Steam valve group valve performance optimization data transmission system and method based on big data

Abstract

The application discloses a steam valve group valve performance optimization data transmission system and method based on big data, relates to the technical field of valve group valve performance optimization, and comprises the following steps: identifying the flow control state of a steam valve group valve to obtain flow control data, and performing abnormality evaluation on the flow control state of the steam valve group valve; analyzing abnormal causes of abnormal steam valve group valve flow control, and optimizing the performance of the abnormal steam valve group valve; evaluating the node performance of a transmission node in a current period to obtain a target transmission node, transmitting performance optimization data to the target transmission node; obtaining the performance optimization data in the target transmission node, verifying the integrity of the performance optimization data, and sending the verified performance optimization data to a staff member through a platform, so that the performance of the steam valve group valve is optimized, and the performance optimization data of the steam valve group valve is safely and quickly transmitted.

Description

Technical Field

[0001] This invention relates to the field of valve assembly and valve component performance optimization technology, specifically a data transmission system and method for steam valve assembly and valve component performance optimization based on big data. Background Technology

[0002] Steam valve assemblies are core control devices in the industrial steam field. Common steam valve assemblies include check valves, air vent valves, and steam traps. These valves are responsible for precisely regulating the flow rate, pressure, direction, and temperature of steam to ensure the safety of the steam system and a stable steam supply. They also prevent damage to other equipment caused by steam backflow. In short, steam valve assemblies are essential devices for ensuring the safe, stable, and efficient operation of steam systems.

[0003] Therefore, it is especially important to ensure that the performance of steam valve assembly components is normal and efficient during use. In the performance optimization of steam valve assembly components, the precise control of steam flow is of paramount importance. Steam is often used for heating. Fluctuations in steam flow will lead to unstable heating temperature, which will not only affect the quality of products and turn a large number of products into scrap, but may also cause a series of safety problems.

[0004] In addition, since the adjustment and optimization of steam valve assembly components require the rapid and accurate acquisition of performance optimization data, traditional data transmission methods are not only time-consuming, but also have different conditions for different transmission nodes, making it impossible to find a suitable transmission node based on the actual situation. Incorrect selection of transmission nodes will not only waste data transmission time, but may even lead to data transmission failure, affecting the progress of steam valve assembly component performance optimization, and may even pose significant safety hazards. Summary of the Invention

[0005] The purpose of this invention is to provide a data transmission system and method for optimizing the performance of steam valve assembly components based on big data, so as to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data transmission method for optimizing the performance of steam valve assembly components based on big data, the method comprising: Step S100: Obtain the flow control monitoring record of the steam valve group valves in the current cycle, identify the flow control status of the steam valve group valves, obtain flow control data, and based on the flow control data, evaluate the abnormality of the flow control status of the steam valve group valves to obtain abnormal steam valve group valves. Step S200: Based on the flow control monitoring records, obtain the reference abnormality cause data of the steam valve group valves, analyze the abnormal causes of the flow control of the abnormal steam valve group valves, obtain the performance optimization reference data from the platform, and optimize the performance of the abnormal steam valve group valves to obtain the performance optimization data. Step S300: Obtain performance optimization data of abnormal steam valve group valves, obtain node performance records of transmission nodes in the platform, evaluate the node performance of transmission nodes in the current cycle, obtain the target transmission node, and transmit the performance optimization data to the target transmission node. Step S400: Obtain performance optimization data from the target transmission node, verify the completeness of the performance optimization data, and send the verified performance optimization data to the staff through the platform.

