Temperature sensor-based temperature prediction method and related system
Through the temperature prediction method based on temperature sensors, the problem of poor monitoring of traditional temperature monitoring in complex environments is solved, and higher monitoring accuracy and system stability are achieved, reducing maintenance costs.
Patent Information
- Application Number
- CN202411513384.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-02-18
AI Technical Summary
Traditional temperature monitoring methods lack in-depth analysis and processing capabilities in complex environments, resulting in poor monitoring results.
The temperature prediction method based on the temperature sensor is adopted, including determining the temperature parameters that need to be collected, acquiring and converting temperature data, analyzing the temperature change trend, predicting the sensor performance, and adjusting according to the prediction results.
Through in-depth analysis and processing of temperature data, identify temperature change trends, improve the ability to respond to environmental changes, promptly detect abnormal situations, ensure the safety of system operation, and reduce frequent maintenance requirements for sensors through prediction and adjustment measures, and reduce maintenance and replacement costs.
Smart Images
Figure CN119474673A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of temperature sensor parameter prediction, and in particular relates to a temperature prediction method based on a temperature sensor and a related system. Background Art
[0002] Micro temperature sensor is a small and accurate temperature detection device, which is widely used in various fields to provide strong guarantee for equipment operation and product quality. Micro temperature sensor is made of high-performance semiconductor materials, with the characteristics of small size, light weight and fast response speed. The sensor includes sensitive components, signal processing circuits and output interfaces. In terms of design, the sensor adopts a compact structure, which is easy to install in different scenarios. Micro temperature sensor has the following characteristics: first, high precision, small measurement error, can accurately reflect the ambient temperature; second, fast response speed, real-time monitoring of temperature changes; third, strong anti-interference ability, suitable for complex environments; fourth, low power consumption, energy saving and environmental protection. In the fields of environmental monitoring, laboratory research, industrial control, etc., real-time monitoring and control of temperature are of great significance to ensure production safety, improve product quality and realize energy management.
[0003] However, traditional temperature monitoring methods usually rely on simple temperature sensors, which can collect temperature data in the environment but lack the ability to deeply analyze and process the data, resulting in poor monitoring effects in complex environments. Summary of the invention
[0004] The purpose of the present invention is to overcome the shortcomings of existing monitoring methods that lack in-depth analysis and processing capabilities, resulting in poor monitoring effects in complex environments, and to provide a temperature prediction method based on a temperature sensor and a related system.
[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a temperature prediction method based on a temperature sensor, comprising the following steps: According to the monitoring requirements, determine the temperature parameters that need to be collected, and obtain the temperature parameters collected by the temperature sensor; Convert temperature parameters into compatible receiving data; Analyze the received data and identify the temperature change trend; Predict the performance of the temperature sensor based on the temperature change trend; The temperature sensor is adjusted based on the predicted performance of the temperature sensor.
[0006] A further improvement of the present invention is that, according to monitoring requirements, the temperature parameters to be collected are determined, and the specific method for obtaining the temperature parameters collected by the temperature sensor is as follows: Get all the data of temperature parameters; Identify the required temperature parameters based on monitoring needs; Determine the frequency and time period for collecting temperature parameters based on monitoring requirements; According to the frequency and time period of temperature parameter acquisition, the required temperature parameters in the temperature sensor are acquired.
[0007] A further improvement of the present invention is that the compatible format of received data includes a timestamp, a sensor ID and a measurement unit; Compatible transmission protocols for receiving data are MQTT, HTTP or CoAP; The temperature parameters are compressed before transmission.
[0008] A further improvement of the present invention is that the specific method of analyzing the received data and identifying the temperature change trend is as follows: Preprocess the received data to remove missing values, outliers and noise, and then perform smoothing to reduce short-term fluctuations; The processed received data are processed using linear regression and sliding average to identify the rising or falling trend of the temperature parameters in the received data; The preset statistical model is used to identify abnormal points in the rising or falling trend of the temperature parameters. If the number of abnormal points exceeds the threshold, an alarm is issued; otherwise, the temperature change trend is output.
