Oxygen Sensor Data Calibration Method and Its Device, Electronic Device, Storage Medium

Abstract

The present application discloses an oxygen sensor data calibration method, its device, an electronic device, and a storage medium. Among them, the method includes: calibrating the original reference point data of the oxygen sensor; establishing a dynamic correction model based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor; calculating the sensor attenuation rate within a preset time period according to the dynamic correction model, and performing attenuation prediction processing based on the sensor attenuation rate to obtain a predicted value at the next sampling moment; calculating a difference between the predicted value and the true value obtained at the next sampling moment to obtain a prediction deviation; in the case where the prediction deviation is less than the deviation threshold, performing correction and calibration processing based on the predicted value and the original reference point data to output a corrected value; in the case where the prediction deviation is greater than the deviation threshold, updating the dynamic correction model according to the true value at the next sampling moment. The embodiments of the present application can effectively improve the effectiveness and reliability of the long-term operation of the oxygen sensor.

Description

Technical Field

[0001] The present application relates to the technical field of oxygen sensors, and in particular to a method and device for calibrating oxygen sensor data, an electronic device, and a storage medium. Background Art

[0002] Sulfur hexafluoride (SF6) gas has been widely used in power repair and the power supply of chemical industries and industrial and mining enterprises. Most of the equipment filled with SF6 gas is installed indoors. If the SF6 gas leaks, due to the colorless, odorless nature of SF6 gas itself and its specific gravity being greater than that of oxygen, it is not easily detected after leakage and is likely to accumulate in the lower space, easily causing local oxygen deficiency and suffocating people, posing a great threat to the safety of on-site workers. Therefore, it is necessary to install an oxygen meter and an SF6 gas leakage alarm in the low-lying area of the laboratory. The alarm device generally configures an SF6 gas sensor and an O2 sensor. The O2 sensor used is usually an electrochemical cell type oxygen sensor. The electrochemical cell type oxygen sensor consists of a platinum electrode, a lead electrode, and a breathable membrane electrolyte, and oxygen enters the working electrode through the permeable membrane. The electrodes in the electrochemical cell type oxygen sensor participate in the oxidation-reduction reaction of the battery to generate current, and the magnitude of the current depends on the oxygen reaction rate. By detecting the reaction current through an amplification detection circuit, the oxygen concentration can be accurately measured. However, in the electrochemical cell type oxygen sensor, as the lead electrode is continuously oxidized and consumed, the oxidation-reduction reaction gradually decays, and at this time, there will be a deviation between the oxygen concentration feedback by the sensor and the oxygen concentration in the environment. Therefore, it is necessary to correct and calibrate the data when a large deviation occurs in the sensor.

[0003] In related technologies, generally, the oxygen sensor is calibrated by off-line comparison. However, this method is time-consuming and laborious, and since the calibration process requires the sensor to be off-line or returned to the factory, the monitoring site loses protection, and the effectiveness and reliability are relatively low. In addition, it is also possible to perform time-axis attenuation compensation according to the ideal attenuation curve of the environment provided by the sensor manufacturer to eliminate the deviation caused by sensor attenuation. This method is effective and accurate for the compensation and correction of the same batch of oxygen sensors and in the initial stage of the use of oxygen sensors. However, for different batches, due to the differences in production processes, there will be deviations in the compensation and correction of oxygen sensors. As the use time extends, under different use environments and working conditions, the oxygen sensors will experience different degrees of attenuation. At this time, it will be difficult to correctly correct the deviation of the oxygen sensor by using the ideal attenuation curve of the environment, and the effectiveness and reliability are low. Summary of the Invention

[0004] The embodiments of the present application provide a method and device for calibrating oxygen sensor data, an electronic device, and a storage medium, which can dynamically calibrate the output data of the oxygen sensor in the long term, improve the efficiency of data correction and calibration, and effectively improve the effectiveness and reliability of the long-term operation of the oxygen sensor.

