Intelligent control processing system data processing method

Abstract

The invention discloses an intelligent control processing system data processing method, and relates to the technical field of intelligent control processing, and the method comprises the steps: measuring the concentration of PM10 dust particles in an air environment of a low-temperature valve processing system, calculating to obtain the concentration of trend dust particles in the low-temperature valve processing environment, calculating to obtain a dust particle prediction value, and calculating to obtain a dust particle prediction result. When the predicted value of the dust particles is greater than 100g / m, sending out dust exceeding early warning; the continuous operation duration and the dust exceeding early warning frequency of the low-temperature valve machining system are obtained, the trend continuous operation duration and the trend dust exceeding early warning frequency of the low-temperature valve are obtained through calculation, and the precision reduction risk index of the machining system is obtained through calculation; the method comprises the following steps: acquiring a central axis offset degree # imgabs0 # and a dimensional deviation of a low-temperature valve produced by a low-temperature valve processing system, calculating to obtain a trend central axis offset degree and a trend dimensional deviation, calculating to obtain a precision reduction index of the low-temperature valve processing system, and selecting a processing scheme according to the value of the precision reduction index.

Description

Technical Field

[0001] The present invention relates to the technical field of valve processing system data, and in particular to a data processing method for an intelligent control processing system. Background Art

[0002] Valves, as important nodes in industrial pipelines, control the gateways between pipelines and between pipelines and the outside world, and play an irreplaceable and important role in the safety of the entire pipeline. Cryogenic valves have even more performance and safety requirements for valves, so valve processing quality control requires special attention. Traditional valve processing often relies on manpower for quality control, resulting in large errors and low efficiency.

[0003] A Chinese invention application, publication number CN118884841A, discloses a data processing method for an intelligent controlled machining system. The method includes the following steps: collecting multidimensional data from machining equipment and the environment; performing feature transformation on the data using a nonlinear mapping function; and predicting the future machining state using the mapped feature vectors via a time-evolution network. Based on the predicted future machining state, a nonlinear feedback network is used to dynamically adjust machining parameters and optimize the control strategy. A global optimization mechanism is used to globally evaluate and adjust multiple time points to ensure that the intelligent controlled machining system maintains its optimal state at different stages. This method utilizes a distributed multi-sensor network to collect multidimensional data from the equipment, workpiece, and environment in real time, ensuring comprehensive monitoring of the machining system's operating parameters, workpiece status, and environmental variables.

[0004] In the above invention application, environmental data of the processing equipment is collected, noise reduction and eigenvalue extraction and conversion are performed on the data, and a nonlinear function mapping method is used to evaluate data processing and monitor the status of the processing system. However, the timeliness of the data is not high, the weight of the latest data is insufficient, and the magnitude difference between different data is not considered.

[0005] To this end, the present invention provides a data processing method for an intelligent control processing system. Summary of the Invention

[0006] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a data processing method for an intelligent control processing system, which processes data through exponentially weighted moving average to make the data more timely, while reducing the impact of short-term fluctuations on the overall data. The standardized processing of the data narrows the gaps in dimensions and orders of magnitude between different indicators, eliminates differences, and takes the risk of reduced accuracy in dust particle concentration analysis. The accuracy of the low-temperature valve processing system is evaluated through center axis offset and dimensional deviation. It can promptly detect the situation where the dust particle concentration in the low-temperature valve processing environment is too high, analyze the risk of reduced accuracy, and finally evaluate the accuracy of the processing system, reducing losses caused by unnecessary shutdowns and maintenance, thereby solving the technical problems recorded in the background technology.

[0007] (2) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A data processing method for an intelligent control processing system, comprising the following steps: Measuring PM10 dust particle concentration in the air environment of a cryogenic valve processing system , calculate the trend dust particle concentration in the cryogenic valve processing environment , according to the trend of dust particle concentration Calculate the dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued; Obtain the continuous operation time of the cryogenic valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times , and calculate the risk index of machining system precision reduction ; Obtain the center axis deviation of cryogenic valves produced by cryogenic valve processing system and size deviation , calculate the trend center axis deviation and trend size deviation , and calculate the precision reduction index of the cryogenic valve processing system , according to the precision reduction index The value of is used to select the corresponding processing scheme.

