Automatic instrument remote management system based on cloud computing

Abstract

The invention relates to the technical field of management, in particular to an automatic instrument remote management system based on cloud computing. The system comprises a perception acquisition unit, a communication transmission unit, a cloud analysis unit and an application interaction unit. The sensing acquisition unit acquires temperature data of industrial field equipment and instruments in real time and transmits the temperature data to the cloud analysis unit through the communication transmission unit. The cloud analysis unit comprises an abnormal value filtering module, a data comparison module and a life value judgment module and can store, clean and analyze data. The abnormal value filtering module firstly judges the instrument temperature, and if not, the application interaction unit outputs the temperature; if yes, the temperature is sent to the data comparison module to be compared with the equipment temperature. If not, outputting the instrument temperature; if yes, the life value judgment module evaluates the service life of the instrument in combination with historical data. And finally, the application interaction unit displays the equipment temperature, the instrument temperature and the remaining service life of the instrument.

Description

Technical Field

[0001] The present invention relates to the field of management technology, and in particular to a cloud computing-based remote management system for automated instruments. Background Art

[0002] Automation instruments refer to intelligent measuring equipment that can automatically detect, display, record, control or transmit industrial process parameters (such as temperature, pressure, flow, liquid level, etc.).

[0003] The existing cloud-based automated instrument temperature monitoring and management process is as follows: Real-time device and instrument temperature data is collected through relevant sensing and acquisition units and uploaded to a cloud platform via a communication transmission unit. Upon receiving the data, the cloud platform applies common algorithms (such as temperature drift compensation algorithms, combined with machine learning) to uniformly process the overall temperature data, performing error correction and trend analysis. It then uses relevant models (such as the Arrhenius model) to predict the remaining instrument lifespan. Once an alarm is triggered, a visual interface displays information such as device temperature, instrument temperature, and remaining instrument lifespan, enabling status monitoring and remote operation and maintenance management.

[0004] The following key technical defects still exist in the existing cloud computing-based instrument temperature monitoring and management: most existing systems simply process and analyze the collected data in a unified manner, which makes it inconvenient to make an initial judgment on whether the instrument temperature data is abnormal, and to conduct multiple rounds of judgments such as further comparison and in-depth analysis. This makes it easy to misjudge the instrument temperature data, and it is inconvenient to carefully classify and handle abnormal instrument temperature conditions. In addition, for abnormal temperature conditions, the existing technology is not convenient for recalculating the remaining service life by combining factors such as the abnormal temperature value and duration. The existing system may not be accurate enough in assessing the instrument life under different working conditions. Summary of the Invention

[0005] The present invention provides a cloud computing-based automated instrument remote management system. By improving the instrument temperature data processing flow and display method, the system adds an abnormal value determination and multi-round analysis mechanism. The abnormal value filtering module can initially distinguish normal and abnormal temperature data. Then, the data comparison module and the life value judgment module can be used to further compare and analyze the abnormal temperature data, conduct in-depth analysis, and re-evaluate the remaining life of the instrument. The normal and abnormal temperature data are calculated based on the cumulative usage time and combined with factors such as the abnormal temperature value and duration, and then displayed accordingly. This allows managers to understand the instrument status more clearly and intuitively, thereby solving the problems raised in the above-mentioned background technology, namely:

[0006] The existing system's judgment of instrument temperature data is not detailed enough and is prone to misjudgment. The instrument life assessment under different working conditions is not accurate enough, and there is a lack of detailed classification and processing steps for abnormal instrument temperature conditions.

[0007] To achieve the above objectives, a cloud computing-based remote management system for automated instruments is provided, which includes a sensing and acquisition unit, a communication transmission unit, a cloud analysis unit, and an application interaction unit.

[0008] The sensing and acquisition unit captures temperature data from industrial field equipment and instruments in real time. The cloud analysis unit receives data transmitted by the communication transmission unit and stores, cleans, and analyzes the data.

[0009] The cloud analysis unit includes an abnormal value filtering module, a data comparison module and a life value judgment module. The abnormal value filtering module is used to judge the instrument temperature. When the instrument temperature is ≤ the normal value, the instrument temperature is output through the application interaction unit. When the instrument temperature is greater than the normal value, the instrument temperature data is input into the data comparison module for comparison with the device temperature data; when the instrument temperature is less than or equal to the device temperature, the instrument temperature is output through the application interaction unit. When the instrument temperature is greater than the device temperature, the instrument temperature data is input into the life value judgment module, and the instrument life at this moment is judged in combination with historical life value test data; finally, the device temperature, instrument temperature and remaining instrument life are displayed through the application interaction unit.

[0010] In the above technical solution, due to the establishment of a multi-level cloud-based analysis unit consisting of outlier filtering, data comparison, and life value judgment modules, even if the collected industrial equipment and instrument temperature data is complex and uncertain, the outlier filtering module can first screen out abnormal data according to standards. Data exceeding normal values enters the data comparison module for comparison with the equipment temperature to further identify abnormalities. Data exceeding the equipment temperature then enters the life value judgment module for in-depth lifespan assessment combined with historical data. This enables multiple rounds of precise control of instrument temperature data, avoiding misjudgments and accurately grasping the instrument status and lifespan. The application interaction unit can also clearly display data from different situations, helping managers make operation and maintenance decisions and ensure stable production.

[0011] On this basis, the perception collection unit includes a timing collection module, an event-triggered collection module, and a remote command collection module. The timing collection module is used to automatically read instrument data at preset time intervals. When the instrument temperature exceeds the threshold, the event-triggered collection module actively reports the data. When the application interaction unit issues a reverse instruction, the remote command collection module immediately uploads the data.