[0005] In a first aspect, an embodiment of the present application provides a method for calibrating oxygen sensor data, the method comprising:

[0006] Calibrating the original reference point data of the oxygen sensor;

[0007] Establishing a dynamic correction model based on a plurality of sampling data points obtained by performing periodic sampling processing on the oxygen sensor;

[0008] Calculating the sensor attenuation rate within a preset time period according to the dynamic correction model, and performing attenuation prediction processing according to the sensor attenuation rate to obtain a predicted value at the next sampling moment;

[0009] Calculating a difference between the predicted value and the true value at the next sampling moment obtained, to obtain a prediction deviation;

[0010] In the case where the prediction deviation is less than the deviation threshold, performing correction and calibration processing according to the predicted value and the original reference point data to output a corrected value;

[0011] In the case where the prediction deviation is greater than the deviation threshold, updating the dynamic correction model according to the true value at the next sampling moment, wherein the updated dynamic correction model is used to re-perform the attenuation prediction processing and the correction and calibration processing for the next time.

[0012] In a second aspect, an embodiment of the present application provides an oxygen sensor data calibration device, comprising:

[0013] A reference calibration module, configured to calibrate the original reference point data of the oxygen sensor;

[0014] A model establishment module, configured to establish a dynamic correction model based on a plurality of sampling data points obtained by performing periodic sampling processing on the oxygen sensor;

[0015] An attenuation prediction module, configured to calculate the sensor attenuation rate within a preset time period according to the dynamic correction model, and perform attenuation prediction processing according to the sensor attenuation rate to obtain a predicted value at the next sampling moment;

[0016] A difference calculation module, configured to calculate a difference between the predicted value and the true value at the next sampling moment obtained, to obtain a prediction deviation;

[0017] A correction and calibration module, configured to, in the case where the prediction deviation is less than the deviation threshold, perform correction and calibration processing according to the predicted value and the original reference point data to output a corrected value;

[0018] A model update module, configured to update the dynamic correction model according to the true value at the next sampling moment when the prediction deviation is greater than the deviation threshold, wherein the updated dynamic correction model is used to re - perform the next attenuation prediction process and correction calibration process.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the oxygen sensor data calibration method described in the first aspect is implemented.

[0020] In a fourth aspect, an embodiment of the present application provides a computer - readable storage medium, storing computer - executable instructions, where the computer - executable instructions are used to implement the oxygen sensor data calibration method described in the first aspect when executed by a processor.

[0021] The embodiments of the present application include: calibrating the original reference point data of an oxygen sensor by using an oxygen sensor data calibration device; establishing a dynamic correction model based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor; calculating the sensor attenuation rate within a preset period according to the dynamic correction model, and performing an attenuation prediction process according to the sensor attenuation rate to obtain a predicted value at the next sampling moment; calculating a difference between the predicted value and the true value obtained at the next sampling moment to obtain a prediction deviation; when the prediction deviation is less than the deviation threshold, performing a correction calibration process according to the predicted value and the original reference point data to output a corrected value; when the prediction deviation is greater than the deviation threshold, updating the dynamic correction model according to the true value at the next sampling moment, wherein the updated dynamic correction model is used to re - perform the next attenuation prediction process and correction calibration process. The established dynamic correction model can be updated based on the latest sampling data obtained, realizing long - term dynamic operation. Through the long - term running dynamic correction model, the output data of the oxygen sensor during operation can be calibrated for a long time and more reliably. That is to say, the solution of the embodiments of the present application can calibrate the output data of the oxygen sensor dynamically for a long time, improve the data correction and calibration efficiency, and effectively improve the effectiveness and reliability of the long - term operation of the oxygen sensor.

[0022] Other features and advantages of the present application will be described in the subsequent specification, and some of them will become obvious from the specification, or be understood by implementing the present application. The objectives and other advantages of the present application can be realized and obtained through the structures specifically pointed out in the specification, claims, and drawings. Description of the Drawings

[0023] The accompanying drawings are used to provide a further understanding of the technical solution of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application, and do not constitute a limitation on the technical solution of the present application.

