.

[0049] W2. Match the operating stage of the oven with the standard power range corresponding to each operating stage to obtain the standard power range of the oven, and then mark the upper critical line and the lower critical line on the power change curve based on the upper limit value and the lower limit value of the standard power range.

[0050] It should be added that the standard power range corresponding to each operating stage is the normal fluctuation range of the heating element power pre-set according to the process requirements of the oven in different operating stages, which is used to determine whether the actual power of the heating element is in a reasonable state.

[0051] In the heating stage, in order to quickly increase the temperature, the heating element needs to operate at full load or high power. The standard power range is usually 80%-100% of the rated power. In the constant temperature stage, a stable temperature needs to be maintained. The heating element power is usually in a low load or intermittent working state. The standard power range is usually 30%-60% of the rated power to balance the temperature maintenance and energy consumption control requirements. In the cooling stage, the heating element usually stops working or only maintains extremely low power to assist in heat dissipation. The standard power range is usually 0%-10% of the rated power.

[0052] W3. Extract the curve length exceeding the upper critical line and below the lower critical line and the total length of the curve from the power variation curve, and then use the ratio of the two as the power abnormality of the heating element in the oven.

[0053] W4. Analyze the deviation between the rotation speed of the equipment abnormal data transfer machine at each abnormal monitoring time point within the abnormal time period and the preset rotation speed to obtain the rotation speed abnormality degree of the oven transfer machine.

[0054] Furthermore, the analysis of the abnormality of the rotation speed of the transfer machine in the oven includes: W4-1, performing relative deviation analysis on the rotation speed at each abnormal monitoring time point and the preset rotation speed to obtain the rotation speed deviation rate of the transfer machine at each abnormal monitoring time point.

[0055] It should be added that the calculation formula of the speed deviation rate is: , where is the speed deviation rate, is the rotation speed, is the preset speed.

[0056] It should be added that the preset speed refers to the theoretical speed value set for the machine under normal operating conditions.

[0057] W4-2. Match the operation stage of the oven with the rotation speed deviation rate threshold corresponding to each operation stage to obtain the rotation speed deviation rate threshold of the oven.

[0058] It's important to note that the speed deviation threshold refers to the maximum relative deviation between the oven's rotational speed and the preset speed during different operating stages. The speed deviation threshold is most stringent during the constant temperature stage, as high-precision temperature control is required. Speed fluctuations directly impact hot air uniformity and drying quality, requiring minimal deviation. The thresholds for the cooling and heating stages are more relaxed, allowing for some fluctuation.

[0059] W4-3. Compare the speed deviation rate of the transfer machine at each abnormal monitoring time point with the speed deviation rate threshold, count the number of abnormal monitoring time points where the speed deviation rate is greater than the speed deviation rate threshold and the total number of abnormal monitoring time points, and then use the ratio of the two as the speed abnormality degree of the transfer machine in the oven.

[0060] W5. Perform weighted fusion calculation on the power abnormality of the heating element in the oven and the rotation speed abnormality of the rotating machine to obtain the equipment status abnormality of the oven.

[0061] It should be added that the calculation formula for the abnormality of the equipment status is: , where is the abnormality of the equipment status, and They are the abnormality of heating element power and the abnormality of rotating speed. and are the weights of the abnormality of heating element power and the abnormality of rotating speed, , .

[0062] It should be added that the heating element is the core energy-consuming component of the oven to achieve the drying function. Its power output directly determines the efficiency of heat supply. Abnormal power will significantly affect the drying effect and energy consumption level. For example, insufficient power may lead to prolonged drying time and increased power waste. The converter is mainly responsible for heat circulation, and its abnormal speed usually has an indirect impact on energy consumption. For example, if the speed is too low, it will lead to uneven heat distribution and require extended heating time. Therefore, the power abnormality of the heating element has a more direct and critical impact on the overall status and energy efficiency of the equipment, and should be given a higher weight. Therefore, it is set , in order to facilitate analysis, The specific value can be 0.6. The specific value can be 0.4.

[0063] The embodiment of the present invention accurately identifies hidden faults in equipment operation through quantitative analysis of heating element power abnormalities and rotor speed abnormalities, avoiding the risk of misjudgment due to exceeding a single parameter limit, while improving the stability and energy efficiency of equipment operation, thereby extending the service life of the equipment.