[0009] A further improvement of the present invention is that, according to the temperature variation trend, a specific method for predicting the performance of the temperature sensor is as follows: Preset key indicators of temperature sensor performance, including sensor sensitivity, accuracy, response time and stability; Obtain the historical performance data of the temperature sensor, and combine it with the temperature change trend of the temperature sensor to obtain the change of the temperature sensor in different ranges; According to the changes of temperature sensors in different ranges and combined with key indicators of temperature sensor performance, a relationship model between temperature and temperature sensor is established, and the impact of temperature changes on temperature sensors is identified using a preset statistical analysis model; The life data analysis method is used to analyze the impact of temperature changes on the temperature sensor, evaluate the failure time point of the temperature sensor, and obtain the performance prediction result of the temperature sensor.
[0010] A further improvement of the present invention is that, according to the predicted performance of the temperature sensor, the specific method for adjusting the temperature sensor is as follows: Analyze the prediction performance of the temperature sensor to obtain the performance differences of the temperature sensor under different temperature conditions; According to the analysis results, the working parameters of the temperature sensor are preliminarily optimized; Re-collecting temperature parameters of the temperature sensor, analyzing the temperature parameters of the temperature sensor, and obtaining drift parameters of the temperature sensor; If the drift parameter of the temperature sensor is within the preset threshold range, the temperature parameter of the temperature sensor is collected again; otherwise, a corresponding adjustment scheme is generated and sent according to corresponding data in a preset adjustment scheme library.
[0011] In a second aspect, the present invention provides temperature prediction based on a temperature sensor, comprising: The parameter acquisition module is used to determine the temperature parameters that need to be collected according to the monitoring requirements and obtain the temperature parameters collected by the temperature sensor; A parameter conversion module, used for converting temperature parameters into compatible receiving data; Data analysis module, used to analyze received data and identify temperature change trends; The performance prediction module is used to predict the performance of the temperature sensor according to the temperature change trend; The adjustment module is used for adjusting the temperature sensor according to the predicted performance of the temperature sensor.
[0012] A further improvement of the present invention is that the function of the data analysis module is realized by the following method: Preprocess the received data to remove missing values, outliers and noise, and then perform smoothing to reduce short-term fluctuations; The processed received data are processed using linear regression and sliding average to identify the rising or falling trend of the temperature parameters in the received data; The preset statistical model is used to identify abnormal points in the rising or falling trend of the temperature parameters. If the number of abnormal points exceeds the threshold, an alarm is issued; otherwise, the temperature change trend is output.
[0013] In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of a temperature prediction method based on a temperature sensor when executing the computer program.
[0014] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a temperature prediction method based on a temperature sensor.
[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention ensures that the acquired data has high accuracy and consistency by determining key temperature parameters and performing effective data conversion, thereby reducing errors caused by incompatible formats or unit confusion. The present invention can identify the temperature change trend by deeply analyzing the received data, thereby improving the responsiveness to environmental changes, helping to timely discover abnormal conditions, and ensuring the safety of system operation. The present invention can more accurately predict the performance of the temperature sensor through the analysis of the temperature change trend, identify possible faults or performance degradation in advance, and reduce the risk of unexpected downtime or equipment damage. The present invention adjusts the temperature sensor according to the real-time prediction results to enhance the adaptability of the system in complex environments. It can make flexible adjustments for different environmental conditions and temperature changes to improve the overall stability and reliability of the system. The present invention reduces the frequent maintenance requirements for sensors and reduces the maintenance and replacement costs caused by downtime due to faults through prediction and adjustment measures. The present invention conducts in-depth analysis and processing of data to improve the utilization rate of data, so that the collected temperature parameters are not only monitoring data, but also provide valuable information support for subsequent decision-making. The present invention promotes the intelligent development of the monitoring system by introducing a prediction model and a real-time adjustment mechanism, realizes autonomous monitoring and self-optimization, and improves the intelligence level of the overall system. The analysis reports and adjustment suggestions generated by the present invention provide a scientific basis for decision-making for management, so that more reasonable measures can be taken when facing complex environments. The present invention optimizes the allocation and utilization efficiency of resources through precise monitoring and prediction, so that energy consumption and operating costs are effectively controlled. In summary, the present invention can not only significantly improve the effectiveness of temperature monitoring, but also provide strong support for a wider range of industrial applications, ensuring the stability and security of the system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 This is a system diagram of Example 5. DETAILED DESCRIPTION
[0017] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.
