Remote valve closing and locking mistaken opening prevention system based on intelligent safety AI valve

Abstract

The invention discloses a remote valve closing and locking mistaken opening prevention system based on an intelligent safety AI valve, and relates to the technical field of fluid control valves. The system comprises an intelligent sensing layer, a local decision-making layer and a cloud collaboration layer; the method comprises the following steps: constructing an AI valve network virtual model, integrating multiple groups of sensor data to obtain multi-modal data, acquiring an AI valve evaluation value in real time by utilizing a local decision-making layer, determining whether to generate a valve closing locking request according to the AI valve evaluation value, and constructing a three-channel parallel data transmission architecture. The cloud collaboration layer carries out fluid simulation and generates a visual display result when receiving the valve closing locking request, and an operator decides whether valve closing locking is carried out or not and carries out upstream and downstream linkage control; the valve closing error rate is reduced through multi-modal data fusion and AI valve network virtual model simulation, communication reliability is guaranteed through three data transmission channels, and accurate valve closing locking and accident range control are achieved through dynamic threshold adjustment.

Description

Technical Field

[0001] The present invention belongs to the field of valve control technology, and in particular is a remote valve closing and locking anti-misopening system based on an intelligent safety AI valve. Background Art

[0002] The current field of valve control technology has evolved from the mechanical control stage in the last century to the electronic control stage and then to the primary intelligent stage today. It has basically solved problems such as high valve closing trigger delay, single data dimension and static valve closing judgment threshold defects. However, there are still problems such as false valve closing caused by sensor false alarms due to bad weather and other reasons, disconnection caused by transmission channels that are easily disturbed during communication transmission, and the lack of upstream and downstream valve linkage control, which makes the impact of accidents easy to expand.

[0003] How to reduce the error valve closure caused by sensor false alarm, realize the linkage control of upstream and downstream valves to reduce the scope of accident impact, and improve the reliability of communication transmission to avoid data loss are issues we need to consider. Summary of the Invention

[0004] The object of the present invention is to provide a remote valve closing and locking anti-misopening system based on an intelligent safety AI valve.

[0005] The object of the present invention can be achieved by the following technical solutions: a remote valve closing and locking anti-misopening system based on an intelligent safety AI valve, comprising an intelligent perception layer, a local decision layer, and a cloud collaboration layer; The intelligent perception layer is used to build a virtual model of the AI ​​valve network, acquire AI valve body data, AI valve surrounding environment perception data, and AI valve physical position offset data in real time, and integrate the acquired data to obtain multimodal data; The local decision layer is used to perform real-time evaluation on the obtained multimodal data and determine whether to generate a valve closing and locking request based on the result of the real-time evaluation; The cloud-based collaborative layer is used to call the AI ​​valve network virtual model to perform fluid simulation when the AI ​​valve generates a valve closing and locking request. The operator determines whether to approve the valve closing and locking request and performs linkage control of upstream and downstream adjacent AI valves, and dynamically adjusts the threshold for generating the valve closing and locking request.