[0064] Exemplarily, the analyzing the degree of abnormal operation of the oven includes: extracting the number of non-compliant door openings of the oven within the abnormal time period, the door opening duration of each non-compliant door opening, and the corresponding operation stage from the operation data.

[0065] The door opening time of each non-compliant door opening is summed to obtain the total door opening time of the non-compliant door opening, and the ratio of the door opening time to the total door opening time is used as the door opening time ratio of each non-compliant door opening.

[0066] The operation stage corresponding to each non-compliant door opening is matched with the abnormal impact factor corresponding to each operation stage to obtain the abnormal impact factor of each non-compliant door opening, and then the abnormal impact factor is summed with the product calculation result of the door opening time ratio to obtain the operation abnormality degree.

[0067] It should be noted that the abnormal impact factor corresponding to each operating stage refers to a pre-set quantitative parameter that reflects the degree of impact of non-compliant door opening operations on energy consumption or equipment operating status based on the process characteristics of the oven at different operating stages. During the heating stage, the temperature needs to be increased rapidly, and non-compliant door opening will cause a large amount of heat loss. The abnormal impact factor can be set to 0.9. During the constant temperature stage, a stable temperature needs to be maintained. Opening the door will interfere with temperature uniformity. The abnormal impact factor can be set to 0.5. During the cooling stage, the sensitivity to temperature fluctuations is relatively low, and the abnormal impact factor can be set to 0.2. The abnormal impact factor is used to quantify the difference in the impact of door opening operations on oven operation during different operating stages.

[0068] The embodiment of the present invention achieves a quantitative evaluation of human operating behavior by extracting the number of non-compliant door openings, duration and corresponding operating stages within the abnormal time period, and combining the abnormal influencing factors of each stage to weightedly calculate the operating operation abnormality. It implements differentiated abnormality analysis for different operating stages, and effectively reduces energy consumption abnormalities and equipment losses caused by operational negligence.

[0069] The abnormal factor determination module analyzes the deviation of the external temperature and external humidity of the oven during the abnormal time period from their preset values to obtain the abnormality of the external environment of the oven. At the same time, the abnormality of the oven is determined based on the abnormality of the equipment status and the abnormality of the operation.

[0070] It should be added that the external temperature and external humidity are respectively monitored by a temperature sensor and a humidity sensor arranged outside the oven.

[0071] It should be added that the preset values refer to the preset external temperature and external humidity, which are reference values pre-set according to the standard environmental conditions of the oven, and are used to measure whether the actual external environment meets the environmental requirements for normal operation of the equipment.

[0072] The preset external temperature is a baseline value set based on the environmental adaptability parameters of the oven design. For example, the preset external temperature for normal operation of an oven is 20°C. The preset external humidity is a baseline value set based on the corrosion protection requirements of the oven equipment. For example, the preset external humidity is 50% RH.

[0073] Exemplarily, the analysis of the abnormality of the external environment of the oven includes: performing relative deviation analysis on the external temperature and external humidity of the oven at each abnormal monitoring time point within the abnormal time period with the corresponding preset values, to obtain the external temperature deviation and external humidity deviation of the oven at each abnormal monitoring time point.

[0074] It should be added that the analytical formula for the external temperature deviation is: , where is the external temperature deviation, is the external temperature, For the preset external temperature, the analysis formula of the external humidity deviation is: , where is the external humidity deviation, is the external humidity, The default external humidity.

[0075] The external temperature deviation and the external humidity deviation are weighted and fused to obtain the external environment anomaly of the oven at each abnormal monitoring time point.

[0076] It should be added that the calculation formula for the external environment abnormality is: , where is the degree of abnormality of the external environment, and are the weights of external temperature deviation and external humidity deviation, , .

[0077] It should be added that the external temperature deviation will directly interfere with the internal temperature control logic of the oven. For example, if the external temperature is too low, the energy consumption in the heating stage may increase. The external humidity deviation mainly affects the material drying efficiency and has no direct effect on the division of the operation stage. Therefore, the external temperature deviation contributes more directly to the external environment abnormality and should have a higher weight. Therefore, set , in order to facilitate analysis, The specific value can be 0.6. The specific value can be 0.4.

