Metering terminal multi-dimensional accurate detection and data optimization method and system based on intelligent algorithm

Abstract

The invention relates to the technical field of metering terminal multi-dimensional accurate detection and data optimization, in particular to a metering terminal multi-dimensional accurate detection and data optimization method and system based on an intelligent algorithm. Obtaining multi-dimensional measurement data in real time through a sensor; processing the metering data by adopting an intelligent algorithm; measurement parameters are automatically adjusted through error compensation; and intelligent adjustment is carried out according to environment change and historical data through the self-adaptive capability. According to the invention, a measurement strategy can be optimized according to real-time feedback and prediction results, long-term stability is maintained, high-precision data support is ensured to be continuously provided in a complex and dynamic environment, and the production efficiency and the equipment reliability are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-dimensional precise detection and data optimization of metering terminals, and in particular to a multi-dimensional precise detection and data optimization method and system for metering terminals based on intelligent algorithms. Background Art

[0002] In modern industrial, environmental monitoring and automation control systems, the accuracy and real-time performance of metering data are crucial to the reliable operation of the system. With the continuous development of industrial automation and Internet of Things technology, equipment and sensors play an increasingly important role in the production process. Real-time monitoring of key parameters such as pressure, temperature, and flow can help realize the automation, precision, and intelligence of the production process. However, sensors are affected by factors such as environmental changes, equipment aging, and external interference, which may cause errors and affect the accuracy of the data. Traditional metering systems usually rely on manual intervention to adjust measurement parameters, which has many problems in modern production processes, especially in high-precision requirements and dynamic environments. Therefore, how to automatically adjust measurement parameters and compensate for errors through intelligent algorithms and adaptive capabilities has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In view of the above problems existing in the prior art, the present invention is proposed.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm, comprising: acquiring multi-dimensional metering data in real time through a sensor;

[0005] Use intelligent algorithms to process measurement data;

[0006] Automatically adjust measurement parameters through error compensation;

[0007] Through adaptive capabilities, intelligent adjustments are made based on environmental changes and historical data.

[0008] As a preferred solution of the multi-dimensional precise detection and data optimization method of a metering terminal based on an intelligent algorithm described in the present invention, the metering data includes pressure, temperature, and flow.

[0009] As a preferred solution of the multi-dimensional precise detection and data optimization method of a metering terminal based on an intelligent algorithm described in the present invention, the intelligent algorithm includes combining data from different sensors and dynamically weighting the data according to the credibility, accuracy and importance of the data.

[0010] As a preferred solution of the multi-dimensional precise detection and data optimization method of a metering terminal based on an intelligent algorithm described in the present invention, wherein: the use of an intelligent algorithm to process the metering data includes processing the collected data once through the intelligent algorithm to ensure the accuracy and stability of the data.

[0011] As a preferred solution of the multi-dimensional precise detection and data optimization method of a metering terminal based on an intelligent algorithm described in the present invention, the error compensation includes training an error model through historical data and real-time data, identifying error characteristics according to the model, and performing compensation.

[0012] As a preferred solution of the multi-dimensional precise detection and data optimization method of a metering terminal based on an intelligent algorithm described in the present invention, the automatic adjustment of measurement parameters includes judging the current state and performance, automatically adjusting the working parameters, and monitoring the difference between the measurement results and the compensated results in real time.

[0013] As a preferred solution of the multi-dimensional precise detection and data optimization method of a metering terminal based on an intelligent algorithm described in the present invention, wherein: the intelligent adjustment according to environmental changes and historical data through adaptive capability includes automatically adjusting measurement parameters through detection of environmental changes and analysis of historical data.

[0014] As a preferred solution of the multi-dimensional precise detection and data optimization system of a metering terminal based on an intelligent algorithm described in the present invention, it includes: an acquisition module, a calculation module, a compensation module, and an adjustment module.

[0015] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods of multi-dimensional precise detection and data optimization of a metering terminal based on an intelligent algorithm are implemented.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in a method for multi-dimensional precise detection and data optimization of a metering terminal based on an intelligent algorithm are implemented.