[0024] Figure 1 It is a schematic flowchart of a method for calibrating oxygen sensor data provided by an embodiment of the present application;

[0025] Figure 2 is Figure 1 a schematic flowchart of the specific method of step S120 in

[0026] Figure 3 is Figure 1 a schematic flowchart of the specific method of step S160 in

[0027] Figure 4 It is a schematic structural diagram of an oxygen sensor data calibration device provided by an embodiment of the present application;

[0028] Figure 5 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0029] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0030] It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from that in the flowchart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0031] The present application provides an oxygen sensor data calibration method, an oxygen sensor data calibration device, an electronic device, and a computer-readable storage medium. By using the oxygen sensor data calibration device, the original reference point data of the oxygen sensor is calibrated; a dynamic correction model is established based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor; the sensor attenuation rate within a preset time period is calculated according to the dynamic correction model, and an attenuation prediction process is performed according to the sensor attenuation rate to obtain a predicted value for the next sampling moment; a difference calculation is performed between the predicted value and the true value obtained for the next sampling moment to obtain a prediction deviation; in the case where the prediction deviation is less than the deviation threshold, a correction and calibration process is performed according to the predicted value and the original reference point data to output a corrected value; in the case where the prediction deviation is greater than the deviation threshold, the dynamic correction model is updated according to the true value of the next sampling moment, wherein the updated dynamic correction model is used to re-perform the next attenuation prediction process and correction and calibration process. The established dynamic correction model can be updated based on the latest sampling data obtained, enabling long-term dynamic operation. Through the long-term operating dynamic correction model, the output data of the operating oxygen sensor can be calibrated in a long-term and more reliable manner. That is to say, the solution of the embodiment of the present application can calibrate the output data of the oxygen sensor dynamically in the long term, improve the data correction and calibration efficiency, and effectively improve the effectiveness and reliability of the long-term operation of the oxygen sensor.

[0032] The following further elaborates on the embodiments of the present application with reference to the accompanying drawings.

[0033] In a first aspect, as Figure 1 shown, Figure 1 is a flowchart of an oxygen sensor data calibration method provided by an embodiment of the present application. The oxygen sensor data calibration method may include but is not limited to steps S110 to S160.

[0034] Step S110: Calibrate the original reference point data of the oxygen sensor.

[0035] Step S120: Establish a dynamic correction model based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor.

[0036] Step S130: Calculate the sensor attenuation rate within a preset time period according to the dynamic correction model, and perform an attenuation prediction process according to the sensor attenuation rate to obtain a predicted value for the next sampling moment.

[0037] Step S140: Perform a difference calculation between the predicted value and the true value obtained for the next sampling moment to obtain a prediction deviation.

[0038] Step S150: In the case where the prediction deviation is less than the deviation threshold, perform a correction and calibration process according to the predicted value and the original reference point data to output a corrected value.

[0039] Step S160: When the prediction deviation is greater than the deviation threshold, update the dynamic correction model according to the true value at the next sampling moment, where the updated dynamic correction model is used to re-perform the next attenuation prediction process and correction calibration process.

[0040] In this embodiment, by adopting the oxygen sensor data calibration method including the above steps S110 to S160, after calibrating the original reference point data of the oxygen sensor, a dynamic correction model is established according to multiple sampling data points obtained through periodic sampling processing. Then, the sensor attenuation rate within a preset time period is calculated according to the dynamic correction model, and the prediction value at the next sampling moment is obtained through the attenuation prediction process based on the sensor attenuation rate. Next, the difference between the prediction value and the true value at the next sampling moment obtained is calculated to obtain the prediction deviation. Then, the prediction deviation is compared with the preset deviation threshold. When the prediction deviation is less than the deviation threshold, the correction calibration process is performed according to the prediction value and the original reference point data to output the correction value. When the prediction deviation is greater than the deviation threshold, the dynamic correction model is updated according to the true value at the next sampling moment, where the updated dynamic correction model is used to re-perform the next attenuation prediction process and correction calibration process. The established dynamic correction model can be updated based on the obtained latest sampling data to achieve long-term dynamic operation. The output data of the oxygen sensor during operation is calibrated in a long-term and more reliable manner through the long-term operating dynamic correction model. Therefore, the solution of the embodiment of the present application can calibrate the output data of the oxygen sensor in a long-term and dynamic manner, improve the data correction and calibration efficiency, and effectively improve the effectiveness and reliability of the long-term operation of the oxygen sensor.