[0006] Furthermore, the intelligent perception layer is used to construct an AI valve network virtual model, obtain AI valve body data, AI valve surrounding environment perception data, and AI valve physical position offset data in real time, and integrate the obtained data to obtain multimodal data. The process includes: The AI valve obtains AI valve equipment information when the AI valve is first put into operation, the AI valve equipment information includes the structure, material, and force bearing range of the AI valve body and the structure, material, and force bearing range of the pipeline connected with the AI valve body, and all AI valves are connected according to the connection relationship of the AI valve and the fluid flow direction in the pipeline to obtain an AI valve network virtual model; The AI valve body data includes the pressure difference of multiple points in the circumferential direction of the valve seat sealing ring of the AI valve and the temperature at the connection between the valve stem and the valve core of the AI valve; Multiple groups of pressure sensors are embedded in the inner side of the valve seat sealing ring of the AI valve, and the maximum value of the pressure difference between multiple points in the circumferential direction of the valve seat sealing ring of the AI valve is obtained in real time through the embedded multiple groups of pressure sensors; The temperature sensor is installed on the valve stem and valve core connection of the AI valve by patching, and the temperature at the valve stem and valve core connection of the AI valve is obtained in real time through the installed temperature sensor; The environmental perception data includes the distance between each heat source point within the detection radius of the AI valve and the AI valve; The environmental perception sensor is installed outside the AI valve, and the heat source point within the detection radius of the AI valve is obtained in real time through the installed environmental perception sensor, and the distance between the closest heat source point and the AI valve is obtained; The AI valve physical position offset data includes the geographical position coordinate offset degree and the inclination angle offset degree; The Beidou satellite positioning device and the six-axis gyroscope are installed outside the AI valve, and the AI valve physical position offset data is obtained in real time through the installed Beidou satellite positioning device and six-axis gyroscope, including the geographical position coordinate offset degree and the AI valve inclination angle offset degree; The obtained maximum value of the pressure difference of multiple points in the circumferential direction of the valve seat sealing ring of the AI valve, the temperature at the valve stem and valve core connection of the AI valve, the distance between the closest heat source point and the AI valve, the geographical position coordinate offset distance of the AI valve, and the inclination angle offset degree of the AI valve are integrated to obtain multi-modal data, and the obtained multi-modal data is imported into the AI valve network virtual model that has been constructed.

[0007] Further, the local decision layer is used to evaluate the obtained multi-modal data in real time, and the process of judging whether to generate a valve closing locking request according to the result of real-time evaluation includes: The AI valve evaluation value V is obtained through the obtained multi-modal data; The obtained AI valve evaluation value is compared with the valve closing locking request threshold value V0: When the comparison result is , a valve closing locking request is generated; When the comparison result is , the AI valve remains open.

[0008] Further, the process in which the local decision layer sends the valve locking request to the cloud collaborative layer includes: The optical fiber network is constructed as the main data transmission channel, and the 5G private network and the LoRa wireless network are constructed as the backup data transmission channel, the sending of the valve locking request and the multi-modal data transmission preferentially use the optical fiber network, when the optical fiber network is unavailable due to failure or interference, the backup data transmission channel is switched to, and an alarm log is sent to the cloud collaborative layer.

[0009] Further, the process in which the cloud collaborative layer performs fluid simulation on the AI valve network virtual model and generates a visual display result includes: When the valve locking request of a single AI valve or the valve locking request of multiple AI valves is received, the fault point is only possible on the AI valve sending the valve locking request and the pipeline connected with the AI valve, then the fluid simulation is performed only on the AI valve sending the valve locking request and the pipeline connected with the AI valve in the AI valve network virtual model, to obtain the force F generated by the fluid operation on each part of the AI valve and the pipeline; When the valve locking request of multiple directly connected AI valves is received, the fluid simulation is first performed on all the AI valves sending the valve locking request and the pipelines connected with the AI valves in the AI valve network virtual model, to obtain the influence data generated by the fluid operation in each AI valve on the upstream and downstream AI valves, the influence data including the influence value of the pressure difference of multiple points in the circumferential direction of the valve seat sealing ring of the AI valve, the influence value of the temperature at the connection between the valve stem and the valve core of the AI valve, the influence value of the deviation distance of the geographical position coordinates of the AI valve, and the influence value of the deviation degree of the inclination angle of the AI valve, the difference between the multi-modal data of each AI valve and the obtained influence data is eliminated, to obtain the real multi-modal data of each AI valve, and the fluid simulation is performed again on all the AI valves sending the valve locking request and the pipelines connected with the AI valves in the AI valve network virtual model based on the obtained real multi-modal data, to obtain the force generated by the fluid operation on the pipeline and the AI valve; The risk levels are set, including the safe level, the warning level and the dangerous level, and the corresponding force bearing range is set for each risk level, wherein the force bearing range corresponding to the safe level is greater than 0 and less than or equal to D1, the force bearing range corresponding to the warning level is greater than D1 and less than or equal to D2, and the force bearing range corresponding to the dangerous level is greater than D2, D1 is 80% of the upper limit of the component force bearing, and D2 is 100% of the upper limit of the component force bearing, the obtained force is matched with the force bearing range, the risk level division is completed according to the matching result, and the divided risk level is visually displayed in the AI valve network virtual model.