[0018] Embodiment 1: See also Figure 1 , the temperature prediction method based on the temperature sensor comprises the following steps: S1, according to the monitoring requirements, determine the temperature parameters that need to be collected, and obtain the temperature parameters collected by the temperature sensor.
[0019] S2, convert the temperature parameter into compatible receiving data.
[0020] S3, analyzing the received data to identify the temperature change trend.
[0021] S4, predicting the performance of the temperature sensor according to the temperature change trend.
[0022] S5, adjusting the temperature sensor according to the predicted performance of the temperature sensor.
[0023] Embodiment 2: See also Figure 2 , temperature prediction based on temperature sensors, including: The parameter acquisition module is used to determine the temperature parameters that need to be collected according to the monitoring requirements and obtain the temperature parameters collected by the temperature sensor; A parameter conversion module, used for converting temperature parameters into compatible receiving data; Data analysis module, used to analyze received data and identify temperature change trends; The performance prediction module is used to predict the performance of the temperature sensor according to the temperature change trend; The adjustment module is used for adjusting the temperature sensor according to the predicted performance of the temperature sensor.
[0024] Embodiment 3: This embodiment provides a temperature prediction method based on a temperature sensor, comprising the following steps: Step 1: First, obtain all temperature data collected by the temperature sensor, identify and determine the temperature parameters that need to be collected according to monitoring requirements, determine the frequency and time period for collecting temperature parameters according to monitoring requirements, and regularly collect the required temperature parameters according to the determined frequency and time period.
[0025] Step 2: Convert the temperature parameters into a compatible receiving data format, including timestamp, sensor ID, and measurement unit, and use a transmission protocol such as MQTT, HTTP, or CoAP for data transmission. Before transmission, compress the temperature parameters to reduce the amount of data.
[0026] Step three, preprocess the received data to remove missing values, outliers and noise, perform smoothing to reduce short-term fluctuations, use linear regression or sliding average methods to analyze the processed received data, identify the rising or falling trend of the temperature parameters, and use the preset statistical model to identify the abnormal points of the temperature parameters. If the number of abnormal points exceeds the threshold, an alarm is triggered; otherwise, the temperature change trend is output.
[0027] Step 4: preset key indicators of temperature sensor performance, including sensitivity, accuracy, response time and stability, obtain historical performance data of temperature sensors, analyze the performance of sensors in different ranges in combination with temperature change trends, establish a relationship model between temperature and sensor performance based on temperature changes and key indicators, identify the impact of temperature changes on sensor performance, use life data analysis method to evaluate the impact of temperature changes on temperature sensors, predict failure time points, and obtain performance prediction results.
[0028] Step 5: Analyze the predicted performance of the temperature sensor, identify its performance differences under different temperature conditions, and preliminarily optimize the working parameters of the temperature sensor based on the analysis results. Re-collect the temperature parameters of the temperature sensor, analyze the acquired temperature data, and calculate the drift parameters of the temperature sensor. If the drift parameters are within the preset threshold range, re-collect the temperature parameters; if they exceed the threshold, generate the corresponding adjustment plan based on the preset adjustment plan library and send it for execution.
[0029] This embodiment ensures effective monitoring and prediction of temperature in complex environments and optimizes the performance of the temperature sensor.
[0030] Embodiment 4: This embodiment is directed to a temperature monitoring system for industrial equipment, and aims to improve the monitoring accuracy and reliability of the equipment in a high temperature environment.