[0007] Furthermore, step S100 includes: Step S101: Acquire the flow control monitoring records of the steam valve group valves and perform data preprocessing on the flow control monitoring records; Step S102: Identify the flow control status of the steam valve assembly components to obtain flow control data. The specific identification process is as follows: Obtain the steam density ρ of the steam valve assembly at time point a within the current cycle from the flow control monitoring records. a Actual traffic volume Q´ a The pressure difference ΔP before and after the valve a Obtain the theoretical flow rate Q of the steam valve assembly at time point a. a ; Step S103: Calculate the flow deviation δ of the steam valve assembly at the a-th time point within the current cycle. a =|Q a -Q´ a | / Q a ; Calculate the steady-state flow deviation G of the steam valve assembly during the current cycle: , Where n is the total number of valves in the steam valve group at any point in time within the current cycle; δ i This represents the flow deviation value of the steam valve assembly at the i-th time point within the current cycle. Step S104: Obtain the average value Q´ of the measured flow rate in the steam valve assembly, and calculate the flow fluctuation value σ of the steam valve assembly. , Among them, Q´ i The measured flow rate at the i-th time point in the steam valve assembly; Calculate the flow fluctuation rate of the steam valve assembly in the current cycle: B = σ / Q´; The flow fluctuation rate and steady-state flow deviation of the steam valve group components are collected to obtain flow control data; Step S104: When the flow fluctuation rate or steady-state flow deviation value in the flow control data is greater than the preset threshold, it is determined that the flow control status of the steam valve group valve in the current cycle is abnormal, and the steam valve group valve is recorded as an abnormal steam valve group valve.

[0008] Furthermore, step S200 includes: Step S201: Based on the flow control monitoring records, analyze the abnormal causes of flow control abnormalities in the steam valve group. The specific analysis process includes: Obtain the theoretical flow coefficient C of the abnormal steam valve assembly. v Obtain the actual flow coefficient C´ of the abnormal steam valve group valve in the current cycle from the flow control monitoring record. v ; Calculate the flow coefficient distortion rate F = C' of abnormal steam valve assembly components. v / C v ; Step S202: Input a preset input signal to the valve components of the abnormal steam valve group through the platform, and obtain the maximum difference Δθ between the output positions of the upward and downward signals under the same input signal. max Calculate the hysteresis rate H of the abnormal steam valve group in the current cycle: H = (Δθ) max ( / 100)×100%; Step S203: Obtain the average pressure difference μ across the valve of the abnormal steam valve group at various time points within the current cycle from the flow control monitoring record. (△,P) and standard deviation σ (△,P) Calculate the differential pressure fluctuation value W of the abnormal steam valve group valves. (△,P) =(σ (△,P) / μ (△,P) )×100%; Step S204: Obtain the high-frequency energy E of the abnormal steam valve group valve in the current cycle from the flow control monitoring record. high and low-frequency energy E low Calculate the cavitation energy ratio I=E from the flow control monitoring records. high / E low ; Step S205: Use the flow coefficient distortion rate, execution hysteresis rate, differential pressure fluctuation value and cavitation energy ratio of the abnormal steam valve group valve in the current cycle as the abnormal detection parameters of the abnormal steam valve group valve in the current cycle. Obtain reference abnormality cause data for abnormal steam valve assembly components. The reference abnormality cause data includes the relationship between the abnormal detection parameters and preset thresholds in the abnormal steam valve assembly components when various abnormal causes occur. The relationship between several abnormal detection parameters corresponding to a certain abnormal cause and a preset threshold is obtained. When the values ​​of several abnormal detection parameters of the abnormal steam valve group in the current period all meet the relationship between several abnormal detection parameters corresponding to a certain abnormal cause and the preset threshold, it is determined that a certain abnormal cause has occurred in the current period. The abnormal cause is recorded as the characteristic abnormal cause of the abnormal steam valve group. The characteristic abnormal causes of the abnormal steam valve group are obtained. Step S206: Obtain performance optimization comparison data from the platform. The performance optimization comparison data includes the platform's preset performance optimization strategies when various abnormal causes occur in the steam valve group valves. The performance of abnormal steam valve assembly components is optimized. The specific optimization process is as follows: Based on the performance optimization comparison data, the performance optimization strategies corresponding to the abnormal causes of each characteristic are obtained. The performance optimization strategies corresponding to the abnormal causes of each characteristic are sorted and collected to obtain the performance optimization data of the steam valve group valves.