[0010] Furthermore, the cloud collaboration layer comprises a process in which an operator determines whether to approve a valve closing and locking request based on the obtained visual display results, and performs upstream and downstream linkage control. When the operator approves the valve closing and locking request, the corresponding AI valve will immediately execute the valve closing and locking operation, and the threshold for generating valve closing and locking requests for the upstream and downstream AI valves of the AI ​​valve being closed and locked will be reduced by 10%. The AI ​​valve's valve closing and locking anti-accidental opening protection mechanism will be activated. At this time, if the corresponding AI valve needs to be opened, the maintenance engineer needs to conduct on-site inspection and confirm that there is no risk and then issue a valve opening request to the operator before it can be opened again. When the operator rejects the valve closing and locking request, the corresponding AI valve does not perform the valve closing and locking operation, and the threshold for the corresponding AI valve to generate the valve closing and locking request is increased by 10%, and the adjacent AI valves are not adjusted.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. The AI ​​valve evaluation value is obtained by weighted calculation of multi-modal data. The AI ​​valve that generates the valve closing and locking request needs to be visually displayed after fluid simulation for operator approval, which reduces the possibility of incorrect valve closing caused by abnormal single sensor indicators. 2. The three data transmission channels serve as backup for each other, enhancing the reliability of data transmission; 3. Based on the evaluation feedback from the operators, the threshold value of the valve closing and locking protection mechanism to prevent accidental opening is dynamically adjusted, and the impact range of possible accidents is effectively controlled by adjusting the threshold value of the valve closing and locking protection mechanism to prevent accidental opening of the upstream and downstream AI valves. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0013] like Figure 1 As shown in the figure, the remote valve closing and locking anti-accidental opening system based on the intelligent safety AI valve includes an intelligent perception layer, a local decision layer, and a cloud collaboration layer; The intelligent perception layer is used to build a virtual model of the AI ​​valve network, acquire AI valve body data, AI valve surrounding environment perception data, and AI valve physical position offset data in real time, and integrate the acquired data to obtain multimodal data; When an AI valve is first put into operation, AI valve device information is obtained. This includes the structure, material, and load-bearing range of the AI ​​valve body and the structure, material, and load-bearing range of the pipeline connected to the AI ​​valve body. Connections are then established between all AI valves based on the fluid flow directions within the AI ​​valves and pipelines to obtain a virtual model of the AI ​​valve network. The AI ​​valve body data includes the pressure difference at multiple points in the circumferential direction of the valve seat sealing ring of the AI ​​valve and the temperature at the connection between the valve stem and the valve core of the AI ​​valve; a plurality of groups of pressure sensors are embedded inside the valve seat sealing ring of the AI valve, the maximum value of the pressure difference between a plurality of points in the circumferential direction of the valve seat sealing ring of the AI valve is obtained in real time through the embedded plurality of groups of pressure sensors, and the obtained maximum value of the pressure difference is denoted as P; a temperature sensor is installed on the valve stem and valve core connection of the AI valve by patching, the temperature at the valve stem and valve core connection of the AI valve is obtained in real time through the installed temperature sensor, and the obtained temperature is denoted as T; The environmental perception data includes the distance between each heat source point within the detection radius of the AI valve and the AI valve. An environmental perception sensor is installed outside the AI valve, heat source points within the detection radius of the AI valve are obtained in real time through the installed environmental perception sensor, the distance between the closest heat source point to the AI valve and the AI valve is obtained, and the minimum value of the obtained heat source point distance is denoted as H. The AI valve physical position offset data includes the geographic position coordinate offset and the inclination angle offset. A Beidou satellite positioning device and a six-axis gyroscope are installed outside the AI valve, AI valve physical position offset data is obtained in real time through the installed Beidou satellite positioning device and six-axis gyroscope, the AI valve physical position offset data includes the geographic position coordinate offset and the inclination angle offset, and the obtained geographic position coordinate offset distance and inclination angle offset of the AI valve are denoted as L and I, respectively. It should be further explained that, in the specific implementation process, the process of integrating the obtained data to obtain multi-modal data by the intelligent perception layer includes: The maximum value P of the pressure difference of a plurality of points in the circumferential direction of the valve seat sealing ring of the AI valve, the temperature T at the valve stem and valve core connection of the AI valve, the distance H between the closest heat source point to the AI valve and the AI valve, the geographic position coordinate offset distance L of the AI valve, and the inclination angle offset I of the AI valve are integrated to obtain multi-modal data, denoted as K, wherein The obtained multi-modal data is introduced into the AI valve network virtual model that has been constructed.