[0017] Beneficial effects of the present invention: The entire metering system has achieved a high degree of intelligence and automation, significantly improving the accuracy, stability and adaptability of the system. Intelligent algorithms and error compensation mechanisms can effectively eliminate errors caused by environmental changes, equipment aging or sensor drift, ensuring the accuracy and reliability of the data. The adaptive adjustment function enables the system to respond to environmental changes and feedback from historical data in real time, automatically adjust measurement parameters, optimize performance, and avoid manual intervention. Through high-frequency sampling, multi-dimensional data fusion and dynamic weighting, the system can comprehensively monitor the production process and equipment operating status, and reflect potential problems in a timely manner. In addition, the system can optimize the measurement strategy based on real-time feedback and prediction results, maintain long-term stability, ensure continuous provision of high-precision data support in complex and dynamic environments, and greatly improve production efficiency and equipment reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 A schematic diagram of the overall architecture of a multi-dimensional precise detection and data optimization method for a metering terminal based on an intelligent algorithm provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] Example 1

[0022] Reference Figure 1 , which is the first embodiment of the present invention, and provides a multi-dimensional accurate detection and data optimization method for a metering terminal based on an intelligent algorithm, comprising:

[0023] S1, obtain multi-dimensional measurement data in real time through sensors;

[0024] It should be noted that the metering data include pressure, temperature, flow rate, etc. These data are real-time monitoring of important information such as production process, equipment operation status or environmental conditions, and can reflect the working status and potential problems of the system.

[0025] Furthermore, the sensor will collect data periodically or in real-time monitoring, and transmit the data to the central processing unit through the communication protocol. The system will collect data according to the preset sampling period. If the sampling period is too long, it may cause data delay and affect the real-time monitoring effect; if the sampling frequency is too high, it may cause too much data and increase the burden of data processing.

[0026] Through high-frequency sampling and real-time collection of multi-dimensional data, the system can obtain more comprehensive status information and promptly reflect changes in the environment or equipment, which helps to improve the real-time and accuracy of monitoring.

[0027] S2, uses intelligent algorithms to process measurement data;

[0028] It should be noted that the intelligent algorithm includes combining data from different sensors and dynamically weighting the data according to the credibility, accuracy and importance of the different data.

[0029] Furthermore, the use of intelligent algorithms to process metering data includes processing the collected data once through the intelligent algorithm to ensure the accuracy and stability of the data.

[0030] It should be noted that during the data collection process, noise, outliers or missing data may appear. The system first pre-processes the raw data through intelligent algorithms (such as Kalman filtering, median filtering, etc.) to remove noise and invalid data to ensure the cleanliness and stability of the data.

[0031] Through preprocessing with intelligent algorithms, the stability and accuracy of data can be greatly improved, measurement errors caused by environmental interference and sensor noise can be avoided, and the reliability of subsequent data analysis and decision-making can be ensured.

[0032] Furthermore, after data preprocessing, the system will verify the processed data through anomaly detection algorithms (such as statistical Z-score method, isolation forest, etc.) to identify whether there are abnormal fluctuations or erroneous data. If anomalies are found, the system will automatically mark them and trigger correction or compensation mechanisms.

[0033] Through precise verification mechanisms, the system can promptly eliminate abnormal data that does not meet expectations, ensuring that subsequent analysis and optimization are based on real measurement results.

[0034] It should be noted that after data collection, noise removal and outlier detection are first performed. Kalman filtering, mean filtering or median filtering can be used to remove high-frequency noise. Outlier detection algorithms (such as Z-score, isolation forest) are used to discover and exclude abnormal data. For multi-dimensional data from multiple sensors, the system needs to assign different weights according to the accuracy and reliability of each sensor. For example, the measurement data of different sensors can be weighted and integrated by weighted averaging, or principal component analysis (PCA) can be used to reduce the dimension and fuse the data. Machine learning (such as support vector machines, neural networks, etc.) is used to train historical data, learn the relationship between data, and automatically adjust the algorithm parameters according to the training results. In this process, cross-validation and error evaluation are used to optimize the accuracy of the model. Through real-time data input and processing, the system can respond quickly and make necessary adjustments. Intelligent algorithms can automatically learn and optimize during the processing process, so that the system gradually improves its accuracy in long-term use.