[0031] Step 1: All data collected by the temperature sensor, including timestamp, temperature value, sensor ID, etc. The sensor collects temperature once a minute and records the temperature data for the past 48 hours. According to the monitoring requirements, the temperature parameters to be collected are determined to be the internal temperature of the device and the ambient temperature. The collection frequency is set to once a minute, the time period is the past 48 hours, and the temperature parameters are recorded once a minute. The data is as follows: Timestamp: 2024-10-25 08:00:00, Device temperature: 75.5°C, Ambient temperature: 22.3°C Timestamp: 2024-10-25 08:01:00, Device temperature: 76.0°C, Ambient temperature: 22.4°C Step 2: Convert the temperature parameter to JSON format: { "timestamp": "2024-10-25 08:00:00", "sensor_id": "T12345", "device_temp": 75.5, "ambient_temp": 22.3, "unit": "°C" } The MQTT protocol is used for data transmission, and the gzip algorithm is used to compress the data to reduce the size of the data packet.
[0032] Step 3: Remove missing values and outliers (such as temperature data outside the normal range), use smoothing to reduce short-term fluctuations, and use the sliding average method to calculate the trend of temperature data. For example, the temperature trend over the past 48 hours shows that the device temperature is on the rise, and the data is as follows: Device temperature trend: 75.5°C → 76.0°C → 76.5°C (increase of about 0.5°C per minute) Assume that the set threshold is 2 abnormal points. If it is exceeded, an alarm will be triggered. After detection, it is found that there is only 1 abnormal point, so no alarm is triggered.
[0033] Step 4: The key indicators set are sensitivity (±0.5°C), accuracy (±1°C), response time (<1 second), and stability (<2% drift). Combined with historical performance data, analyze the performance of the sensor in the past 6 months. For example, under high temperature conditions (>80°C), the stability decreases by 5%. A linear model is established to identify the impact of temperature changes on sensor performance through statistical analysis. For example, for every 1°C increase in temperature, the sensor stability decreases by about 0.1%. The life data analysis method is used to evaluate the failure time point of the equipment at high temperature. For example, it is expected that under continuous high temperature (>85°C), the sensor will fail after 1000 hours.
[0034] Step 5: The analysis results show that when the ambient temperature is higher than 25°C, the device temperature response becomes slower. The sensor's operating parameters are optimized and the sampling frequency is adjusted to once every 30 seconds to improve data responsiveness. The data is recollected and it is found that the drift parameter is 1.5°C, which is lower than the set threshold (2°C). Therefore, it is decided that no immediate calibration is required. According to the preset adjustment solution library, if the drift parameter exceeds the threshold, an adjustment solution for the drift is generated, such as adjusting the sensor's calibration settings and replacing the sensor.
[0035] This embodiment can intuitively demonstrate how to implement a temperature prediction method based on a temperature sensor in combination with specific data to improve the accuracy and reliability of monitoring.
[0036] Embodiment 5: See also Figure 3 As shown, the present invention also provides an electronic device 100 for a temperature prediction method based on a temperature sensor; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0037] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the temperature prediction method based on the temperature sensor described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
[0038] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.
[0039] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a temperature prediction method based on a temperature sensor, and the processor 102 may execute the plurality of instructions to implement: According to the monitoring requirements, determine the temperature parameters that need to be collected, and obtain the temperature parameters collected by the temperature sensor; Convert temperature parameters into compatible receiving data; Analyze the received data and identify the temperature change trend; Predict the performance of the temperature sensor based on the temperature change trend; The temperature sensor is adjusted based on the predicted performance of the temperature sensor.
[0040] Embodiment 6: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).
[0041] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0042] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0043] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A temperature prediction method based on a temperature sensor, characterized in that: The following steps are involved: According to the monitoring requirements, determine the temperature parameters that need to be collected, and obtain the temperature parameters collected by the temperature sensor; Convert temperature parameters into compatible receiving data; Analyze the received data and identify the temperature change trend; Predict the performance of the temperature sensor based on the temperature change trend; The temperature sensor is adjusted based on the predicted performance of the temperature sensor.