[0009] Furthermore, step S300 includes: Step S301: Obtain each transmission node in the platform, obtain the node performance record of each transmission node in the current period; Step S302: Obtain the average values ​​of bandwidth, latency, and packet loss rate of the transmission node from the node performance records. Standardize the bandwidth, latency, and packet loss rate of the transmission node to obtain the labeled values ​​X of bandwidth, latency, and packet loss rate of the transmission node. α norm X β norm and X γ norm ; Step S303: Calculate the node performance score Y=λ of the transmission node. α ·X α norm +λ β ·X β norm +λ γ ·X γ norm , where λ α , λ β and λ γ These are preset proportional coefficients for the bandwidth, latency, and packet loss rate of the transmission node, respectively; λ α+λ β +λ γ =1, λ α , λ β and λ γ All are greater than 0; Step S304: Obtain the node performance score of each transmission node in the current period, and select the transmission node with the maximum node performance score as the target transmission node, and transmit the performance optimization data of the abnormal steam valve group valve to the target transmission node. The above steps are because different transmission nodes will have different conditions in the current cycle. If the wrong transmission node is selected, it will not only lead to longer transmission time, but also affect data security. By scoring the performance of transmission nodes, data security and transmission efficiency can be guaranteed to the greatest extent.

[0010] Furthermore, step S400 includes: Step S401: Obtain the performance optimization data of the abnormal steam valve group valve in the target transmission node, and the hash value h´ of the performance optimization data in the target transmission node; Step S402: Verify the integrity of the performance optimization data. The specific verification process is as follows: obtain the hash value h of the performance optimization data before transmission. When the hash value h' is equal to the hash value h, it is determined that the performance optimization data in the target transmission node is transmitted completely. Step S403: The verified performance optimization data is sent to the personnel managing the steam valve group through the platform, prompting them to perform performance optimization on the abnormal steam valve group components.

[0011] To better implement the above methods, a data transmission system for optimizing the performance of steam valve assembly components based on big data was also proposed. The system includes an anomaly assessment module, a performance optimization module, a performance optimization data transmission module, and a data verification module. The anomaly assessment module is used to assess the anomaly status of the flow control state of the steam valve group components and identify abnormal steam valve group components. The performance optimization module is used to optimize the performance of abnormal steam valve group components and obtain performance optimization data; The performance optimization data transmission module is used to evaluate the node performance of the transmission node in the current period, obtain the target transmission node, and transmit the performance optimization data to the target transmission node. The data verification module is used to verify the completeness of performance optimization data and then send the verified performance optimization data to staff through the platform.

[0012] Furthermore, the anomaly assessment module includes a flow control identification unit and an anomaly assessment unit; The flow control identification unit is used to identify the flow control status of the valve components in the steam valve group and obtain flow control data. The anomaly assessment unit is used to assess the anomaly status of the steam valve group valves based on the flow control data, and to identify abnormal steam valve group valves.

[0013] Furthermore, the performance optimization module includes an anomaly analysis unit and a performance optimization unit; The anomaly analysis unit is used to acquire reference anomaly cause data for steam valve group components and analyze the anomaly causes of flow control anomalies in abnormal steam valve group components. The performance optimization unit is used to acquire performance optimization comparison data and optimize the performance of abnormal steam valve group components to obtain performance optimization data.

[0014] Furthermore, the performance optimization data transmission module includes a node performance evaluation unit and a performance optimization data transmission unit; The node performance evaluation unit is used to acquire the node performance records of the transmission nodes within the platform, evaluate the node performance of the transmission nodes in the current period, and obtain the target transmission node. The performance optimization data transmission unit is used to acquire the target transmission node in the platform and transmit the performance optimization data of the abnormal steam valve group to the target transmission node.

[0015] Furthermore, the data verification module includes a data verification unit; The data verification unit is used to verify the completeness of the performance optimization data. After verification, the performance optimization data is sent to the personnel managing the steam valve group through the platform, prompting them to perform performance optimization on abnormal steam valve group components.

[0016] Compared with the prior art, the beneficial effects of the present invention are: the present invention realizes the optimization of the performance of steam valve group components and the secure and rapid transmission of steam valve group component performance optimization data. By identifying the flow control status of the steam valve group components, abnormal conditions of the steam valve group components are determined, and the causes of the abnormalities are analyzed to formulate specific performance optimization data, thereby optimizing the steam valve group components and ensuring their efficient and safe operation. Furthermore, based on practical considerations, the optimal transmission node is selected from the platform for transmission, effectively ensuring transmission efficiency and safety. Attached Figure Description