[0014] The local decision layer is used to use the AI inference unit local to the AI valve to evaluate the obtained multi-modal data in real time, and determine whether to generate a valve closing and locking request according to the result of real-time evaluation. It should be further explained that, in the specific implementation process, the process of the local decision layer evaluating the obtained multi-modal data in real time includes: The AI valve evaluation value is obtained through the obtained multi-modal data K, denoted as V, wherein , is an operation coefficient; It needs to be further explained that in the specific implementation process, the process in which the local decision layer judges whether to generate a valve closing lock request according to the obtained AI valve evaluation value includes: comparing the obtained AI valve evaluation value with a threshold V0 for generating a valve closing lock request: when the comparison result is , a valve closing lock request is generated; when the comparison result is , the AI valve remains open.

[0015] It needs to be further explained that in the specific implementation process, the process in which the local decision layer sends a valve closing lock request to the cloud collaborative layer is as follows: The optical fiber network is constructed as a main data transmission channel, the 5G private network and the LoRa wireless network are constructed as backup data transmission channels, the sending of the valve closing lock request and the multi-modal data transmission preferentially use the optical fiber network, when the optical fiber network is unavailable due to failure or interference, the backup data transmission channel is switched to, and an alarm log is sent to the cloud collaborative layer.

[0016] The cloud collaborative layer is configured to, when the AI valve generates a valve closing lock request, call an AI valve network virtual model to perform fluid simulation, judge whether to approve the valve closing lock request and perform upstream and downstream adjacent AI valve linkage control by an operator, and dynamically adjust the threshold for generating the valve closing lock request. It needs to be further explained that the cloud collaborative layer performs fluid simulation on the AI valve network virtual model in the following process: obtaining, through the fluid simulation, an acting force F of the fluid on each part of the AI valve and the pipeline, wherein ; wherein , is an initial temperature of the AI valve, E is an elastic modulus, is a thermal deformation coefficient, , B is a stiffness coefficient, and C is a damping coefficient. When a valve closing lock request of a single AI valve or valve closing lock requests of multiple AI valves are received, the fault point is only possible on the AI valve sending the valve closing lock request and the pipeline connected with the AI valve, then the fluid simulation is only performed on the AI valve sending the valve closing lock request and the pipeline connected with the AI valve in the AI valve network virtual model to obtain the acting force F of the fluid operation on each part of the AI valve and the pipeline. When valve closing lock requests of multiple directly connected AI valves are received, the fluid simulation is first performed on all the AI valves sending the valve closing lock request and the pipelines connected with the AI valves in the AI valve network virtual model to obtain influence data of the fluid operation in each AI valve on the upstream and downstream AI valves, the influence data including influence values of pressure differences of multiple points in the circumferential direction of the valve seat sealing ring of the AI valve. , the influence value of the temperature at the connection between the valve stem and the valve core of the AI valve , the influence value of the distance of the deviation of the geographical position coordinates of the AI valve , and the influence value of the deviation degree of the inclination angle of the AI valve , the influence value of the pressure difference of multiple points in the circumferential direction of the valve seat sealing ring of the AI valve , wherein is the length of the pipeline between the adjacent AI valve and the AI valve, is the pipeline damping coefficient; the influence value of the temperature at the connection between the