[0008] Furthermore, the PM10 dust particle concentration in the air environment of the processing system is measured using a PM10 meter. , calculate the trend dust particle concentration in the cryogenic valve processing environment :

[0009] in, j Indicates the time sequence number of each data. j =1, 2, ..., n , n Is a positive integer, the latest monitoring result .

[0010] Furthermore, obtain the trend of dust particle concentration of cryogenic valves , calculate the dust particle concentration change rate :

[0011] The dust particle concentration change rate The calculation formula is as above.

[0012] Further, obtain the dust particle concentration and dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued:

[0013] When the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm indicating that the dust exceeds the standard will be issued and the HEPA filter will be activated to remove dust from the air environment of the processing system.

[0014] It's important to note that when PM10 concentrations exceed 100µg / m³, they can impact industrial equipment, causing maintenance issues and reduced efficiency, and can also affect optical equipment, reducing accuracy. HEPA filters are high-efficiency particulate air filters that effectively remove over 99.77% of particles 0.3 microns and larger.

[0015] Furthermore, obtain the continuous operation time of the cryogenic valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times :

[0016] in, j Indicates the time sequence number of each data. j =1, 2, ..., n ,n A positive integer, the latest monitoring result , .

[0017] Furthermore, the usage time of the processing system is obtained based on the processing system work records. and humidity warning times , calculate the risk index of machining system precision reduction :

[0018] When the machining system accuracy decreases, the risk index ≥ When the error is detected, it indicates that the cryogenic valve processing system has a greater risk of accuracy reduction, and the cryogenic valve processing system needs to be tested and an accuracy analysis performed.

[0019] Furthermore, the center axis deviation of the cryogenic valve produced by the cryogenic valve processing system was measured by a micrometer and a three-coordinate measuring machine. and size deviation , calculate the trend center axis deviation and trend size deviation :

[0020] in, i Indicates the time sequence number when each sample valve is extracted. i =1, 2, ..., m , m A positive integer, the latest monitoring result , .

[0021] Furthermore, the trend of the center axis deviation of the cryogenic valve produced by the cryogenic valve processing system is obtained. and trend size deviation , calculate the precision reduction index of cryogenic valve processing system , according to the precision reduction index The value to select the processing scheme:

[0022] when ≥ ( ) When the low-temperature valve processing system is used, the production accuracy is low, and the low-temperature valves produced are prone to problems. The system stops running and performs maintenance, and the dust on each measuring and positioning component is handled; when( )≤ < ( ) reflects that the production accuracy of the cryogenic valve processing system is general and needs to be strengthened; when <( ), it reflects that the production accuracy of the cryogenic valve is high and no measures are needed.

[0023] in, Reduce the index for historical accuracy The mean of Reduce the index for historical accuracy The variance of .

[0024] (3) Beneficial effects The present invention provides a data processing method for a cryogenic valve intelligent control processing system, which has the following beneficial effects: 1. Data is processed through exponentially weighted moving average, making the data more timely while reducing the impact of short-term fluctuations on the overall data. The standardized processing of data narrows the gaps in dimensions and orders of magnitude between different indicators and eliminates differences.

[0025] 2. By measuring the PM10 dust particle concentration in the air environment of the cryogenic valve processing system , calculate the trend dust particle concentration in the cryogenic valve processing environment , according to the trend of dust particle concentration Calculate the dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued to indicate that the dust exceeds the standard. This can promptly detect excessive dust in the processing system's air environment and allow for timely dust removal, helping to maintain the air quality in the low-temperature valve processing system and ensure the accuracy of optical instruments.

[0026] 3. Obtain the continuous operation time of the low-temperature valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times , calculate and obtain the risk index of machining system precision reduction , can timely discover the risk of reduced precision of the cryogenic valve processing system, help to evaluate the reduction in precision caused by failure of the processing system, and reduce unnecessary losses caused by the continuous operation of the cryogenic valve processing system under low precision.