[0078] The average value of the abnormality degree of the external environment of the oven at each abnormality monitoring time point is calculated as the abnormality degree of the external environment of the oven.

[0079] The embodiment of the present invention monitors the deviation of ambient temperature and humidity and integrates the calculation of external environmental anomalies to dynamically identify the impact of environmental fluctuations on energy consumption, making up for the current defect of only focusing on the internal status of the equipment, and improving the adaptability and energy efficiency stability of the oven in complex environments.

[0080] Exemplarily, determining the abnormal factors of the oven includes comparing the abnormality of the oven's equipment state, the abnormality of its operation, and the abnormality of its external environment with their preset abnormality thresholds respectively.

[0081] If the abnormality of the device state of the oven is greater than a preset abnormality threshold of the device state, the abnormality of the device state is regarded as an abnormal factor of the oven.

[0082] If the degree of abnormal operation of the oven is greater than a preset threshold value of abnormal operation, the abnormal operation is regarded as an abnormal factor of the oven.

[0083] If the degree of abnormality of the external environment of the oven is greater than a preset threshold value of abnormality of the external environment, the abnormality of the external environment is regarded as an abnormal factor of the oven.

[0084] It should be added that the preset equipment status abnormality threshold, operation abnormality threshold and external environment abnormality threshold are all obtained by performing gradient descent optimization analysis based on oven historical data.

[0085] It should be noted that as a precision thermal equipment, the performance degradation of the oven's core components will directly affect energy efficiency. For example, the power drop caused by the aging of the heating element can increase energy consumption. Traditional monitoring only focuses on the current value and cannot identify this type of progressive aging problem in advance. Abnormal opening and closing of doors by operators is a major cause of abnormal energy consumption. Changes in temperature and humidity in the external environment can significantly affect heat transfer efficiency. Therefore, when the oven's power consumption is abnormal, a multi-dimensional analysis of the equipment status, operation, and external environment can accurately trace the abnormal power consumption factors.

[0086] The embodiments of the present invention break through the limitations of traditional single-dimensional diagnosis through multi-dimensional fusion analysis of equipment status abnormality, operation abnormality and external environment abnormality, accurately distinguish between equipment failure, operational errors and environmental interference, significantly improve the accuracy of abnormality positioning, and reduce the cost of manual investigation and the risk of misjudgment.

[0087] The graded warning feedback terminal triggers graded warnings based on abnormal factors of the oven and provides corresponding feedback.

[0088] It should be added that the graded warning includes: when the abnormal factor of the oven is an abnormal equipment status, a first-level warning is triggered. At this time, the warning light on the oven body continues to flash red at a high frequency, indicating that the equipment is at risk of serious failure and needs to be shut down immediately for maintenance.

[0089] When the abnormal factor of the oven is abnormal operation, the second-level warning is triggered. At this time, the warning light on the oven body flashes regularly in yellow at a medium frequency, prompting the operator to adjust the operating parameters to avoid potential risks.

[0090] When the abnormal factor of the oven is an abnormal external environment, a third-level warning is triggered. At this time, the warning light on the oven body flashes slowly in blue at a low frequency, prompting the user to pay attention to the impact of the external environment on the operation of the equipment.

[0091] When the oven's abnormal factors are multiple, a graded warning is triggered. At this time, the warning light on the oven body is displayed in the color corresponding to the highest warning level as the main display, and the colors corresponding to other warning levels are used as auxiliary prompts, indicating that the equipment has a complex abnormal risk and requires comprehensive treatment.

[0092] See also Figure 3 As shown, the present invention also provides an artificial intelligence-based real-time measurement and analysis method for energy-saving oven power consumption, the method comprising: S1, analyzing the change rate of the oven temperature at each monitoring time point within a preset time period to determine the oven operation stage.

[0093] S2. Compare the power consumption of the oven with the standard power consumption range corresponding to its operation stage to determine whether the power consumption of the oven is abnormal.

[0094] S3. When the power consumption of the oven is abnormal, the device abnormality data and operation data of the oven during the abnormal time period are collected, and the device abnormality data and its preset threshold are compared and analyzed to obtain the device status abnormality degree of the oven, and the operation data are fused and analyzed to obtain the operation abnormality degree of the oven.