[0017] Figure 1 This is a flowchart of the data transmission method for optimizing the performance of steam valve assembly components based on big data, as described in this invention. Figure 2 This is a schematic diagram of the data transmission system for optimizing the performance of steam valve groups based on big data, as described in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: Figures 1-2 As shown, this invention provides a technical solution: a data transmission method for optimizing the performance of steam valve assembly components based on big data. The method includes: Step S100: Obtain the flow control monitoring record of the steam valve group valves in the current cycle, identify the flow control status of the steam valve group valves, obtain flow control data, and based on the flow control data, evaluate the abnormality of the flow control status of the steam valve group valves to obtain abnormal steam valve group valves. Step S100 includes: Step S101: Acquire the flow control monitoring records of the steam valve group valves and perform data preprocessing on the flow control monitoring records; For example, data preprocessing includes outlier removal and time alignment; Step S102: Identify the flow control status of the steam valve assembly components to obtain flow control data. The specific identification process is as follows: Obtain the steam density ρ of the steam valve assembly at time point a within the current cycle from the flow control monitoring records. a Actual traffic volume Q´ a The pressure difference ΔP before and after the valve a Obtain the theoretical flow rate Q of the steam valve assembly at time point a. a ; For example, the theoretical flow rate Q of the steam valve assembly at time point a. a The specific calculation formula is as follows: , Where K is the valve flow coefficient of the steam valve assembly; ρ a 0 represents the density of steam under standard conditions at the a-th time point; Step S103: Calculate the flow deviation δ of the steam valve assembly at the a-th time point within the current cycle. a =|Q a -Q´ a | / Q a ; Calculate the steady-state flow deviation G of the steam valve assembly during the current cycle: , Where n is the total number of valves in the steam valve group at any point in time within the current cycle; δ i This represents the flow deviation value of the steam valve assembly at the i-th time point within the current cycle. Step S104: Obtain the average value Q´ of the measured flow rate in the steam valve assembly, and calculate the flow fluctuation value σ of the steam valve assembly. , Among them, Q´ i The measured flow rate at the i-th time point in the steam valve assembly; Calculate the flow fluctuation rate of the steam valve assembly in the current cycle: B = σ / Q´; The flow fluctuation rate and steady-state flow deviation of the steam valve group components are collected to obtain flow control data; Step S104: When the flow fluctuation rate or steady-state flow deviation value in the flow control data is greater than the preset threshold, it is determined that the flow control status of the steam valve group valve in the current cycle is abnormal, and the steam valve group valve is recorded as an abnormal steam valve group valve. Step S200: Based on the flow control monitoring records, obtain the reference abnormality cause data of the steam valve group valves, analyze the abnormal causes of the flow control of the abnormal steam valve group valves, obtain the performance optimization reference data from the platform, and optimize the performance of the abnormal steam valve group valves to obtain the performance optimization data. Step S200 includes: Step S201: Based on the flow control monitoring records, analyze the abnormal causes of flow control abnormalities in the steam valve group. The specific analysis process includes: Obtain the theoretical flow coefficient C of the abnormal steam valve assembly. v Obtain the actual flow coefficient C´ of the abnormal steam valve group valve in the current cycle from the flow control monitoring record. v ; For example, the actual flow coefficient C' v The specific calculation formula is as follows: , Where △P is the pressure difference across the valve in the abnormal steam valve assembly; ρ is the density of steam under standard conditions, which is generally the same as the density of water, 1000 kg / m³. 3 ; Calculate the flow coefficient distortion rate F = C' of abnormal steam valve assembly components. v / C v ; Step S202: Input a preset input signal to the valve components of the abnormal steam valve group through the platform, and obtain the maximum difference Δθ between the output positions of the upward and downward signals under the same input signal. max Calculate the hysteresis rate H of the abnormal steam valve group in the current cycle: H = (Δθ) max ( / 100)×100%; For example, the input signal is a triangular wave signal that goes from 0% to 100% and then back to 0%. Step S203: Obtain the average pressure difference μ across the valve of the abnormal steam valve group at various time points within the current cycle from the flow control monitoring record. (△,P) and standard deviation σ (△,P) Calculate the differential pressure fluctuation value W of the abnormal steam valve group valves. (△,P) =(σ (△,P) / μ (△,P) )×100%; Step S204: Obtain the high-frequency energy E of the abnormal steam valve group valve in the current cycle from the flow control monitoring record. high and low-frequency energy E low Calculate the cavitation energy ratio I=E from the flow control monitoring records. high / E low ; Step S205: Use the flow coefficient distortion rate, execution hysteresis rate, differential pressure fluctuation value and cavitation energy ratio of the abnormal steam valve group valve in the current cycle as the abnormal detection parameters of the abnormal steam valve group valve in the current cycle. Obtain reference abnormality cause data for abnormal steam valve assembly components. The reference abnormality cause data includes the relationship between the abnormal detection parameters and preset thresholds in the abnormal steam valve assembly components when various abnormal causes occur. For example, various abnormal causes include valve core wear, controller mismatch, and actuator jamming; The relationship between several abnormal detection parameters corresponding to a certain abnormal cause and a preset threshold is obtained. When the values ​​of several abnormal detection parameters of the abnormal steam valve group in the current period all meet the relationship between several abnormal detection parameters corresponding to