[0041] In some embodiments, a sampling data point includes an output voltage-temperature ratio and an output voltage-atmospheric pressure ratio. It can be understood that before establishing the dynamic correction model according to multiple sampling data points, it further includes: performing multiple periodic sampling processes on the oxygen sensor according to a preset sampling period, collecting multiple output voltage data, and correspondingly collecting the ambient temperature value and ambient atmospheric pressure value of the operating environment where the oxygen sensor is located each time of the periodic sampling process. In one periodic sampling process, the output voltage data, ambient temperature value, and ambient atmospheric pressure value are obtained. The output voltage data is compared with the ambient temperature value to obtain the output voltage-temperature ratio, and the output voltage data is compared with the ambient atmospheric pressure value to obtain the output voltage-atmospheric pressure ratio, that is, a sampling data point is obtained. Obtaining multiple sampling data points provides a data basis for subsequent establishment of the dynamic correction model.

[0042] In some embodiments, step S110: "Calibrating the original reference point data of the oxygen sensor" is further described, and this step may include but is not limited to the following steps:

[0043] First, detect the output voltage data of the oxygen sensor in the access detection circuit.

[0044] Then, when the output voltage data is stably output, after a preset aging time, obtain the reference voltage data output by the reference oxygen sensor in the test environment.

[0045] Next, obtain the reference temperature value and the reference atmospheric pressure value in the test environment.

[0046] Then, compare the reference voltage data with the reference temperature value to obtain a first reference ratio, and compare the reference voltage data with the reference atmospheric pressure value to obtain a second reference ratio.

[0047] Finally, calibrate the first reference ratio and the second reference ratio as the original reference point data of the oxygen sensor.

[0048] By obtaining the original reference point data of the reference oxygen sensor, reliable reference data is provided for subsequent correction and calibration processing. Specifically, the original reference point data includes the first reference ratio and the second reference ratio. The first reference ratio is used in temperature deviation compensation, and the second reference ratio is used in atmospheric pressure deviation compensation. In the correction and calibration processing of the oxygen sensor, the original reference point data is used as the calibration target to provide reliable reference data for the correction and calibration processing. It can be understood that there are many factors affecting the oxygen sensor. The original reference point data can also include a reference ratio obtained based on the reference humidity value of the test environment. As the application scenarios of the oxygen sensor become more complex and the factors affecting the deviation of the oxygen sensor increase, those skilled in the art can obtain the required original reference point data according to the actual situation with reference to the method proposed in this application.

[0049] Specifically, the preset aging time is set to 72 hours, that is, three days. The preset aging time can also be set to other values, and this application does not make specific limitations on this.

[0050] It can be understood that the test environment is generally a room temperature and atmospheric pressure environment, or it can also be a constant temperature and constant pressure laboratory environment. This application does not make specific limitations on this.

[0051] In some embodiments, as Figure 2 shown, Figure 2 is Figure 1 a schematic flowchart of the specific method of step S120. This step S120 may include but is not limited to steps S210 to S230.

[0052] Step S210: Establish a first output characteristic curve on a continuous time axis according to multiple output voltage-temperature ratios;

[0053] Step S220: Establish a second output characteristic curve on a continuous time axis according to multiple output voltage-atmospheric pressure ratios;

[0054] Step S230: establishing a dynamic correction model according to the first output characteristic curve and the second output characteristic curve.

[0055] In this embodiment, by adopting the oxygen sensor data calibration method including the above steps S210 to S230, a dynamic correction model including a first output characteristic curve and a second output characteristic curve is established. The first output characteristic curve can intuitively show the change of the output voltage-temperature ratio over a continuous time. Similarly, the second output characteristic curve can intuitively show the change of the output voltage-atmospheric pressure ratio over a continuous time. The first output characteristic curve and the second output characteristic curve characterize the fluctuation of the output voltage data of the oxygen sensor in the operating environment. The sensor attenuation of the oxygen sensor in a certain period of time can be obtained through the first output characteristic curve and the second output characteristic curve.