[0095] S4. Analyze the deviation of the external temperature and external humidity of the oven during the abnormal time period from their preset values to obtain the abnormality of the external environment of the oven. Combined with the abnormality of the equipment status and the abnormality of the operation of the oven, determine the abnormal factors of the oven.

[0096] S5. Trigger graded warnings based on abnormal factors of the oven and provide corresponding feedback.

[0097] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0098] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0099] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0101] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0102] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time measurement and analysis system for energy-saving oven electricity based on artificial intelligence, characterized by: The system includes: The operation stage determination module analyzes the temperature change rate of the oven at each monitoring time point within a preset time period to determine the oven operation stage; The power anomaly analysis module compares the power consumption of the oven with the standard power consumption range corresponding to its operating stage to determine whether the power consumption of the oven is abnormal; The oven abnormality analysis module collects the equipment abnormality data and operation data of the oven during the abnormal time period when the power consumption of the oven is abnormal. Based on the comparison and analysis of the equipment abnormality data and its preset threshold value, the abnormality degree of the oven equipment status is obtained. The operation data is then integrated and analyzed to obtain the abnormality degree of the oven operation. The abnormal factor determination module analyzes the deviation of the oven's external temperature and external humidity during the abnormal time period from their preset values to determine the degree of abnormality of the oven's external environment. It also combines the abnormality of the oven's equipment status and operating conditions to determine the abnormality factor. The graded warning feedback terminal triggers graded warnings based on abnormal factors of the oven and provides corresponding feedback.

2. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 1, characterized in that: The step of determining the oven operation stage includes: R1. Combine each monitoring time point with its adjacent next monitoring time point to obtain each monitoring time group, perform deviation analysis on the temperature within each monitoring time group, and obtain the temperature change rate of each monitoring time group; R2. Compare the temperature change rate of each monitoring time group with the preset temperature change range; R3. Count the number of monitoring groups whose temperature change rate is less than the lower limit of the preset interval, within the preset interval, and greater than the upper limit of the preset interval, and record them as the number of cooling trend monitoring groups, the number of constant temperature steady-state monitoring groups, and the number of warming trend monitoring groups respectively; R4. Compare the number of cooling trend monitoring groups, the number of constant temperature steady-state monitoring groups, and the number of warming trend monitoring groups; R5. If the number of cooling trend monitoring groups is the maximum value, the oven is judged to be in the cooling stage. If the number of constant temperature steady-state monitoring groups is the maximum value, the oven is judged to be in the constant temperature stage. If the number of heating trend monitoring groups is the maximum value, the oven is judged to be in the heating stage.

3. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 2, characterized in that: The analysis of the temperature change rate of each monitoring time group includes: The time interval between adjacent monitoring points in the monitoring time group is taken as the interval duration; The temperature difference of each monitoring time group is obtained by subtracting the temperature of the next monitoring time point from the previous monitoring time point within the monitoring time group; The ratio of the temperature difference to the interval time is taken as the temperature change rate of each monitoring time group.

4. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 1 is characterized in that: The determining whether the power consumption of the oven is abnormal includes: Match the operation phase of the oven with the standard power consumption interval corresponding to each operation phase to obtain the standard power consumption interval of the oven; The power consumption of the oven is compared with the standard power consumption range. If the power consumption of the oven is within the standard power consumption range, it is determined that the power consumption of the oven is normal. Otherwise, it is determined that the power consumption of the oven is abnormal.

5. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 1 is characterized in that: The analysis of the abnormality of the equipment status of the oven includes: W1. Extract the power value of the heating element at each abnormal monitoring time point during the abnormal time period from the equipment abnormality data, and construct a power change curve of the heating element with the abnormal monitoring time point as the horizontal axis and the power value as the vertical axis; W2. Matching the operation stages of the oven with the standard power intervals corresponding to the operation stages to obtain the standard power intervals of the oven, and then marking the upper critical line and the lower critical line on the power variation curve based on the upper limit and the lower limit of the standard power interval, respectively; W3, extracting the curve length exceeding the upper critical line and below the lower critical line and the total length of the curve from the power change curve, and then taking the ratio of the two as the power abnormality of the heating element in the oven; W4. Analyze the deviation between the rotation speed of the equipment abnormal data transfer machine at each abnormal monitoring time point within the abnormal time period and the preset rotation speed to obtain the rotation speed abnormality degree of the oven transfer machine; W5. Perform weighted fusion calculation on the power abnormality of the heating element in the oven and the rotation speed abnormality of the rotating machine to obtain the equipment status abnormality of the oven.

6. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 5, characterized in that: The analysis of the abnormal speed of the oven transfer machine includes: Perform relative deviation analysis on the speed at each abnormal monitoring time point and the preset speed to obtain the speed deviation rate of the transfer machine at each abnormal monitoring time point; Matching the operation phase of the oven with the speed deviation rate threshold corresponding to each operation phase to obtain the speed deviation rate threshold of the oven; The speed deviation rate of the transfer machine at each abnormal monitoring time point is compared with the speed deviation rate threshold. The number of abnormal monitoring time points with a speed deviation rate greater than the speed deviation rate threshold and the total number of abnormal monitoring time points are counted. The ratio of the two is then used as the speed abnormality degree of the transfer machine in the oven.

7. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 1 is characterized in that: The analysis of the degree of abnormal operation of the oven includes: Extract the number of non-compliant door openings of the oven during the abnormal time period, the duration of each non-compliant door opening, and the corresponding operating stage from the operating data; Sum the door opening times of each non-compliant door opening to obtain the total door opening time of the non-compliant door opening, and use the ratio of the door opening time to the total door opening time as the door opening time ratio of each non-compliant door opening; The operation stage corresponding to each non-compliant door opening is matched with the abnormal impact factor corresponding to each operation stage to obtain the abnormal impact factor of each non-compliant door opening, and then the abnormal impact factor is summed with the product calculation result of the door opening time ratio to obtain the operation abnormality degree.

8. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 1 is characterized by: The analysis of the abnormality of the external environment of the oven includes: Perform relative deviation analysis on the external temperature and external humidity of the oven at each abnormal monitoring time point during the abnormal time period and the corresponding preset values, and obtain the external temperature deviation and external humidity deviation of the oven at each abnormal monitoring time point; The external temperature deviation and the external humidity deviation are weighted and fused to obtain the external environment abnormality of the oven at each abnormal monitoring time point; The average value of the abnormality of the external environment of the oven at each abnormality monitoring time point is calculated as the abnormality of the external environment of the oven.

9. The artificial intelligence-based real-time measurement and analysis system for energy-saving oven electricity consumption according to claim 1, characterized in that: The factors for determining the abnormality of the oven include: Compare the abnormality of the equipment status, operation and external environment of the oven with their preset abnormality thresholds respectively; If the abnormality of the oven's equipment state is greater than its preset abnormality threshold, the abnormality of the equipment state is regarded as an abnormal factor of the oven; If the degree of abnormal operation of the oven is greater than the preset threshold value of abnormal operation, the abnormal operation is regarded as an abnormal factor of the oven; If the degree of abnormality of the external environment of the oven is greater than a preset threshold value of abnormality of the external environment, the abnormality of the external environment is regarded as an abnormal factor of the oven.

10. A method for real-time measurement and analysis of energy consumption in energy-saving ovens based on artificial intelligence, characterized by: The method includes: S1. Analyze the rate of change of the oven temperature at each monitoring time point within a preset time period to determine the oven operation stage; S2. Compare the power consumption of the oven with the standard power consumption range corresponding to its operating stage to determine whether the power consumption of the oven is abnormal; S3. When the power consumption of the oven is abnormal, collect the device abnormality data and operation data of the oven during the abnormal time period, compare and analyze the device abnormality data with a preset threshold value to obtain the device state abnormality degree of the oven, and fuse and analyze the operation data to obtain the operation abnormality degree of the oven; S4. Analyze the deviation of the external temperature and external humidity of the oven during the abnormal time period from their preset values to obtain the degree of abnormality of the external environment of the oven. Combined with the degree of abnormality of the equipment status and the degree of abnormality of the operation of the oven, determine the abnormal factor of the oven; S5. Trigger graded warnings based on abnormal factors of the oven and provide corresponding feedback.

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