[0012] In another technical solution, the abnormal value filtering module is used to judge the instrument temperature data, directly output the normal instrument temperature to the application interaction unit, and the instrument temperature data with too high temperature (>normal temperature) enters the data comparison module for re-judgment;

[0013] The data comparison module is used to compare the instrument temperature data of excessively high temperature (>normal temperature) with the device temperature at the same time, and treats the instrument temperature data that is less than or equal to the device temperature threshold as normal temperature and output it to the application interaction unit, while treats the instrument temperature data that is greater than the device temperature threshold as abnormal temperature and inputs the abnormal temperature data into the life value judgment module to recalculate the instrument life;

[0014] On this basis, the life value judgment module is used to calculate the remaining life of the instrument under abnormal temperature data, including the following method steps:

[0015] Input layer: Input the abnormal temperature data of the instrument, obtain the historical production temperature test data set of the instrument, read the nominal life parameters of the instrument and the current cumulative operating time;

[0016] Preprocessing layer: Standardizes input data, including unifying temperature data units, converting time units, and removing outliers to ensure that data quality meets calculation requirements;

[0017] Feature extraction layer: calculates the current temperature deviation from the calibration value, extracts historical temperature extremes, counts the temperature fluctuation frequency, and calculates the benchmark life loss rate;

[0018] Model calculation layer: Calculate the temperature acceleration factor based on the Arrhenius model, apply the Coffin-Manson model to evaluate the cumulative thermal fatigue, and comprehensively calculate the corrected remaining life;

[0019] Calibration optimization layer: The prediction results are calibrated by using a correction function based on historical test data, a sliding window algorithm is used to smooth data fluctuations, and a life confidence interval with a 95% confidence level is established;

[0020] Output layer: Outputs a complete evaluation report containing the remaining life prediction value and confidence interval to the application interaction unit.

[0021] This technical solution first distinguishes between normal and abnormal temperature data through the outlier filtering module, and further identifies them through the data comparison module, thereby improving the accuracy of temperature judgment; through the life value judgment module, multiple key data are collected from the input layer, the data quality is guaranteed by the preprocessing layer, the key features are extracted by the feature extraction layer, and then the remaining life is accurately calculated by integrating multiple models through the model calculation layer, and the calibration optimization layer calibrates and optimizes the results, outputting a more comprehensive and reliable evaluation report, thereby achieving more accurate evaluation and control of the instrument life.

[0022] On this basis, the application interaction unit is used to display the device temperature data, the instrument temperature data, and the instrument remaining life data, wherein the acquisition of these data includes the following method steps:

[0023] S1. The sensing and acquisition unit collects device temperature data and instrument temperature data in real time, and uploads the data to the cloud analysis unit through the communication transmission unit. The cloud analysis unit directly processes the device temperature data and outputs it to the application interaction unit for visual display.

[0024] S2. First, the instrument temperature data is judged by the abnormal value filtering module. If the instrument temperature is ≤ the preset normal temperature threshold, it is judged as normal temperature data; if the instrument temperature is greater than the preset normal temperature threshold, the data comparison module is entered for secondary judgment;

[0025] S3. The temperature data of the instrument determined to be normal is directly output to the application interaction unit, and the remaining life under normal wear and tear is calculated based on the accumulated usage time of the instrument, and the life assessment result is synchronously output to the application interaction unit;

[0026] S4. For the instrument temperature data determined to be abnormal (>normal temperature threshold), compare and analyze it with the device temperature at the same time point in the data comparison module. If the instrument temperature is ≤ the device temperature threshold, it is considered normal temperature and step S3 is executed. If the instrument temperature is > the device temperature threshold, it is determined to be abnormal temperature data.

[0027] S5. The abnormal temperature data finally determined is input into the life value judgment module for in-depth analysis. The accelerated aging coefficient is calculated based on the abnormal temperature value and duration, and the remaining service life of the instrument under abnormal working conditions is recalculated;

[0028] S6. The instrument life assessment results under abnormal temperature conditions, including abnormal life value indicators and corrected remaining service life, are displayed in a warning manner through the application interaction unit;

[0029] S7. Display the equipment temperature, instrument temperature, and instrument remaining life assessment results through the application interactive unit.

[0030] Furthermore, as a further improvement to the present technical solution, when the remaining service life of the meter is lower than the remaining service life of the meter under normal consumption, the application interaction unit will transmit the warning data including the abnormal life value indicator and the corrected remaining service life to the cloud analysis unit, and then transmit it to the perception collection unit via the communication transmission unit. After receiving the data, the remote instruction collection module in the perception collection unit waits for subsequent further operation instructions based on the warning situation.

[0031] When the management personnel need to obtain the data of the current equipment temperature, instrument temperature and remaining life of the instrument, the application interaction unit will reversely trigger the remote instruction. The instruction is first transmitted from the application interaction unit to the communication transmission unit, and then the communication transmission unit transmits the instruction to the remote instruction acquisition module. The remote instruction acquisition module receives the instruction and collects the equipment temperature data and instrument temperature data in real time. Then, the collected data is uploaded to the cloud analysis unit through the communication transmission unit for corresponding processing, and finally the processed data is transmitted back to the application interaction unit.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. In the cloud computing-based automated instrument remote management system, in terms of instrument temperature data judgment, a multi-level cloud analysis unit consisting of abnormal value filtering, data comparison and life value judgment modules is set up. First, abnormal data is preliminarily screened according to standards through the abnormal value filtering module, and then further identified by the data comparison module. Finally, in-depth evaluation is performed through the life value judgment module. This achieves multiple rounds of precise control of instrument temperature data from preliminary judgment to in-depth analysis, and meticulously classifies and processes abnormal instrument temperature situations, effectively avoiding the occurrence of misjudgments, greatly improving the accuracy of temperature judgment, and being able to more accurately grasp the real-time status of the instrument.