[0027] 4. Obtain the center axis deviation of the cryogenic valve produced by the cryogenic valve processing system and size deviation , calculate the trend center axis deviation and trend size deviation , calculate the precision reduction index of cryogenic valve processing system , according to the precision reduction index The value of the low-temperature valve processing system can be used to select the treatment plan, which can effectively judge the situation of low-temperature valve precision reduction produced by the low-temperature valve processing system. To choose whether to stop operation for maintenance, it is helpful for the low-temperature valve processing system to continue to operate with high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a structural diagram of a data processing method for an intelligent control processing system according to the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] See also Figure 1 The present invention provides a data processing method for an intelligent control processing system, comprising the following steps: Step 1: Measure the PM10 dust particle concentration in the air environment of the cryogenic valve processing system , calculate the trend dust particle concentration in the cryogenic valve processing environment , according to the trend of dust particle concentration Calculate the dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued indicating that the dust exceeds the standard.

[0031] The step 1 includes the following contents: Step 101: Use a PM10 measuring instrument to measure the PM10 dust particle concentration in the air environment of the processing system. , calculate the trend dust particle concentration in the cryogenic valve processing environment :

[0032] in, j Indicates the time sequence number of each data. j =1, 2, ..., n , n Is a positive integer, the latest monitoring result .

[0033] Step 102: Obtain the trend dust particle concentration of the cryogenic valve , calculate the dust particle concentration change rate :

[0034] Step 103: Obtain dust particle concentration and dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued:

[0035] When the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm indicating that the dust exceeds the standard will be issued and the HEPA filter will be activated to remove dust from the air environment of the processing system.

[0036] It's important to note that when PM10 concentrations exceed 100µg / m³, they can impact industrial equipment, causing maintenance issues and reduced efficiency, and can also affect optical equipment, reducing accuracy. HEPA filters are high-efficiency particulate air filters that effectively remove over 99.77% of particles 0.3 microns and larger.

[0037] When using, combine the contents in steps 101 to 103: By measuring the PM10 dust particle concentration in the air environment of the cryogenic valve processing system , calculate the trend dust particle concentration in the cryogenic valve processing environment , according to the trend of dust particle concentration Calculate the dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued to indicate that the dust exceeds the standard. This can promptly detect excessive dust in the processing system's air environment and allow for timely dust removal, helping to maintain the air quality in the low-temperature valve processing system and ensure the accuracy of optical instruments.

[0038] Step 2: Obtain the continuous operation time of the cryogenic valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times , and calculate the risk index of machining system precision reduction .

[0039] The second step includes the following contents: Step 201: Obtain the continuous operation time of the cryogenic valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times :

[0040] in, j Indicates the time sequence number of each data. j =1, 2, ..., n , n A positive integer, the latest monitoring result , .

[0041] Step 202: Obtain the usage time of the processing system based on the processing system work record and humidity warning times , calculate the risk index of machining system precision reduction :

[0042] When the machining system accuracy decreases, the risk index ≥ When the error is detected, it indicates that the cryogenic valve processing system has a greater risk of accuracy reduction, and the cryogenic valve processing system needs to be tested and an accuracy analysis performed.

[0043] When using, combine the contents in step 201 to step 202: By obtaining the continuous operation time of the low temperature valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times , and calculate the risk index of machining system precision reduction , can timely discover the risk of reduced precision of the cryogenic valve processing system, help to evaluate the reduction in precision caused by failure of the processing system, and reduce unnecessary losses caused by the continuous operation of the cryogenic valve processing system under low precision.

[0044] Step 3: Obtain the center axis deviation of the cryogenic valve produced by the cryogenic valve processing system and size deviation , calculate the trend center axis deviation and trend size deviation , and calculate the precision reduction index of the cryogenic valve processing system , according to the precision reduction index The value of is used to select the corresponding processing scheme.

[0045] The step three includes the following contents: Step 301: Measure the center axis deviation of the cryogenic valve produced by the cryogenic valve processing system using a micrometer and a three-dimensional coordinate measuring machine. and size deviation , calculate the trend center axis deviation and trend size deviation :

[0046] in, i Indicates the time sequence number when each sample valve is extracted. i =1, 2, ..., m , m A positive integer, the latest monitoring result , .

[0047] Step 302: Obtain the trend center axis deviation of the cryogenic valve produced by the cryogenic valve processing system and trend size deviation , calculate the precision reduction index of the cryogenic valve processing system , according to the precision reduction index The value of % selects the corresponding processing solution:

[0048] when ≥ ( ) When the low-temperature valve processing system is used, the production accuracy is low, and the low-temperature valves produced are prone to problems. The system stops running and performs maintenance, and the dust on each measuring and positioning component is handled; when( )≤ < ( ) reflects that the production accuracy of the cryogenic valve processing system is general and needs to be strengthened; when <( ), it reflects that the production accuracy of the cryogenic valve is high and no measures are needed.