a certain abnormal cause and the preset threshold, it is determined that a certain abnormal cause has occurred in the current period. The abnormal cause is recorded as the characteristic abnormal cause of the abnormal steam valve group. The characteristic abnormal causes of the abnormal steam valve group are obtained. For example, when the valve core is worn, the flow coefficient distortion rate is less than the threshold of 0.9. Therefore, when the flow coefficient distortion rate is less than 0.9, it is determined that the valve core of the abnormal steam valve group has worn. Step S206: Obtain performance optimization comparison data from the platform. The performance optimization comparison data includes the platform's preset performance optimization strategies when various abnormal causes occur in the steam valve group valves. The performance of abnormal steam valve assembly components is optimized. The specific optimization process is as follows: Based on the performance optimization comparison data, the performance optimization strategies corresponding to the abnormal causes of each characteristic are obtained. The performance optimization strategies corresponding to the abnormal causes of each characteristic are sorted and collected to obtain the performance optimization data of the steam valve group valves. Step S300: Obtain performance optimization data of abnormal steam valve group valves, obtain node performance records of transmission nodes in the platform, evaluate the node performance of transmission nodes in the current cycle, obtain the target transmission node, and transmit the performance optimization data to the target transmission node. Step S300 includes: Step S301: Obtain each transmission node in the platform, obtain the node performance record of each transmission node in the current period; Step S302: Obtain the average values ​​of bandwidth, latency, and packet loss rate of the transmission node from the node performance records. Standardize the bandwidth, latency, and packet loss rate of the transmission node to obtain the labeled values ​​X of bandwidth, latency, and packet loss rate of the transmission node. α norm X β norm and X γ norm ; For example, the bandwidth tag value X α norm The calculation formula is: , Among them, X α X represents the average bandwidth of the transmission nodes in the node performance record; α max X represents the maximum bandwidth recorded in the node performance logs of each transmission node. α min This refers to the minimum bandwidth value recorded in the node performance records of each transmission node. For example, the delayed tag value X β norm The calculation formula is: , Among them, X β X represents the average bandwidth of the transmission nodes in the node performance record; β max X represents the maximum bandwidth recorded in the node performance logs of each transmission node. βmin This refers to the minimum bandwidth value recorded in the node performance records of each transmission node. For example, the delayed tag value X γ norm The calculation formula is: , Among them, X γ X represents the average bandwidth of the transmission nodes in the node performance record; γ max X represents the maximum bandwidth recorded in the node performance logs of each transmission node. γ min This refers to the minimum bandwidth value recorded in the node performance records of each transmission node. Step S303: Calculate the node performance score Y=λ of the transmission node. α ·X α norm +λ β ·X β norm +λ γ ·X γ norm , where λ α , λ β and λ γ These are preset proportional coefficients for the bandwidth, latency, and packet loss rate of the transmission node, respectively; λ α +λ β +λ γ =1, λ α , λ β and λ γ All are greater than 0; Step S304: Obtain the node performance score of each transmission node in the current period, and select the transmission node with the maximum node performance score as the target transmission node, and transmit the performance optimization data of the abnormal steam valve group valve to the target transmission node. Step S400: Obtain performance optimization data from the target transmission node, verify the completeness of the performance optimization data, and send the verified performance optimization data to the staff through the platform; Step S400 includes: Step S401: Obtain the performance optimization data of the abnormal steam valve group valve in the target transmission node, and the hash value h´ of the performance optimization data in the target transmission node; Step S402: Verify the integrity of the performance optimization data. The specific verification process is as follows: obtain the hash value h of the performance optimization data before transmission. When the hash value h' is equal to the hash value h, it is determined that the performance optimization data in the target transmission node is transmitted completely. Step S403: The verified performance optimization data is sent to the personnel managing the steam valve group through the platform, prompting them to perform performance optimization on the abnormal steam valve group components. To better implement the above methods, a data transmission system for optimizing the performance of steam valve assembly components based on big data was also proposed. The system includes an anomaly assessment module, a performance optimization module, a performance optimization data transmission module, and a data verification module. The anomaly assessment module is used to assess the anomaly status of the flow control state of the steam valve group components and identify abnormal steam valve group components. The performance optimization module is used to optimize the performance of abnormal steam valve group components and obtain performance optimization data; The performance optimization data transmission module is used to evaluate the node performance of the transmission node in the current period, obtain the target transmission node, and transmit the performance optimization data to the target transmission node. The data verification module is used to verify the completeness of performance optimization data and send the verified performance optimization data to staff through the platform. The anomaly assessment module includes a flow control identification unit and an anomaly assessment unit. The flow control identification unit is used to identify the flow control status of the valve components in the steam valve group and obtain flow control data. The anomaly assessment unit is used to assess the anomaly status of the steam valve group valves based on the flow control data, and to identify abnormal steam valve group valves.