[0034] 2. In the cloud computing-based automated instrument remote management system, in terms of instrument life assessment, normal and abnormal temperature data are calculated based on the accumulated usage time and combined with factors such as abnormal temperature value and duration. In particular, the life value judgment module collects multiple key data from the input layer, and after preprocessing, feature extraction, model calculation, calibration optimization and other multi-level processing, it integrates multiple models to accurately calculate the remaining life and outputs a more comprehensive and reliable assessment report containing the remaining life prediction value and confidence interval, realizing more accurate assessment and control of the instrument life under different working conditions, and can provide a more valuable reference basis for operation and maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a block diagram of the overall system structure of the present invention;

[0036] Figure 2 This is a structural block diagram of the components assembled in the cloud analysis unit of the present invention.

[0037] The meaning of each number in the figure is:

[0038] 100, sensing and acquisition unit; 101, timing acquisition module; 102, event trigger acquisition module; 103, remote command acquisition module;

[0039] 200. Communication transmission unit;

[0040] 300, cloud analysis unit; 301, abnormal value filtering module; 302, data comparison module; 303, health value judgment module;

[0041] 400. Application interaction unit. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] At present, the problems of insufficiently detailed judgment of instrument temperature data and easy misjudgment, lack of detailed classification and processing steps for abnormal situations, and inaccurate evaluation of instrument life under different working conditions are solved. This invention provides an automated instrument remote management system based on cloud computing. Figure 1-Figure 2 As shown, it includes a perception collection unit 100, a communication transmission unit 200, a cloud analysis unit 300 and an application interaction unit 400;

[0044] The sensing and collecting unit 100 captures temperature data of industrial field equipment and instruments in real time, and the cloud analysis unit 300 receives the data transmitted by the communication transmission unit 200, and stores, cleans and analyzes the data, wherein:

[0045] The cloud analysis unit 300 includes an abnormal value filtering module 301, a data comparison module 302 and a life value judgment module 303. The abnormal value filtering module 301 is used to judge the instrument temperature. When the instrument temperature is ≤ the normal value, the instrument temperature is output through the application interaction unit 400. When the instrument temperature is greater than the normal value, the instrument temperature data is input into the data comparison module 302 for comparison with the device temperature data; when the instrument temperature is less than or equal to the device temperature, the instrument temperature is output through the application interaction unit 400. When the instrument temperature is greater than the device temperature, the instrument temperature data is input into the life value judgment module 303, and the instrument life at this moment is judged in combination with the historical life value test data; finally, the device temperature, instrument temperature and remaining instrument life are displayed through the application interaction unit 400.

[0046] The sensing and collection unit 100 includes a timing collection module 101, an event-triggered collection module 102, and a remote command collection module 103. The timing collection module 101 plays the role of regularly collecting instrument data in the system. Its operation process is as follows:

[0047] S1. Parameter setting: During the system initialization phase, the operation and maintenance personnel or system administrator will set a preset time interval for the timing acquisition module 101; this time interval can be adjusted according to actual needs and the specific conditions of the industrial site, for example, set to every 5 minutes, 10 minutes or 1 hour, etc.

[0048] S2. Start timer: After the module is started, the internal timer starts working and counts according to the preset time interval.

[0049] S3. Data reading: When the timer reaches the preset time interval, the timing acquisition module 101 will automatically communicate with the instrument at the industrial site and read the temperature data of the instrument using the corresponding communication protocol (such as Modbus, Profibus, etc.).

[0050] S4. Data upload: After reading the data, the module sends the data to the communication transmission unit 200 so as to be uploaded to the cloud analysis unit 300 for subsequent processing.

[0051] S5. Loop operation: After completing one data collection and upload, the timer restarts and waits for the arrival of the next time interval. This cycle repeats to achieve regular collection of instrument data.

[0052] The event-triggered acquisition module 102 is mainly used to proactively report data in a timely manner when the instrument temperature exceeds a set threshold. The operation steps are as follows:

[0053] S1. Threshold setting: During system configuration, a temperature threshold is set for the event triggering acquisition module 102 according to the normal operating range of the instrument and the safety requirements of industrial production, for example, 80°C.

[0054] S2. Real-time monitoring: The acquisition module 102 is triggered by events to monitor the temperature data of the instrument in real time.

[0055] S3. Threshold determination: The event triggering acquisition module 102 compares the real-time monitored instrument temperature with a set threshold.

[0056] S4. Event triggering: When the monitored instrument temperature exceeds a set threshold, the event triggering acquisition module 102 will immediately trigger an event, which will interrupt the normal timing acquisition process.

[0057] S5. Data reporting: After the event is triggered, the module will quickly collect the temperature data of the current instrument, including temperature value, collection time and other information, and actively report this data to the cloud analysis unit 300 through the communication transmission unit 200, so that the system can handle abnormal situations in a timely manner.

[0058] The remote instruction collection module 103 is used to upload the instrument data immediately upon receiving the reverse instruction from the application interaction unit 400. The operation process is as follows:

[0059] S1. Command reception: The remote command acquisition module 103 continuously monitors commands from the application interaction unit 400 ; the application interaction unit 400 is a terminal device (such as a computer, mobile phone, etc.) used by management personnel; when management personnel need to obtain instrument data in a timely manner, they will send commands to the remote command acquisition module 103 through the application interaction unit 400 .