[0049] in, Reduce the index for historical accuracy The mean of Reduce the index for historical accuracy The variance of .

[0050] When using, combine the contents in step 301 to step 302: By obtaining the center axis deviation of the cryogenic valve produced by the cryogenic valve processing system and size deviation , calculate the trend center axis deviation and trend size deviation , and calculate the precision reduction index of the cryogenic valve processing system , according to the precision reduction index The value of the low-temperature valve processing system can be used to select the treatment plan, which can effectively judge the situation of low-temperature valve precision reduction produced by the low-temperature valve processing system. To choose whether to stop operation for maintenance, it is helpful for the low-temperature valve processing system to continue to operate with high precision.

[0051] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0052] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0053] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A data processing method for a cryogenic valve intelligent control processing system, characterized in that: The following steps are involved: Measuring PM10 dust particle concentration in the air environment of a cryogenic valve processing system , calculate the trend dust particle concentration in the cryogenic valve processing environment , according to the trend of dust particle concentration Calculate the dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued; Obtain the continuous operation time of the cryogenic valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times , and calculate the risk index of machining system precision reduction ; Obtain the center axis deviation of cryogenic valves produced by cryogenic valve processing system and size deviation , calculate the trend center axis deviation and trend size deviation , and calculate the precision reduction index of the cryogenic valve processing system , according to the precision reduction index The value of is used to select the corresponding processing scheme.

2. The data processing method of an intelligent control processing system according to claim 1, characterized in that: Use PM10 measuring instrument to measure the PM10 dust particle concentration in the air environment of the processing system , calculate the trend dust particle concentration in the cryogenic valve processing environment : in, j Indicates the time sequence number of each data. j =1, 2, ..., n , n Is a positive integer, the latest monitoring result .

3. The data processing method of an intelligent control processing system according to claim 1, characterized in that: Obtain trending dust particle concentrations for cryogenic valves , calculate the dust particle concentration change rate : The dust particle concentration change rate The calculation formula is as above.

4. The data processing method of an intelligent control processing system according to claim 1, characterized in that: Get dust particle concentration and dust particle concentration change rate , calculate the dust particle prediction value , when the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm will be issued: When the dust particle prediction value When the dust concentration is greater than 100µg / m³, an alarm indicating that the dust exceeds the standard will be issued and the HEPA filter will be activated to remove dust from the air environment of the processing system.

5. The data processing method of an intelligent control processing system according to claim 1, characterized in that: Obtain the continuous operation time of the cryogenic valve processing system Number of warnings for dust exceeding the standard , calculate the trend of continuous operation time of cryogenic valve and trend of dust exceeding standard warning times : in, j Indicates the time sequence number of each data. j =1, 2, ..., n , n A positive integer, the latest monitoring result , .

6. The data processing method of an intelligent control processing system according to claim 1, characterized in that: Obtain the usage time of the processing system based on the processing system work records and humidity warning times , calculate the risk index of machining system precision reduction : When the machining system accuracy decreases, the risk index ≥ When the error is detected, it indicates that the cryogenic valve processing system has a greater risk of accuracy reduction, and the cryogenic valve processing system needs to be tested and an accuracy analysis performed.

7. The data processing method of an intelligent control processing system according to claim 1, characterized in that: The center axis deviation of the cryogenic valve produced by the cryogenic valve processing system is measured by a micrometer and a three-coordinate measuring machine. and size deviation , calculate the trend center axis deviation and trend size deviation : in, i Indicates the time sequence number when each sample valve is extracted. i =1, 2, ..., m , m A positive integer, the latest monitoring result , .

8. The data processing method of an intelligent control processing system according to claim 1, characterized in that: Obtain the trend center axis deviation of cryogenic valves produced by cryogenic valve processing system and trend size deviation , calculate the precision reduction index of cryogenic valve processing system , according to the precision reduction index The value to select the processing scheme: when ≥ ( ) reflects that the production accuracy of the cryogenic valve processing system is low; when( )≤ < ( ) reflects that the production accuracy of the cryogenic valve processing system is average; when <( ), it reflects the high production accuracy of cryogenic valves.

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