[0020] Furthermore, the performance optimization module includes an anomaly analysis unit and a performance optimization unit; The anomaly analysis unit is used to acquire reference anomaly cause data for steam valve group components and analyze the anomaly causes of flow control anomalies in abnormal steam valve group components. The performance optimization unit is used to acquire performance optimization comparison data and optimize the performance of abnormal steam valve group components to obtain performance optimization data. The performance optimization data transmission module includes a node performance evaluation unit and a performance optimization data transmission unit. The node performance evaluation unit is used to acquire the node performance records of the transmission nodes within the platform, evaluate the node performance of the transmission nodes in the current period, and obtain the target transmission node. The performance optimization data transmission unit is used to acquire the target transmission node in the platform and transmit the performance optimization data of the abnormal steam valve group valve to the target transmission node. The data verification module includes a data verification unit. The data verification unit is used to verify the completeness of the performance optimization data. After verification, the performance optimization data is sent to the personnel managing the steam valve group through the platform, prompting them to perform performance optimization on abnormal steam valve group components.

[0021] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A data transmission method for optimizing the performance of steam valve assembly components based on big data, characterized in that, The method includes: Step S100: Obtain the flow control monitoring record of the steam valve group valves in the current period, identify the flow control status of the steam valve group valves, obtain flow control data, and based on the flow control data, evaluate the abnormality of the flow control status of the steam valve group valves to obtain abnormal steam valve group valves. Step S200: Based on the flow control monitoring record, obtain the reference abnormality cause data of the steam valve group valve, analyze the abnormal causes of the flow control of the abnormal steam valve group valve, obtain the performance optimization reference data from the platform, and optimize the performance of the abnormal steam valve group valve to obtain performance optimization data. Step S300: Obtain the performance optimization data of the abnormal steam valve group valve, obtain the node performance record of the transmission node in the platform, evaluate the node performance of the transmission node in the current period, obtain the target transmission node, and transmit the performance optimization data to the target transmission node. Step S400: Obtain the performance optimization data from the target transmission node, verify the completeness of the performance optimization data, and send the verified performance optimization data to the staff through the platform.