[0060] S2. Instruction analysis: After receiving the instruction, the remote instruction acquisition module 103 will analyze the instruction, confirm the type and content of the instruction, and determine whether it is an instruction to upload data immediately.

[0061] S3. Data collection: If it is confirmed that the instruction requires immediate data upload, the remote instruction collection module 103 will immediately communicate with the instrument at the industrial site to read the current instrument temperature data.

[0062] S4. Data upload: After collecting the data, the module will quickly upload the data to the cloud analysis unit 300 through the communication transmission unit 200 to meet the management personnel's demand for real-time data.

[0063] In the cloud analysis unit 300, the abnormal value filtering module 301 is used to judge the instrument temperature data. During the system initialization phase, a normal temperature threshold is set in the abnormal value filtering module 301 based on the instrument design specifications, industrial production environment, and past operating experience. This threshold represents the upper temperature limit of the instrument under normal working conditions. For example, for certain types of instruments, the normal temperature threshold is set to 70°C.

[0064] By receiving the instrument temperature data transmitted from the sensing and collecting unit 100 through the communication transmission unit 200; this data includes the real-time collected temperature value of each instrument and the corresponding collection time and other information; each received instrument temperature data is compared with the set normal temperature threshold; if the instrument temperature is less than or equal to the normal temperature threshold, it means that the temperature of the instrument is within the normal range, and the normal instrument temperature data is directly sent to the application interaction unit 400 for display so that the management personnel can promptly understand the normal operation status of the instrument; if the instrument temperature is greater than the normal temperature threshold, it is considered that the instrument temperature may be abnormal, and the data with excessively high temperature are filtered out and input into the data comparison module 302 for further judgment;

[0065] At this point, the data comparison module 302 compares the instrument temperature data with an excessively high temperature (>normal temperature) with the device temperature at the same time. Based on the characteristics, operating conditions, and relevant safety standards of the industrial field equipment, a device temperature threshold is determined. This threshold reflects the upper limit of the normal temperature range that the equipment can withstand at the same time. For example, the device temperature threshold is set to 80°C. The excessively high instrument temperature data input by the abnormal value filtering module 301 is matched with the device temperature data at the same time, ensuring that the instrument and device temperatures at the same time are compared to ensure the accuracy and effectiveness of the comparison. The matched instrument temperature data is compared with the device temperature threshold. If the instrument temperature is less than or equal to the device temperature threshold, it indicates that the instrument temperature, although exceeding the normal temperature threshold, is still within the device's tolerable range. The matched instrument temperature data is considered normal temperature data and is output to the application interaction unit 400 for display. If the instrument temperature is greater than the device temperature threshold, the instrument temperature is determined to be abnormal temperature data and is input to the life value determination module 303 for recalculating and evaluating the remaining life of the instrument.

[0066] The life value judgment module 303 calculates the remaining life of the instrument under abnormal temperature data, including the following method steps:

[0067] Input layer: Receives abnormal temperature data (including the specific value of the abnormal temperature and the corresponding time information) from the data comparison module 302, obtains the historical production temperature test data set of the instrument from the system database (including the temperature changes under different working conditions), reads the nominal life parameters of the instrument, and obtains the current cumulative operating time of the instrument, that is, the total operating time of the instrument from the time it was put into use to the current moment;

[0068] Preprocessing layer: Check the units of input temperature data to ensure that all temperature data has consistent units. If there are temperature data with different units, convert them to a unified unit, for example, convert them to degrees Celsius (°C) to avoid calculation errors caused by inconsistent units. Convert the units of the time data involved to make all time data have the same unit. For example, convert the time information corresponding to the accumulated running time and temperature data to common time units such as hours or days to facilitate subsequent calculations and analysis.

[0069] The input data is then carefully checked to identify and remove outliers (interference during data collection, sensor failure data). These outliers are removed from the dataset using statistical methods (such as standard deviation-based methods) or reasonable ranges set based on experience to ensure data quality and reliability.

[0070] Feature extraction layer: Let the current abnormal temperature data be T current(Unit: °C), the calibration temperature of the instrument is T calibration (Unit: °C), the current temperature deviation from the calibration value ΔT is calculated as follows: ΔT = T cuttent -T calibration ,For example, if the current abnormal temperature is 85℃ and the calibration temperature value is 70℃, then ΔT=85-70=15℃;

[0071] Where ΔT represents the deviation of the current temperature from the calibration value, in degrees Celsius (°C), which reflects the degree of deviation of the current abnormal temperature from the instrument calibration temperature; T current Indicates the current abnormal temperature data in degrees Celsius (℃), that is, the actual instrument temperature value currently collected that exceeds the normal range; T calibration Refers to the calibration temperature value of the instrument, in degrees Celsius (℃), which is the standard reference temperature of the instrument under normal working conditions. It is determined by the instrument manufacturer based on factors such as instrument design and performance;

[0072] Then, from the historical production temperature test data set {Thistory}, by traversing all the temperature values in the data set, the maximum value T is found through the comparison algorithm. max and the minimum value T min ; Assume that the abnormal temperature data sequence is {T n}, the time series is {t n} (n represents the serial number of the data point), the absolute value of the difference between adjacent temperature data is greater than a set threshold (ΔT threshold ) to count the temperature fluctuation frequency f; first calculate the adjacent temperature difference sequence {ΔT n}:

[0073] ΔT n =|T n -T n-1 |(n≥2);

[0074] Among them, {Thistory} represents the historical production temperature test data set, which is a collection of temperature data of the recording instrument at each time point in the historical production process; T max Thistory represents the historical production temperature test data set, which is a collection of temperature data recorded by the instrument at various time points during the historical production process. min The minimum value found by the comparison algorithm from the historical production temperature test data set {Thistory} is the lowest temperature value experienced by the instrument during its historical operation, in °C; {T n} represents the abnormal temperature data sequence, which is a series of abnormal temperature data arranged in chronological order, where n represents the sequence number of the data point, T n Indicates the abnormal temperature value corresponding to the nth data point, in °C; {tn} represents the time series, and the abnormal temperature data series {T n} Correspondingly, record the time point of each abnormal temperature data collection, n represents the sequence number of the data point, t n Indicates the time corresponding to the nth data point; ΔT threshold Indicates the set temperature difference threshold, which is used to judge whether the temperature fluctuation is significant. When the absolute value of the difference between adjacent temperature data is greater than the threshold, it is considered that a significant temperature fluctuation has occurred. The unit is ℃; ΔT n Represents the nth value in the sequence of adjacent temperature differences;

[0075] Then statistics satisfy ΔT n >ΔT threshold The number N (within a certain time interval), assuming that the total length of the statistical time interval is T total (The unit is consistent with the time series, such as hours), then the calculation formula for the temperature fluctuation frequency f is:

[0076] Among them, N represents the time interval that satisfies ΔT n >ΔT threshold The number of times the temperature fluctuation exceeds the set threshold within the time interval; T total Indicates the total duration of the time interval selected when statistically analyzing temperature fluctuations. The unit is the same as the time series {t n} consistent (such as hours, minutes, etc.); f represents the frequency of temperature fluctuation;

[0077] For example, if the temperature difference between adjacent values is greater than 5°C 20 times within 10 hours, the temperature fluctuation frequency is

[0078] The calculation is based on the Arrhenius equation and the linear cumulative damage theory:

[0079] Assume the nominal life parameter of the instrument is L nominal (Unit: hours or other time units), the current cumulative running time is t accumulated (Unit: hours or time units consistent with nominal life), temperature acceleration factor A derived from the Arrhenius equation T (The calculation will be detailed in the subsequent model calculation layer, but it is assumed here that it has been obtained), then the base life loss rate L base The calculation formula is:

[0080] Among them, L base Represents the benchmark life loss rate, which is a proportional value that reflects the degree of loss of the instrument relative to the nominal life under the condition of considering the current cumulative operating time and temperature acceleration factor; taccumulated Indicates the current cumulative operating time of the instrument, in hours or a time unit consistent with the nominal life, that is, the total operating time from the time the instrument was put into use to the current moment; A T It represents the temperature acceleration factor, which is derived based on the Arrhenius equation and reflects the accelerating effect of temperature increase on the aging rate of the instrument. It is a dimensionless value. nominal Indicates the nominal life parameter of the instrument, in hours or other time units, provided by the instrument manufacturer, representing the designed service life of the instrument under normal working conditions;

[0081] For example: Instrument nominal life L nominal = 10,000 hours, cumulative operating time t accumulated =2000 hours, temperature acceleration factor A T =1.5, then: That is, the baseline life loss rate is 30%;

[0082] Model calculation layer: Calculate the temperature acceleration factor based on the Arrhenius model. The basic expression of the Arrhenius model is:

[0083] Where: k represents the reaction rate constant (which can be compared to the parameter related to aging rate in the context of instrument life assessment); A is the pre-exponential factor (also known as the frequency factor, which is a constant related to the specific substance and reaction); E a is the activation energy (a fixed value for specific instrument materials and aging-related chemical reactions, usually expressed in J / mol); R is the ideal gas constant, approximately 8.314 J / (mol·K); T is the absolute temperature (in K, converted as T(K) = t(°C) + 273.15);

[0084] When calculating the temperature acceleration factor A T When comparing the reaction rate constants k1 and k2 at two different temperatures T1 and T2 (corresponding to absolute temperatures T1 (K) and T2 (K)), the temperature acceleration factor A T It can be expressed as:

[0085]

[0086] Among them, A T It represents the temperature acceleration factor, which reflects the accelerating effect of temperature increase on the aging rate of the instrument. It is a dimensionless value. k1 represents the reaction rate constant at temperature T1 (corresponding to absolute temperature T1 (K)), which is used to measure the rate of instrument-related aging reactions and other processes at this temperature. k2 represents the reaction rate constant at temperature T2 (corresponding to absolute temperature T2 (K)), which is used to measure the rate of instrument-related aging reactions and other processes at this temperature.

[0087] For example, the activation energy E of a certain instrument material is known a =50000 J / mol, calculate the temperature acceleration factor A at the normal operating temperature T1 = 353.15 K (80°C) and the current abnormal temperature T2 = 373.15 K (100°C). T :

[0088]

[0089] A T ≈2.49 (round the result to two decimal places);

[0090] This means that under the current abnormal temperature, the aging speed of the instrument is about 2.49 times faster than that under normal temperature;

[0091] The cumulative thermal fatigue is evaluated by applying the Coffin-Manson model, where a common expression of the Coffin-Manson model is: N f =C·(Δε p ) -α ;