valve stem and the valve core of the AI valve , wherein is the thermal conductivity of the fluid, is the contact area of the fluid and the pipeline; the influence value of the distance of the deviation of the geographical position coordinates of the AI valve , wherein is the Young's modulus of the pipeline, is the flange connection stiffness, is the radius of the pipeline; the influence value of the deviation degree of the inclination angle of the AI valve , wherein is the pipeline installation gap; eliminate the difference between the multi-modal data of each AI valve and the obtained influence data, obtain the real multi-modal data of each AI valve, and based on the obtained real multi-modal data, re-perform fluid simulation on all AI valves issuing a valve locking request in the AI valve network virtual model and the pipeline connected with the AI valve, to obtain the force F on the pipeline and the AI valve generated by fluid operation; set risk levels, respectively, safe, warning, and dangerous levels, and set corresponding bearing ranges for each risk level, wherein the bearing range corresponding to the safe level is greater than 0 and less than or equal to D1, the bearing range corresponding to the warning level is greater than D1 and less than or equal to D2, and the bearing range corresponding to the dangerous level is greater than D2, D1 is 80% of the upper limit of the component bearing force, and D2 is 100% of the upper limit of the component bearing force, match the obtained force with the bearing range, complete the risk level division according to the matching result, and visually display the divided risk level in the AI valve network virtual model, for example: for the valve seat sealing ring of the AI valve, when the bearing range of the force F generated by fluid operation is greater than 0 and less than or equal to D1, the valve seat sealing ring of the AI valve is in the safe level, and the corresponding part in the AI valve network virtual model is displayed in green; ​When the force F generated by the obtained fluid operation is in the bearing range greater than D1 and less than or equal to D2, at this time, the valve seat sealing ring of the AI valve is in the warning level, and the corresponding part in the AI valve network virtual model is displayed in yellow; When the force F generated by the obtained fluid operation is in the bearing range greater than D2, at this time, the valve seat sealing ring of the AI valve is in the danger level, and the corresponding part in the AI valve network virtual model is displayed in red; According to the set risk level, each component in the AI valve network virtual model is marked with color respectively, a visual display result is generated, and the generated visual display result is fed back to the operator.

[0017] It needs to be further explained that the process of judging whether to approve the valve closing locking request and performing upstream and downstream linkage control according to the obtained visual display result by the operator is as follows: When the operator approves the valve closing locking request, the corresponding AI valve immediately performs the valve closing locking operation, reduces the threshold value of the upstream and downstream adjacent AI valves of the executed valve closing locking AI valve by 10% to generate the valve closing locking request, and starts the valve closing locking anti-misoperation protection mechanism of the AI valve. At this time, if the corresponding AI valve needs to be opened, the maintenance engineer needs to confirm that there is no risk on site and then send an opening valve request to the operator to open it again. When the operator denies the valve closing locking request, the corresponding AI valve does not perform the valve closing locking operation, and the threshold value of the corresponding AI valve to generate the valve closing locking request is increased by 10%, and the adjacent AI valve is not adjusted.

[0018] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the technical solution of the present application. Any modification or equivalent replacement of the above embodiments according to the technical essence of the present application, which does not depart from the technical solution of the present application, still belongs to the scope of the technical solution of the present application.