[0056] Specifically, the sensor attenuation includes a first attenuation coefficient and a second attenuation coefficient. The first attenuation coefficient of the oxygen sensor can be calculated through the first output characteristic curve, and the second attenuation coefficient of the oxygen sensor can be calculated through the second output characteristic curve. That is, the sensor attenuation rate in a certain period of time can be calculated by dynamically correcting the sensor, so as to predict the attenuation value in the next period of time based on the sensor attenuation rate.

[0057] Based on this, step S130: "Calculate the sensor attenuation rate within a preset time period according to the dynamic correction model, and perform attenuation prediction processing according to the sensor attenuation rate to obtain a predicted value at the next sampling time" is further explained. It can be understood that the preset time period can be one hour, 24 hours, one month or one year. The hourly sensor attenuation rate, daily sensor attenuation rate, monthly sensor attenuation rate or annual sensor attenuation rate of the oxygen sensor can be calculated by the dynamic correction model. After obtaining the sensor attenuation rate of a certain time period, the attenuation prediction value of the oxygen sensor in the next time period can be predicted according to the sensor attenuation rate. The predicted value at the next sampling time can be obtained by subtracting the attenuation prediction value from the current value. The dynamic correction model can monitor the operation of the oxygen sensor for a long time.

[0058] In some embodiments, Figure 3 As shown, Figure 3 yes Figure 1 The flowchart of the specific method of step S160 in FIG. 1 is a flowchart of the specific method of step S160 in FIG. 1. Step S160 may include but is not limited to steps S310 to S330.

[0059] Step S310: according to the first-in-first-out processing principle, the data points with the earliest sampling time in the first output characteristic curve and the second output characteristic curve are respectively removed;

[0060] Step S320: Add the true value at the next sampling moment to the first output characteristic curve and the second output characteristic curve to obtain an updated first output characteristic curve and an updated second output characteristic curve;

[0061] Step S330: Obtain an updated dynamic correction model based on the updated first output characteristic curve and the updated second output characteristic curve.

[0062] In this embodiment, by adopting the oxygen sensor data calibration method including the above steps S310 to S330, when the prediction deviation is greater than the deviation threshold, according to the principle of first-in, first-out processing, the data points with the earliest sampling moments in the first output characteristic curve and the second output characteristic curve are respectively removed, which is beneficial to improving the reliability of the attenuation rate calculated in the attenuation prediction process. Then, the first output characteristic curve and the second output characteristic curve are updated with the true value at the next sampling moment, thereby updating the dynamic correction model and improving the reliability of the dynamic correction model. The updated dynamic correction model is used to re-perform the next attenuation prediction process and correction calibration process. Establishing a dynamically corrected model for long-term operation is beneficial to monitoring the attenuation of the oxygen sensor and can timely correct the output data of the oxygen sensor, ensuring the effectiveness and reliability of the long-term operation of the oxygen sensor.

[0063] In this embodiment, through steps S150 and S160, when the prediction deviation is less than the deviation threshold, the output voltage data of the oxygen sensor is compensated according to the predicted value, which can improve the data correction and calibration efficiency while ensuring good correction and calibration effects. When the prediction deviation is greater than the deviation threshold, the dynamic correction model is updated according to the true value at the next sampling moment, and a new sensor attenuation rate is obtained based on the updated dynamic correction model. The attenuation prediction value for the next period and calibration correction and other processes are re-performed through the sensor attenuation rate. By establishing a dynamically corrected model for long-term operation, the output of the oxygen sensor can be dynamically and effectively corrected, effectively improving the effectiveness and reliability of the long-term operation of the oxygen sensor and reducing the probability of misreporting the attenuation of the oxygen sensor due to the self-attenuation of the oxygen sensor data calibration device.