[0035] S3, automatically adjusts measurement parameters through error compensation;

[0036] The goal of error compensation is to eliminate or reduce measurement errors caused by sensor drift, environmental changes, etc., thereby improving measurement accuracy. At this stage, the system trains the error model based on historical data and real-time data, and automatically adjusts the measurement parameters based on the error compensation results.

[0037] It should be noted that the error compensation includes training an error model through historical data and real-time data, identifying error features according to the model, and performing compensation.

[0038] It should be noted that the system trains the error compensation model using historical data and real-time data. First, the system builds error models based on deviation patterns in historical data (such as temperature-induced pressure sensor drift, flow meter errors, etc.). Then, the system uses these models to compare with real-time data, identify errors in measurements, and dynamically update compensation strategies.

[0039] Through real-time error compensation, the system can quickly respond to various environmental changes, sensor drift and measurement errors to ensure that measurement accuracy is maximized.

[0040] Furthermore, the automatic adjustment of measurement parameters includes determining the current state and performance, automatically adjusting the working parameters, and monitoring the difference between the measurement results and the compensated results in real time.

[0041] When the compensation result is determined, the system will automatically adjust the measurement parameters according to the current working status, environmental changes and performance evaluation. Common adjustment parameters include: sensor gain, calibration coefficient, sampling frequency, etc. For example, if a pressure sensor error caused by temperature change is detected, the system will automatically adjust the temperature compensation model or the sensor gain coefficient.

[0042] The automatic adjustment mechanism can help the system maintain high measurement accuracy in different environments. Especially after long-term operation, it can automatically correct errors caused by equipment aging or environmental changes to ensure continuous and stable performance.

[0043] It should be noted that the system monitors the difference between the measured results and the compensated results in real time. If the difference is large, the system will trigger the adjustment mechanism to further optimize the error model and readjust the parameters. The system will also determine whether additional calibration or manual intervention is required through set thresholds and rules.

[0044] Through real-time monitoring, the system can promptly identify abnormal conditions and automatically adjust and optimize parameters, reducing human intervention and improving the level of automation.

[0045] Furthermore, the error sources are first analyzed through historical data, and a corresponding error compensation model is established. For example, if it is found that temperature changes cause deviations in the pressure sensor, the system will use a regression model to predict and compensate for this error. Compensation values ​​are calculated using algorithms such as regression analysis and Kalman filtering. The system will adjust the measurement results based on the difference between the real-time data and the compensation model. If there is still a large error in the compensated data, the system will automatically adjust the relevant measurement parameters (such as sensor gain, sampling frequency, calibration coefficient, etc.) according to the deviation. For example, the system may find that the pressure sensor error caused by temperature is large, so it will adjust the temperature compensation model or directly adjust the gain value of the sensor. After compensation, the system continuously monitors the difference between the measurement results and the compensated ones. If the difference is large, the system will readjust the compensation strategy to ensure measurement accuracy.

[0046] S4, through adaptive capabilities, makes intelligent adjustments based on environmental changes and historical data.

[0047] It should be noted that the intelligent adjustment according to environmental changes and historical data through adaptive capabilities includes automatically adjusting measurement parameters through detection of environmental changes and analysis of historical data.

[0048] Furthermore, the system monitors environmental variables (such as temperature, humidity, pressure, etc.) in real time and analyzes them in combination with historical data. When the environmental change exceeds the set threshold, the system will determine whether the measurement process needs to be adjusted. The intelligent algorithm will automatically determine the adjustment plan based on the error pattern and sensor response characteristics in similar environments in the past.

[0049] Through real-time detection of environmental changes and analysis of historical data, the system can quickly adapt to new working environments or changing conditions, ensuring the stability and accuracy of measurement results.

[0050] If the system identifies environmental changes or historical data analysis shows that the currently set measurement parameters are no longer applicable, the system will automatically adjust the relevant parameters. For example, if the temperature rises and causes the sensor error to increase, the system will automatically adjust the gain, calibration factor or sampling frequency based on historical data and environmental monitoring results.

[0051] The system can cope with the uncertainty caused by environmental changes through adaptive adjustment, ensuring that the measurement data always maintains high accuracy in complex production environments. The adaptive mechanism improves the flexibility and long-term stability of the system and reduces the need for manual intervention.