[0092] Among them, N f represents fatigue life (i.e., the number of cycles until failure of a material under given thermal cycling conditions); C and α are material-related constants, specifically determined through material testing and other methods; Δε p It is the plastic strain amplitude. When considering thermal fatigue caused by temperature changes, it is related to the temperature change amplitude; ΔT (here it can be understood as the temperature fluctuation range in each thermal cycle) and the thermal expansion coefficient β of the material, and can be approximately expressed as Δε r ≈β·ΔT (in practical applications, more complex geometric and mechanical factors may need to be considered for correction);

[0093] When evaluating cumulative thermal fatigue, the actual number of thermal cycles n experienced is usually calculated based on the temperature fluctuation frequency f (unit: times / hour or other time units) and the operating time t (unit: hours): n = f t;

[0094] Then, the above Coffin-Manson model is combined to evaluate the cumulative damage degree D. A simple calculation method based on the linear cumulative damage theory is (assuming that the damage caused by each thermal cycle is the same):

[0095] For example, if the C of a certain instrument material is 1000, α is 2, and the thermal expansion coefficient is β is 10 -5 / ℃, the temperature fluctuation frequency f = 10 times / hour in a certain period of time, the operating time t = 100 hours, and the average temperature change amplitude ΔT = 20℃, then:

[0096] First calculate the plastic strain amplitude Δε p ≈β·ΔT=10 -5 ×20=2×10 -4 ;

[0097] Then calculate the fatigue life N f =C·(Δε p ) -α =1000×(2×10 -4 ) -2 =2.5×10 8 ;

[0098] The actual number of thermal cycles experienced is n = f·t = 10 × 100 = 1000;

[0099] Cumulative damage

[0100] Finally, the revised remaining life is calculated comprehensively, and the baseline life loss rate is set to be L base (The calculation method has been introduced above), the temperature acceleration factor is A T , the damage factor corresponding to the cumulative thermal fatigue degree is D (which can be calculated based on the above Coffin-Manson model and other related calculations), and the corrected life loss rate; L modified The calculation formula can be expressed as follows (this is just a simple comprehensive consideration method, and in practice, parameters such as weights may be adjusted according to more accurate models and actual conditions): L modified =L base ×A T ×(1+D);

[0101] At this time, the remaining service life of the instrument under the current abnormal working condition is L remaining According to the nominal life of the instrument L nominal Calculated: L remaining =L nominal ×(1-L modified );

[0102] For example, if the nominal life of the instrument is known to be L nominal =5000 hours, base life loss rate L base =0.2, temperature acceleration factor A T =1.5, cumulative damage degree D = 0.1, then:

[0103] L modified =0.2×1.5×(1+0.1)=0.33;

[0104] L remaining =5000×(1-0.33)=3350 hours;

[0105] That is, the remaining service life of the instrument under the current abnormal operating conditions is approximately 3350 hours.

[0106] Calibration optimization layer: Combine historical test data to calibrate the prediction results through the correction function. Assume that the initial prediction result obtained by the model calculation layer is L predicted (e.g., remaining life prediction value, etc.), let the correction function be f(x), its input x can be a combination of multiple variables related to historical data, such as historical temperature data characteristics, historical life loss rate, etc. After being processed by the correction function, the calibrated prediction result L is obtained calibrated , the calculation formula can be expressed as: L calibrated =f(x)×L predicted ; Among them, the specific form of the correction function f(x) needs to be determined by fitting based on historical data;

[0107] For example, by performing regression analysis on a large amount of historical data (assuming linear regression is used), it is found that the historical temperature fluctuation range T fluctuation There is a linear relationship between the deviation ratio of actual life loss and predicted life loss, and the regression equation is y = a × T fluctuation +b (a and b are coefficients obtained by fitting historical data), where y can be used as a simple form of f(x), that is, f(x) = 1 + y = 1 + a × T fluctuation +b;

[0108] For example, the initial predicted remaining life L predicted = 1000 hours, and the current temperature fluctuation range T is obtained through statistical analysis of historical data fluctuation =5℃, and fitting a=0.05,b=0.1, then: f(x)=1+0.05×5+0.1=1.35; L calibrated =1.35×1000=1350 hours;

[0109] Then, the simple moving average method is used: let the original data sequence be {x n}(n=1,2,…,N, N is the total number of data points), the sliding window size is m(m<N), and the data sequence obtained after smoothing by simple moving average method is {y n}; For n ≥ m, the smoothed data y n The calculation formula is:

[0110] For example, there is a temperature data sequence {25, 26, 28, 30, 32, 35, 33, 31, 30, 28}, and the sliding window size is m = 3, then: (keep two and leave two); The entire smoothed data series is obtained by analogy. A 95% confidence interval for life is established. Assuming that the predicted remaining life after calibration and smoothing is μ (which can be regarded as the mean), the standard deviation of the remaining life is σ obtained from statistical analysis of historical data (for example, by calculating the sample standard deviation). The calculation formula for the 95% confidence interval (under normal distribution) is: (μ-1.96σ, μ+1.96σ).

[0111] For example, the mean value of the remaining life of the instrument after calibration and smoothing is μ = 800 hours. The standard deviation of the remaining life obtained through statistical analysis of historical data is σ = 50 hours. The confidence interval of the life with a 95% confidence level is:

[0112] (800-1.96×50,800+1.96×50)=(702,898) (hours);

[0113] This means that at a 95% confidence level, the remaining life of the instrument is likely to be between 702 hours and 898 hours.

[0114] Output layer: Outputs a complete evaluation report including the remaining life prediction value and confidence interval to the application interaction unit 400.