[0064] In one embodiment, the oxygen sensor data calibration method further includes: when it is detected that the first output characteristic curve or the second output characteristic curve of the oxygen sensor shows non-linear oscillation, it is determined that the oxygen sensor is faulty; a sensor fault notification is sent. When it is detected that the first output characteristic curve or the second output characteristic curve of the oxygen sensor shows non-linear oscillation, the correction and calibration process cannot be performed, so the oxygen sensor detection state is exited, and the sensor fault notification can prompt the staff to replace the oxygen sensor in time to ensure the safety and normal operation of the place where the oxygen sensor is used.

[0065] In one embodiment, after replacing the oxygen sensor with a new one, the original reference point data of the oxygen sensor will be recalibrated. Specifically, after the replaced oxygen sensor operates stably and after a preset aging time, the reference voltage data output by the reference oxygen sensor in the test environment is obtained, the new original reference point data is stored, and then a new correction dynamic model is established for the replaced oxygen sensor. Based on the new correction dynamic model, attenuation prediction processing and correction calibration processing are performed on the replaced oxygen sensor. The solution provided by the embodiments of the present application can perform self-learning based on the replaced oxygen sensor, detect the oxygen sensor more conveniently and reliably, and has general applicability.

[0066] In a second aspect, based on the above oxygen sensor data calibration method, an oxygen sensor data calibration device capable of implementing the above embodiments is proposed. As Figure 4 shown, Figure 4 is a schematic structural diagram of an oxygen sensor data calibration device provided by an embodiment of the present application. The oxygen sensor data calibration device 400 includes: a reference calibration module 410, a model establishment module 420, an attenuation prediction module 430, a difference calculation module 440, a correction calibration module 450, and a model update module 460.

[0067] Among them, the reference calibration module 410 is used to calibrate the original reference point data of the oxygen sensor;

[0068] The model establishment module 420 is used to establish a dynamic correction model according to multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor;

[0069] The attenuation prediction module 430 is used to calculate the sensor attenuation rate within a preset time period according to the dynamic correction model, and perform attenuation prediction processing according to the sensor attenuation rate to obtain a predicted value at the next sampling moment;

[0070] The difference calculation module 440 is used to calculate the difference between the predicted value and the true value obtained at the next sampling moment to obtain a prediction deviation;

[0071] The correction calibration module 450 is used to perform correction calibration processing and output a correction value according to the predicted value and the original reference point data when the prediction deviation is less than the deviation threshold;

[0072] The model update module 460 is used to update the dynamic correction model according to the true value at the next sampling moment when the prediction deviation is greater than the deviation threshold, where the updated dynamic correction model is used to perform the next attenuation prediction processing and correction calibration processing again.

[0073] In this embodiment, after the oxygen sensor data calibration device 400 calibrates the original reference point data of the oxygen sensor by using the reference calibration module 410, it uses the model establishment module 420 to establish a dynamic correction model based on multiple sampling data points obtained through periodic sampling processing. Then, the attenuation prediction module 430 calculates the sensor attenuation rate within a preset time period according to the dynamic correction model, and performs attenuation prediction processing based on the sensor attenuation rate to obtain a predicted value for the next sampling moment. Next, the difference calculation module 440 calculates the difference between the predicted value and the true value obtained at the next sampling moment to obtain a prediction deviation. Then, the prediction deviation is compared with a preset deviation threshold. When the prediction deviation is less than the deviation threshold, the correction and calibration module 450 performs correction and calibration processing based on the predicted value and the original reference point data to output a corrected value. When the prediction deviation is greater than the deviation threshold, the model update module 460 updates the dynamic correction model according to the true value at the next sampling moment. Among them, the updated dynamic correction model is used to perform the next attenuation prediction processing and correction and calibration processing again. The established dynamic correction model can be updated based on the latest sampling data obtained, realizing long-term dynamic operation. Through the long-term dynamic correction model, the output data of the operating oxygen sensor can be calibrated for a long time and more reliably. Therefore, the oxygen sensor data calibration device 400 can calibrate the output data of the oxygen sensor dynamically for a long time, improve the data correction and calibration efficiency, and effectively improve the effectiveness and reliability of the long-term operation of the oxygen sensor.