Claims

1. A remote valve closing and locking anti-accidental opening system based on intelligent safety AI valve, characterized in that: Includes intelligent perception layer, local decision-making layer and cloud collaboration layer; The intelligent perception layer is used to build a virtual model of the AI ​​valve network, acquire AI valve body data, AI valve surrounding environment perception data, and AI valve physical position offset data in real time, and integrate the acquired data to obtain multimodal data; The local decision layer is used to perform real-time evaluation on the obtained multimodal data and determine whether to generate a valve closing and locking request based on the result of the real-time evaluation; The cloud-based collaborative layer is used to call the AI ​​valve network virtual model to perform fluid simulation when the AI ​​valve generates a valve closing and locking request. The operator determines whether to approve the valve closing and locking request and performs linkage control of upstream and downstream adjacent AI valves, and dynamically adjusts the threshold for generating the valve closing and locking request.

2. According to claim 1, a remote valve closing and locking anti-accidental opening system based on an intelligent safety AI valve is characterized in that: The process of constructing the AI ​​valve network virtual model by the intelligent perception layer includes: When the AI ​​valve is put into operation for the first time, the AI ​​valve equipment information is obtained. The AI ​​valve equipment information includes the structure, material, and load-bearing range of the AI ​​valve body and the structure, material, and load-bearing range of the pipeline connected to the AI ​​valve body. All AI valves are connected according to the fluid flow direction in the AI ​​valve and the pipeline to obtain an AI valve network virtual model.

3. According to claim 2, a remote valve closing and locking anti-accidental opening system based on an intelligent safety AI valve is characterized in that: The process of the intelligent perception layer obtaining the AI ​​valve local data, the AI ​​valve surrounding environment perception data, and the AI ​​valve physical position offset data includes: The AI ​​valve body data includes the pressure difference at multiple points in the circumferential direction of the valve seat sealing ring of the AI ​​valve and the temperature at the connection between the valve stem and the valve core of the AI ​​valve; Multiple sets of pressure sensors are embedded inside the valve seat sealing ring of the AI ​​valve, and the maximum value of the pressure difference between multiple points in the circumferential direction of the valve seat sealing ring of the AI ​​valve is obtained in real time through the embedded multiple sets of pressure sensors; The temperature sensor is installed at the connection between the valve stem and the valve core of the AI ​​valve by patch type, and the temperature at the connection between the valve stem and the valve core of the AI ​​valve is obtained in real time through the installed temperature sensor; The environmental sensing data includes the distance between each heat source point within the detection radius of the AI ​​valve and the AI ​​valve; An environmental perception sensor is installed on the outside of the AI ​​valve. The installed environmental perception sensor obtains the heat source points within the AI ​​valve detection radius in real time and obtains the distance between the heat source point closest to the AI ​​valve and the AI ​​valve; The AI ​​valve physical position offset data includes the geographic location coordinate deviation and the tilt angle deviation; A BeiDou satellite positioning device and a six-axis gyroscope are installed on the outside of the AI ​​valve, and the physical position offset data of the AI ​​valve is obtained in real time through the installed BeiDou satellite positioning device and the six-axis gyroscope.

4. According to claim 3, a remote valve closing and locking anti-accidental opening system based on an intelligent safety AI valve is characterized in that: The process of the intelligent perception layer obtaining multimodal data includes: The maximum value of the pressure difference at multiple points in the circumferential direction of the valve seat sealing ring of the AI ​​valve, the temperature at the connection between the valve stem and the valve core of the AI ​​valve, the distance between the AI ​​valve and the heat source point closest to the AI ​​valve, the geographical coordinate deviation distance of the AI ​​valve and the inclination angle deviation of the AI ​​valve are integrated to obtain multimodal data, and the obtained multimodal data are imported into the constructed AI valve network virtual model.