[0115] Furthermore, the application interaction unit 400 is used to display the device temperature data, the instrument temperature data, and the instrument remaining life data, wherein the acquisition of these data includes the following method steps:

[0116] S1. The sensing and collecting unit 100 collects device temperature data and instrument temperature data in real time, and uploads the data to the cloud analysis unit 300 via the communication transmission unit 200. The cloud analysis unit 300 directly processes the device temperature data and outputs the data to the application interaction unit 400 for visual display.

[0117] S2. The abnormal value filtering module 301 in the cloud analysis unit 300 starts working, obtains the uploaded instrument temperature data, and compares it with a pre-set normal temperature threshold. This normal temperature threshold is determined based on factors such as the normal operating range of the instrument, past operating experience, and industrial production requirements (for example, set to 70°C). If the instrument temperature is less than or equal to the pre-set normal temperature threshold, the instrument temperature data is determined to be normal temperature data and can proceed to the next step of processing. If the instrument temperature is greater than the pre-set normal temperature threshold, it is determined that the instrument temperature data may be abnormal and needs to be re-evaluated by the data comparison module 302.

[0118] S3. For the temperature data of the instrument determined to be normal, it is directly output from the cloud analysis unit 300 to the application interaction unit 400 for display, which is convenient for the management personnel to view intuitively. At the same time, the remaining life of the instrument under normal wear and tear conditions is calculated based on the accumulated usage time of the instrument. This calculation process may refer to the nominal life of the instrument (provided by the instrument manufacturer, for example, the nominal life is 10,000 hours) and the operating time, and the remaining life value is calculated using a corresponding calculation formula (for example, a simple linear wear model: remaining life = nominal life - operating time). This life assessment result is then also synchronously sent to the application interaction unit 400 for display, so that the management personnel can fully understand the relevant conditions of the instrument under normal operating conditions.

[0119] S4. For the instrument temperature data determined in step S2 to be greater than the normal temperature threshold, a secondary determination is performed in the data comparison module 302. The data comparison module 302 obtains the device temperature data at the same time for comparison and analysis. The device temperature threshold is also pre-set based on the normal operating parameters of the device, industrial safety standards, etc. (for example, set to 90°C). If the instrument temperature is less than or equal to the device temperature threshold, it means that although the instrument temperature exceeds the normal temperature range, it is still within the acceptable range for the device. In this case, the instrument temperature data is considered normal temperature data and processed according to step S3. If the instrument temperature is greater than the device temperature threshold, the instrument temperature data is considered to be truly abnormal temperature data and needs to be further input into the health value determination module 303 for in-depth analysis.

[0120] S5. When the instrument temperature data is determined to be abnormal, it is input into the life value judgment module 303. This module first obtains key information such as the abnormal temperature value of the instrument and the duration of the abnormal temperature. Then, based on this information and in combination with relevant theoretical models (such as the Arrhenius model and the Coffin-Manson model), it calculates the accelerated aging coefficient.

[0121] For example, the Arrhenius model can be used to quantify the accelerated impact of temperature on instrument aging based on the difference between abnormal and normal operating temperatures. This can then be used to recalculate the instrument's remaining service life under the current abnormal operating conditions, taking other factors into account. This provides a basis for a more accurate assessment of the instrument's health status.

[0122] S6. The instrument life assessment results under abnormal temperature conditions obtained after in-depth analysis by the life value judgment module 303, including abnormal life value indicators (e.g., quantitative indicators reflecting the deviation of the current health of the instrument from the normal state) and the corrected remaining service life, are transmitted by the cloud analysis unit 300 to the application interaction unit 400 and displayed in an early warning manner on the application interaction unit 400. This early warning display uses eye-catching color identification (e.g., red indicates abnormality), pop-up reminders, or chart highlights to attract the attention of management personnel, allowing them to promptly detect abnormal instrument conditions and take appropriate measures.

[0123] S7. The application interaction unit 400 displays the received device temperature data, instrument temperature data, and the instrument remaining life assessment results (including the remaining life under normal wear and abnormal operating conditions) obtained through different determination and calculation processes through an intuitive and friendly visual interface.

[0124] For example, by using charts (such as line charts showing the temperature trend over time, bar charts comparing the remaining life of different instruments, etc.), tables (listing the detailed temperature and remaining life values of each device and instrument, etc.) or dashboards (intuitively displaying the current status of key indicators), managers can easily view the overall situation at a glance, and then make reasonable operation and maintenance decisions based on these data to ensure the stable operation of related links of automation instruments in industrial production.

[0125] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. Cloud computing-based remote management system for automated instruments, characterized by: It comprises a perception collection unit (100), a communication transmission unit (200), a cloud analysis unit (300) and an application interaction unit (400); The temperature data of industrial field equipment and instruments is captured in real time by a sensing and collecting unit (100), the data transmitted by the communication transmission unit (200) is received by a cloud analysis unit (300), and the data is stored, cleaned, and analyzed, wherein: The cloud analysis unit (300) comprises an abnormal value filtering module (301), a data comparison module (302) and a life value judgment module (303). The abnormal value filtering module (301) is used to judge the instrument temperature. When the instrument temperature is less than or equal to the normal value, the instrument temperature is output through the application interaction unit (400). When the instrument temperature is greater than the normal value, the instrument temperature data is input into the data comparison module (302) for comparison with the device temperature data. When the instrument temperature is less than or equal to the device temperature, the instrument temperature is output through the application interaction unit (400). When the instrument temperature is greater than the device temperature, the instrument temperature data is input into the life value judgment module (303). The instrument life at this moment is judged in combination with historical life value test data. Finally, the device temperature, instrument temperature and remaining instrument life are displayed through the application interaction unit (400).