[0074] In some embodiments, one sampling data point includes an output voltage-temperature ratio and an output voltage-atmospheric pressure ratio. The model establishment module further includes:

[0075] A first data processing module, configured to establish a first output characteristic curve based on multiple output voltage-temperature ratios on a continuous time axis;

[0076] A second data processing module, configured to establish a second output characteristic curve based on multiple output voltage-atmospheric pressure ratios on a continuous time axis;

[0077] A model establishment sub-module, configured to establish a dynamic correction model based on the first output characteristic curve and the second output characteristic curve.

[0078] In some embodiments, the model update module further includes:

[0079] A first data update module, configured to remove the data points with the earliest sampling moments in the first output characteristic curve and the second output characteristic curve respectively according to the principle of first-in, first-out processing;

[0080] A second data update module, configured to add the true value at the next sampling moment to the first output characteristic curve and the second output characteristic curve to obtain an updated first output characteristic curve and an updated second output characteristic curve;

[0081] A model update sub-module, configured to obtain an updated dynamic correction model according to the updated first output characteristic curve and the updated second output characteristic curve.

[0082] In some embodiments, the oxygen sensor data calibration device further includes a fault detection module. The fault detection module is configured to determine that the oxygen sensor is faulty when it detects that the first output characteristic curve or the second output characteristic curve of the oxygen sensor exhibits non-linear oscillation; and send a sensor fault notification.

[0083] It should be noted that since the oxygen sensor data calibration device of this embodiment can implement the oxygen sensor data calibration method of any previous embodiment, the oxygen sensor data calibration device of this embodiment and the oxygen sensor data calibration method of any previous embodiment have the same technical principle and the same technical effect. To avoid redundant content, it will not be elaborated here.

[0084] In a third aspect, as Figure 5 shown, Figure 5 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. The electronic device 500 includes: a memory 520, a processor 510, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the oxygen sensor data calibration method according to the embodiment of the first aspect.

[0085] The processor 510 and the memory 520 can be connected through a bus or other means.

[0086] The processor 510 can be implemented by means of a general-purpose central processing unit, a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present application.

[0087] The memory 520, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory 520 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] The non-transitory software program and instructions required to implement the oxygen sensor data calibration method of the above embodiments are stored in a memory, and when executed by the processor 510, the method steps S110 to S160 described above are executed Figure 1 in the method steps S110 to S160 in Figure 2 the method steps S210 to S230 in Figure 3 and the method steps S310 to S330 in

[0089] The device embodiments or system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, an embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor or a controller, for example, executed by a processor in the above device embodiment, the above processor can execute the oxygen sensor data calibration method in the above embodiment. For example, execute the method steps S110 to S160 described above Figure 1 in the method steps S110 to S160 in Figure 2 the method steps S210 to S230 in Figure 3 and the method steps S310 to S330 in

[0091] Those of ordinary skill in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery media.

[0092] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the above embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present application, and these equivalent deformations or substitutions are all included within the scope defined by the present application.

Claims

1. A method for calibrating oxygen sensor data, characterized in that, Including: Calibrating the original reference point data of the oxygen sensor; Establishing a dynamic correction model based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor; Calculating the sensor attenuation rate within a preset time period according to the dynamic correction model, and performing attenuation prediction processing according to the sensor attenuation rate to obtain a predicted value at the next sampling moment; Calculating a difference between the predicted value and the true value at the next sampling moment obtained, to obtain a prediction deviation; In the case where the prediction deviation is less than the deviation threshold, performing correction and calibration processing according to the predicted value and the original reference point data to output a corrected value; In the case where the prediction deviation is greater than the deviation threshold, updating the dynamic correction model according to the true value at the next sampling moment, wherein the updated dynamic correction model is used to re-perform the next attenuation prediction processing and correction and calibration processing; One of the sampling data points includes an output voltage-temperature ratio and an output voltage-atmospheric pressure ratio; establishing the dynamic correction model based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor includes: Establishing a first output characteristic curve on a continuous time axis according to multiple output voltage-temperature ratios; Establishing a second output characteristic curve on a continuous time axis according to multiple output voltage-atmospheric pressure ratios; Establishing the dynamic correction model according to the first output characteristic curve and the second output characteristic curve; The updating the dynamic correction model according to the true value at the next sampling moment includes: According to the principle of first-in-first-out processing, respectively removing the data points with the earliest sampling moments in the first output characteristic curve and the second output characteristic curve; Adding the true value at the next sampling moment to the first output characteristic curve and the second output characteristic curve to obtain an updated first output characteristic curve and an updated second output characteristic curve; Obtaining an updated dynamic correction model according to the updated first output characteristic curve and the updated second output characteristic curve; In the case where non-linear oscillation appears in the first output characteristic curve or the second output characteristic curve of the oxygen sensor is detected, determining that the oxygen sensor is faulty.