5. According to claim 4, a remote valve closing and locking anti-accidental opening system based on an intelligent safety AI valve is characterized in that: The process in which the local decision layer evaluates the obtained multimodal data in real time and determines whether to generate a valve closing and locking request based on the result of the real-time evaluation includes: Obtaining an AI valve evaluation value V through the obtained multimodal data; The obtained AI valve evaluation value is compared with the threshold value V0 for generating the valve closing lock request: When the comparison result is When , a valve closing and locking request is generated; When the comparison result is When the AI ​​valve is open, 6. A remote valve closing and locking anti-accidental opening system based on an intelligent safety AI valve according to claim 5, characterized in that: The process of the local decision layer sending a valve closing and locking request to the cloud collaboration layer includes: Build a fiber optic network as the primary data transmission channel, and a 5G private network and LoRa wireless network as backup data transmission channels. The fiber optic network is used first for sending valve closing and locking requests and multimodal data transmission. When the fiber optic network is unavailable due to failure or interference, it switches to the backup data transmission channel and sends an alarm log to the cloud collaboration layer.

7. The remote valve closing and locking anti-accidental opening system based on the intelligent safety AI valve according to claim 6 is characterized in that: The cloud collaboration layer performs fluid simulation on the AI ​​valve network virtual model and generates a visual display of the results as follows: When a valve closing and locking request is received for a single AI valve or multiple AI valves, the fault point can only be the AI ​​valve that sent the valve closing and locking request and the pipeline connected to the AI ​​valve. In this case, a fluid dynamics simulation is performed only on the AI ​​valve that sent the valve closing and locking request and the pipeline connected to the AI ​​valve within the AI ​​valve network virtual model to obtain the forces generated by the fluid flow on various parts of the AI ​​valve and pipeline. When valve closing and locking requests are received from multiple directly connected AI valves, a fluid dynamics simulation is first performed on all AI valves that have issued valve closing and locking requests and the pipelines connected to the AI ​​valves within the AI ​​valve network virtual model to obtain data on the impact of fluid flow within each AI valve on upstream and downstream AI valves. The impact data includes the impact value of the pressure difference at multiple points along the circumference of the AI ​​valve seat sealing ring, the impact value of the temperature at the connection between the AI ​​valve stem and the valve core, the impact value of the distance of the AI ​​valve's geographical coordinate deviation, and the impact value of the inclination angle deviation of the AI ​​valve. The difference between the multimodal data of each AI valve and the obtained impact data is eliminated to obtain the true multimodal data of each AI valve. Based on the obtained true multimodal data, a fluid dynamics simulation is again performed on all AI valves that have issued valve closing and locking requests and the pipelines connected to the AI ​​valves within the AI ​​valve network virtual model to obtain the forces exerted on the pipelines and the AI ​​valves by the fluid flow. Set risk levels as safety, warning, and danger levels, and set a corresponding load-bearing range for each risk level. The load-bearing range corresponding to the safety level is greater than 0 and less than or equal to D1, the load-bearing range corresponding to the warning level is greater than D1 and less than or equal to D2, and the load-bearing range corresponding to the danger level is greater than D2. D1 is 80% of the upper limit of the component's load-bearing capacity, and D2 is 100% of the upper limit of the component's load-bearing capacity. Match the obtained force with the load-bearing range, complete the risk level division according to the matching results, and visualize the divided risk levels in the AI ​​valve network virtual model.

8. The remote valve closing and locking anti-accidental opening system based on the intelligent safety AI valve according to claim 7 is characterized in that: The process in which the operator at the cloud collaboration layer determines whether to approve the valve closing and locking request based on the obtained visual display results and performs upstream and downstream linkage control includes: When the operator approves the valve closing and locking request, the corresponding AI valve will immediately execute the valve closing and locking operation, and the threshold for generating valve closing and locking requests for the upstream and downstream AI valves of the AI ​​valve being closed and locked will be reduced by 10%. The AI ​​valve's valve closing and locking anti-accidental opening protection mechanism will be activated. At this time, if the corresponding AI valve needs to be opened, the maintenance engineer needs to conduct on-site inspection and confirm that there is no risk and then issue a valve opening request to the operator before it can be opened again. When the operator rejects the valve closing and locking request, the corresponding AI valve does not perform the valve closing and locking operation, and the threshold for the corresponding AI valve to generate the valve closing and locking request is increased by 10%, and the adjacent AI valves are not adjusted.

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