2. The cloud computing-based remote management system for automated instruments according to claim 1, characterized in that: The sensing collection unit (100) comprises a timing collection module (101), an event-triggered collection module (102), and a remote instruction collection module (103). The timing collection module (101) is used to automatically read instrument data at preset time intervals. When the instrument temperature exceeds a threshold, the event-triggered collection module (102) actively reports the data. When the application interaction unit (400) issues a reverse instruction, the remote instruction collection module (103) immediately uploads the data.

3. The cloud computing-based remote management system for automated instruments according to claim 2, characterized in that: The abnormal value filtering module (301) is used to judge the instrument temperature data if: Normal instrument temperature is directly output to the application interaction unit (400), while instrument temperature data with a temperature that is too high (>normal temperature) enters the data comparison module (302) for re-judgment.

4. The cloud computing-based remote management system for automated instruments according to claim 3, characterized in that: The data comparison module (302) is used to compare the instrument temperature data of the overly high temperature (>normal temperature) with the device temperature at the same time, if: Instrument temperature data that is less than or equal to the device temperature threshold is considered normal temperature and output to the application interaction unit (400), while instrument temperature data that is greater than or equal to the device temperature threshold is considered abnormal temperature and input to the life value judgment module (303) to recalculate the instrument life.

5. The cloud computing-based remote management system for automated instruments according to claim 4, characterized in that: The life value judgment module (303) is used to calculate the remaining life of the instrument under abnormal temperature data, and includes the following method steps: Input layer: Input the abnormal temperature data of the instrument, obtain the historical production temperature test data set of the instrument, read the nominal life parameters of the instrument and the current cumulative operating time; Preprocessing layer: Standardizes input data, including unifying temperature data units, converting time units, and removing outliers to ensure that data quality meets calculation requirements; Feature extraction layer: calculates the current temperature deviation from the calibration value, extracts historical temperature extremes, counts the temperature fluctuation frequency, and calculates the benchmark life loss rate; Model calculation layer: Calculate the temperature acceleration factor based on the Arrhenius model, apply the Coffin-Manson model to evaluate the cumulative thermal fatigue, and comprehensively calculate the corrected remaining life; Calibration optimization layer: The prediction results are calibrated by using a correction function based on historical test data, a sliding window algorithm is used to smooth data fluctuations, and a life confidence interval with a 95% confidence level is established; Output layer: outputs a complete evaluation report including the remaining life prediction value and confidence interval to the application interaction unit (400).

6. The cloud computing-based remote management system for automated instruments according to claim 5, characterized in that: The application interaction unit (400) is used to display device temperature data, instrument temperature data, and instrument remaining life data, wherein the acquisition of these data includes the following method steps: S1. Real-time device temperature data and instrument temperature data are collected through the sensing and collecting unit (100), and uploaded to the cloud analysis unit (300) through the communication transmission unit (200). The cloud analysis unit (300) directly processes the device temperature data and then outputs it to the application interaction unit (400) for visual display; S2. First, the instrument temperature data is judged by the abnormal value filtering module (301). If the instrument temperature is less than or equal to a preset normal temperature threshold, it is judged to be normal temperature data; If the instrument temperature is greater than the preset normal temperature threshold, the data comparison module (302) is entered for secondary determination; S3. The temperature data of the instrument determined to be normal temperature is directly output to the application interaction unit (400), and the remaining life under normal wear and tear is calculated based on the accumulated usage time of the instrument, and the life evaluation result is synchronously output to the application interaction unit (400); S4. For the instrument temperature data determined to be abnormal temperature (> normal temperature threshold), compare and analyze it with the device temperature at the same time point in the data comparison module (302). If the instrument temperature is less than or equal to the device temperature threshold, it is considered to be normal temperature and step S3 is executed. If the instrument temperature is greater than the device temperature threshold, it is determined to be abnormal temperature data. S5. The abnormal temperature data finally determined is input into the life value judgment module (303) for in-depth analysis, and the accelerated aging coefficient is calculated based on the abnormal temperature value and duration, and the remaining service life of the instrument under abnormal working conditions is recalculated; S6. The instrument life assessment result under abnormal temperature conditions, including the abnormal life value index and the corrected remaining service life, is displayed in a warning manner through the application interaction unit (400); S7. Displaying the equipment temperature, instrument temperature and instrument remaining life assessment results through the application interaction unit (400).

7. The cloud computing-based remote management system for automated instruments according to claim 6, characterized in that: When the remaining service life of the meter is lower than the remaining service life of the meter under normal consumption, the application interaction unit (400) transmits the warning data including the abnormal life value indicator and the corrected remaining service life in reverse to the cloud analysis unit (300), and then transmits the data to the perception collection unit (100) via the communication transmission unit (200). After receiving the data, the remote instruction collection module (103) in the perception collection unit (100) waits for subsequent further operation instructions based on the warning situation.

8. The cloud computing-based remote management system for automated instruments according to claim 6, characterized in that: When the management personnel need to obtain the data of the current device temperature, instrument temperature and the remaining life of the instrument, the application interaction unit (400) will reversely trigger the remote instruction, and the instruction is first transmitted from the application interaction unit (400) to the communication transmission unit (200), and then the communication transmission unit (200) transmits the instruction to the remote instruction acquisition module (103), which receives the instruction through the remote instruction acquisition module (103) and collects the device temperature data and the instrument temperature data in real time. Then, the collected data is uploaded to the cloud analysis unit (300) through the communication transmission unit (200) again for corresponding processing, and finally the processed data is transmitted back to the application interaction unit (400).

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