2. The oxygen sensor data calibration method according to claim 1, characterized in that The calibrating the original reference point data of the oxygen sensor includes: Detecting the output voltage data of the oxygen sensor connected to the detection circuit; After the output voltage data is stably output, after a preset aging time, obtaining the reference voltage data output by the reference oxygen sensor in the test environment; Obtaining the reference temperature value and the reference atmospheric pressure value in the test environment; Comparing the reference voltage data with the reference temperature value to obtain a first reference ratio, and comparing the reference voltage data with the reference atmospheric pressure value to obtain a second reference ratio; Calibrating the first reference ratio and the second reference ratio as the original reference point data of the oxygen sensor.

3. The oxygen sensor data calibration method according to claim 1, characterized in that, After determining that the oxygen sensor is faulty in the case where non-linear oscillation appears in the first output characteristic curve or the second output characteristic curve of the oxygen sensor is detected, the method further includes: Send a sensor failure notification.

4. An oxygen sensor data calibration device, characterized in that, Including: A reference calibration module for calibrating the original reference point data of the oxygen sensor; A model establishment module for establishing a dynamic correction model based on multiple sampling data points obtained by performing periodic sampling processing on the oxygen sensor; An attenuation prediction module for calculating the sensor attenuation rate within a preset time period according to the dynamic correction model, and performing attenuation prediction processing according to the sensor attenuation rate to obtain a predicted value at the next sampling moment; A difference calculation module for calculating the difference between the predicted value and the true value at the next sampling moment obtained, to obtain a prediction deviation; A correction and calibration module for performing correction and calibration processing according to the predicted value and the original reference point data and outputting a corrected value when the prediction deviation is less than the deviation threshold; A model update module for updating the dynamic correction model according to the true value at the next sampling moment when the prediction deviation is greater than the deviation threshold, wherein the updated dynamic correction model is used to re-perform the next attenuation prediction processing and correction and calibration processing; Wherein, one of the sampling data points includes an output voltage-temperature ratio and an output voltage-atmospheric pressure ratio; the model establishment module further includes: A first data processing module for establishing a first output characteristic curve on a continuous time axis according to multiple output voltage-temperature ratios; A second data processing module for establishing a second output characteristic curve on a continuous time axis according to multiple output voltage-atmospheric pressure ratios; A model establishment sub-module for establishing the dynamic correction model according to the first output characteristic curve and the second output characteristic curve; The model update module further includes: A first data update module for respectively removing the data points with the earliest sampling moments in the first output characteristic curve and the second output characteristic curve according to the principle of first-in-first-out processing; A second data update module for adding the true value at the next sampling moment to the first output characteristic curve and the second output characteristic curve to obtain an updated first output characteristic curve and an updated second output characteristic curve; A model update sub-module for obtaining an updated dynamic correction model according to the updated first output characteristic curve and the updated second output characteristic curve; The device is further configured to: determine that the oxygen sensor fails when it is detected that the first output characteristic curve or the second output characteristic curve of the oxygen sensor exhibits non-linear oscillation.

5. An electronic device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the oxygen sensor data calibration method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that, Stored with computer-executable instructions, and the computer-executable instructions are used to implement the oxygen sensor data calibration method according to any one of claims 1 to 3 when being executed by a processor.

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