2. The data transmission method for optimizing the performance of steam valve assembly components based on big data as described in claim 1, characterized in that, Step S100 includes: Step S101: Acquire the flow control monitoring records of the steam valve group valves and perform data preprocessing on the flow control monitoring records; Step S102: Identify the flow control status of the steam valve assembly to obtain flow control data. The specific identification process is as follows: Obtain the steam density ρ of the steam valve assembly at the a-th time point within the current cycle from the flow control monitoring record. a Actual traffic volume Q´ a The pressure difference ΔP before and after the valve a Obtain the theoretical flow rate Q of the steam valve assembly at the a-th time point. a ; Step S103: Calculate the flow deviation value δ of the steam valve assembly at the a-th time point within the current cycle. a =|Q a -Q´ a | / Q a ; Calculate the steady-state flow deviation G of the steam valve assembly in the current cycle: ， Where n is the total number of valves in the steam valve group at any point in time within the current cycle; δ i The flow deviation value of the steam valve assembly at the i-th time point in the current cycle; Step S104: Obtain the average value Q´ of the measured flow rate in the steam valve assembly, and calculate the flow fluctuation value σ of the steam valve assembly. ， Among them, Q´ i The measured flow rate at the i-th time point in the steam valve assembly; Calculate the flow fluctuation rate of the steam valve assembly in the current cycle: B = σ / Q´; The flow fluctuation rate and the steady-state flow deviation value of the steam valve group are collected to obtain flow control data; Step S104: When the flow fluctuation rate or the steady-state flow deviation value in the flow control data is greater than a preset threshold, it is determined that the flow control status of the steam valve group valve in the current cycle is abnormal, and the steam valve group valve is recorded as an abnormal steam valve group valve.

3. The data transmission method for optimizing the performance of steam valve assembly components based on big data as described in claim 2, characterized in that, Step S200 includes: Step S201: Based on the flow control monitoring records, analyze the abnormal causes of the abnormal flow control of the abnormal steam valve group. The specific analysis process includes: Obtain the theoretical flow coefficient C of the abnormal steam valve assembly. v The actual flow coefficient C´ of the abnormal steam valve group in the current cycle is obtained from the flow control monitoring record. v ; Calculate the flow coefficient distortion rate F = C' of the abnormal steam valve assembly. v / C v ; Step S202: Input a preset input signal to the abnormal steam valve group valve via the platform, and obtain the maximum difference Δθ between the output positions of the upward and downward signals under the same input signal. max Calculate the hysteresis rate H = (Δθ) of the abnormal steam valve group valve in the current cycle. max ( / 100)×100%; Step S203: Obtain the average value μ of the pressure difference across the valve of the abnormal steam valve group at various time points within the current cycle from the flow control monitoring record. (△,P) and standard deviation σ (△,P) Calculate the differential pressure fluctuation value W of the abnormal steam valve group valves. (△,P) =(σ (△,P) / μ (△,P) )×100%; Step S204: Obtain the high-frequency energy E of the abnormal steam valve group valve in the current cycle from the flow control monitoring record. high and low-frequency energy E low Calculate the cavitation energy ratio I=E from the flow control monitoring record. high / E low ; Step S205: The flow coefficient distortion rate, execution hysteresis rate, differential pressure fluctuation value and cavitation energy ratio of the abnormal steam valve group valve in the current cycle are used as the abnormal detection parameters of the abnormal steam valve group valve in the current cycle. Obtain control abnormality cause data in the abnormal steam valve group valves. The control abnormality cause data includes the relationship between the abnormal detection parameters in the abnormal steam valve group valves and preset thresholds when the abnormal steam valve group valves have various abnormal causes. The relationship between several abnormal detection parameters corresponding to a certain abnormal cause and a preset threshold is obtained. When the values ​​of the abnormal steam valve group valve in the current period are all in line with the relationship between the several abnormal detection parameters corresponding to the certain abnormal cause and the preset threshold, it is determined that the abnormal steam valve group valve has the certain abnormal cause in the current period, and the certain abnormal cause is recorded as the characteristic abnormal cause of the abnormal steam valve group valve. The characteristic abnormal causes of the abnormal steam valve group valve are obtained. Step S206: Obtain performance optimization comparison data from the platform, including various performance optimization strategies preset by the platform when various abnormal causes occur in the steam valve group valves; The performance of the abnormal steam valve assembly components is optimized, and the specific optimization process is as follows: Based on the performance optimization comparison data, the performance optimization strategies corresponding to the abnormal causes of each characteristic are obtained. The performance optimization strategies corresponding to the abnormal causes of each characteristic are organized and collected to obtain the performance optimization data of the steam valve group.