2. The temperature prediction method based on temperature sensor according to claim 1, characterized in that: According to the monitoring requirements, determine the temperature parameters that need to be collected. The specific method for obtaining the temperature parameters collected by the temperature sensor is as follows: Get all the data of temperature parameters; Identify the required temperature parameters based on monitoring needs; Determine the frequency and time period for collecting temperature parameters based on monitoring requirements; According to the frequency and time period of temperature parameter acquisition, the required temperature parameters in the temperature sensor are acquired.
3. The temperature prediction method based on temperature sensor according to claim 1, characterized in that: The format of the received data includes timestamp, sensor ID and measurement unit. Compatible transmission protocols for receiving data are MQTT, HTTP or CoAP; The temperature parameters are compressed before transmission.
4. The temperature prediction method based on temperature sensor according to claim 1, characterized in that: The specific method for analyzing the received data and identifying the temperature change trend is as follows: Preprocess the received data to remove missing values, outliers and noise, and then perform smoothing to reduce short-term fluctuations; The processed received data are processed using linear regression and sliding average to identify the rising or falling trend of the temperature parameters in the received data; Use the preset statistical model to identify abnormal points in the rising or falling trend of temperature parameters. If the number of abnormal points exceeds the threshold, an alarm will be issued; Otherwise, output the temperature change trend.
5. The temperature prediction method based on temperature sensor according to claim 1, characterized in that: According to the temperature change trend, the specific method for predicting the performance of the temperature sensor is as follows: Preset key indicators of temperature sensor performance, including sensor sensitivity, accuracy, response time and stability; Obtain the historical performance data of the temperature sensor, and combine it with the temperature change trend of the temperature sensor to obtain the change of the temperature sensor in different ranges; According to the changes of temperature sensors in different ranges and combined with key indicators of temperature sensor performance, a relationship model between temperature and temperature sensor is established, and the impact of temperature changes on temperature sensors is identified using a preset statistical analysis model; The life data analysis method is used to analyze the impact of temperature changes on the temperature sensor, evaluate the failure time point of the temperature sensor, and obtain the performance prediction result of the temperature sensor.
6. The temperature prediction method based on temperature sensor according to claim 1, characterized in that: Based on the predicted performance of the temperature sensor, the specific method for adjusting the temperature sensor is as follows: Analyze the prediction performance of the temperature sensor to obtain the performance differences of the temperature sensor under different temperature conditions; According to the analysis results, the working parameters of the temperature sensor are preliminarily optimized; Re-collecting temperature parameters of the temperature sensor, analyzing the temperature parameters of the temperature sensor, and obtaining drift parameters of the temperature sensor; If the drift parameter of the temperature sensor is within the preset threshold range, the temperature parameter of the temperature sensor is collected again; otherwise, a corresponding adjustment scheme is generated and sent according to corresponding data in a preset adjustment scheme library.
7. Temperature prediction based on temperature sensor, characterized in that: include: The parameter acquisition module is used to determine the temperature parameters that need to be collected according to the monitoring requirements and obtain the temperature parameters collected by the temperature sensor; A parameter conversion module, used for converting temperature parameters into compatible receiving data; Data analysis module, used to analyze received data and identify temperature change trends; The performance prediction module is used to predict the performance of the temperature sensor according to the temperature change trend; The adjustment module is used for adjusting the temperature sensor according to the predicted performance of the temperature sensor.
8. The temperature prediction based on the temperature sensor according to claim 7, characterized in that: The functions of the data analysis module are implemented in the following ways: Preprocess the received data to remove missing values, outliers and noise, and then perform smoothing to reduce short-term fluctuations; The processed received data are processed using linear regression and sliding average to identify the rising or falling trend of the temperature parameters in the received data; Use the preset statistical model to identify abnormal points in the rising or falling trend of temperature parameters. If the number of abnormal points exceeds the threshold, an alarm will be issued; Otherwise, output the temperature change trend.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the temperature prediction method based on a temperature sensor according to any one of claims 1 to 6 are implemented.
10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the temperature prediction method based on a temperature sensor according to any one of claims 1 to 6 are implemented.