[0052] It should be noted that historical data analysis not only helps the system identify current error patterns, but also provides a basis for future adaptive adjustments. The system will regularly review historical data, analyze data change trends, and predict possible future changes based on these data. Based on the prediction results, the system can adjust measurement parameters in advance to avoid error accumulation.

[0053] Through continuous learning and prediction of historical data, the system can continuously optimize its own adjustment strategy, avoid long-term negative impact of environmental changes on measurement results, and improve the system's adaptability in a dynamic environment.

[0054] The system monitors environmental change data in real time. If environmental parameters such as temperature and humidity change dramatically, the system will determine whether the measurement parameters need to be adjusted. For example, temperature changes may affect the response of the sensor, and the system will dynamically adjust the gain of the sensor based on historical data and real-time data. The system will identify error patterns under different environmental conditions based on long-term accumulated historical data. For example, in a low-temperature environment, the flow meter may have a problem of reduced accuracy. By analyzing these historical data, the system can adjust relevant parameters such as gain and filter settings in advance to avoid accuracy problems. The system will use adaptive algorithms (such as LMS algorithm, reinforcement learning) to adjust the working parameters. Through online learning, the system can continuously optimize the adjustment strategy during real-time operation to improve the robustness and adaptability of the system. For example, the system can optimize the measurement accuracy of the flow meter through reinforcement learning, adjust the measurement strategy under different temperature conditions, and ensure continuous and stable measurement. Once the environmental change is identified, the system will automatically adjust the parameters through the adaptive adjustment mechanism, and optimize the adjustment effect through real-time feedback. For example, if the measurement error increases in an environment with high humidity, the system will adjust the gain of the sensor and optimize the filter parameters to reduce the influence of humidity.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0056] Example 2

[0057] The second embodiment of the present invention provides a multi-dimensional precise detection and data optimization system for a metering terminal based on an intelligent algorithm, which is characterized by comprising an acquisition module, a calculation module, a compensation module, and an adjustment module.

[0058] The acquisition module acquires multi-dimensional measurement data in real time through sensors;

[0059] The calculation module uses intelligent algorithms to process the measurement data;

[0060] Compensation module, automatically adjusts measurement parameters through error compensation;

[0061] The adjustment module uses adaptive capabilities to make intelligent adjustments based on environmental changes and historical data.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0063] Example 3

[0064] The third embodiment of the present invention is different from the first two embodiments in that:

[0065] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0067] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0068] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A multi-dimensional accurate detection and data optimization method for a metering terminal based on an intelligent algorithm, characterized in that: include, Acquire multi-dimensional measurement data in real time through sensors; Use intelligent algorithms to process measurement data; Automatically adjust measurement parameters through error compensation; Through adaptive capabilities, intelligent adjustments can be made based on environmental changes and historical data.

2. The multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm as claimed in claim 1, characterized in that: The metering data includes pressure, temperature and flow rate.

3. The multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm as claimed in claim 1, characterized in that: The intelligent algorithm includes combining data from different sensors and dynamically weighting the data according to the credibility, accuracy and importance of the different data.

4. The multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm as claimed in claim 1, characterized in that: The use of intelligent algorithms to process metering data includes processing the collected data once through the intelligent algorithm to ensure the accuracy and stability of the data.

5. The multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm as claimed in claim 1, characterized in that: The error compensation includes training an error model through historical data and real-time data, identifying error features according to the model, and performing compensation.

6. The multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm as claimed in claim 1, characterized in that: The automatic adjustment of the measurement parameters includes determining the current state and performance, automatically adjusting the working parameters, and monitoring the difference between the measurement result and the compensated result in real time.

7. The multi-dimensional accurate detection and data optimization method of a metering terminal based on an intelligent algorithm as claimed in claim 1, characterized in that: The intelligent adjustment based on environmental changes and historical data through adaptive capabilities includes automatically adjusting measurement parameters through detection of environmental changes and analysis of historical data.

8. A system based on the intelligent algorithm-based multi-dimensional accurate detection and data optimization method of a metering terminal according to any one of claims 1 to 7, characterized in that: It includes an acquisition module, a calculation module, a compensation module and an adjustment module.

9. A computer 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 method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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