4. The data transmission method for optimizing the performance of steam valve assembly components based on big data as described in claim 3, characterized in that, Step S300 includes: Step S301: Obtain each transmission node in the platform, obtain the node performance record of the transmission node in the current period, and obtain the node performance record of each transmission node in the current period. Step S302: Obtain the average values ​​of bandwidth, latency, and packet loss rate of the transmission node from the node performance record, and standardize the bandwidth, latency, and packet loss rate of the transmission node to obtain the label values ​​X of the bandwidth, latency, and packet loss rate of the transmission node. α norm X β norm and X γ norm ; Step S303: Calculate the node performance score Y=λ of the transmission node. α ·X α norm +λ β ·X β norm +λ γ ·X γ norm , where λ α , λ β and λ γ These are preset proportional coefficients for the bandwidth, latency, and packet loss rate of the transmission node, respectively; λ α +λ β +λ γ =1, λ α , λ β and λ γ All are greater than 0; Step S304: Obtain the node performance score of each transmission node in the current period, and select the transmission node with the maximum node performance score as the target transmission node, and transmit the performance optimization data of the abnormal steam valve group valve to the target transmission node.

5. The data transmission method for optimizing the performance of steam valve assembly components based on big data according to claim 4, characterized in that, Step S400 includes: Step S401: Obtain the performance optimization data of the abnormal steam valve group valve in the target transmission node, wherein the hash value h´ of the performance optimization data in the target transmission node is; Step S402: Verify the integrity of the performance optimization data. The specific verification process is as follows: obtain the hash value h of the performance optimization data before transmission. When the hash value h' is equal to the hash value h, it is determined that the performance optimization data in the target transmission node is transmitted completely. Step S403: The verified performance optimization data is sent to the personnel managing the steam valve group through the platform, prompting the personnel to perform performance optimization on the abnormal steam valve group.

6. A data transmission system for optimizing the performance of steam valve components based on big data, used to execute the data transmission method for optimizing the performance of steam valve components based on big data as described in any one of claims 1-5, characterized in that, The system includes an anomaly assessment module, a performance optimization module, a performance optimization data transmission module, and a data verification module; The anomaly assessment module is used to assess the anomaly status of the flow control state of the steam valve group valves and identify abnormal steam valve group valves. The performance optimization module is used to optimize the performance of the abnormal steam valve group components and obtain performance optimization data. The performance optimization data transmission module is used to evaluate the node performance of the transmission node in the current period, obtain the target transmission node, and transmit the performance optimization data to the target transmission node. The data verification module is used to verify the completeness of the performance optimization data and send the verified performance optimization data to the staff through the platform.

7. The data transmission system for optimizing the performance of steam valve assembly components based on big data as described in claim 6, characterized in that, The anomaly assessment module includes a flow control identification unit and an anomaly assessment unit; The flow control identification unit is used to identify the flow control status of the valve components in the steam valve group and obtain flow control data. The anomaly assessment unit is used to assess the anomaly status of the steam valve group valves based on the flow control data, and to identify abnormal steam valve group valves.

8. The data transmission system for optimizing the performance of steam valve assembly components based on big data as described in claim 6, characterized in that, The performance optimization module includes an anomaly analysis unit and a performance optimization unit; The anomaly analysis unit is used to acquire the reference anomaly cause data of the steam valve group valves and to analyze the anomaly cause of the flow control of the abnormal steam valve group valves. The performance optimization unit is used to acquire performance optimization comparison data and optimize the performance of the abnormal steam valve group valves to obtain performance optimization data.

9. The data transmission system for optimizing the performance of steam valve groups based on big data as described in claim 6, characterized in that, The performance optimization data transmission module includes a node performance evaluation unit and a performance optimization data transmission unit; The node performance evaluation unit is used to acquire the node performance records of the transmission nodes in the platform, evaluate the node performance of the transmission nodes in the current period, and obtain the target transmission node. The performance optimization data transmission unit is used to acquire the target transmission node in the platform and transmit the performance optimization data of the abnormal steam valve group to the target transmission node.

10. The data transmission system for optimizing the performance of steam valve assembly components based on big data as described in claim 6, characterized in that, The data verification module includes a data verification unit; The data verification unit is used to verify the completeness of the performance optimization data, and then sends the verified performance optimization data to the personnel managing the steam valve group through the platform, prompting the personnel to perform performance optimization on the abnormal steam valve group.

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