Rising stem gate valve opening and closing state judgment method, system and equipment and storage medium

Abstract

The invention discloses a rising stem gate valve opening and closing state judgment method, system and equipment and a storage medium, and relates to the technical field of rail transit. The method comprises the following steps: training by using a Center Net network improved by a training data set of the rising stem gate valve, and constructing a rising stem gate valve recognition model; acquiring a real-time image of the rising stem gate valve, and inputting the real-time image into the rising stem gate valve identification model for operation to obtain detection frame position information and rising stem gate valve position information; and the opening degree of the rising stem gate valve is calculated according to the detection frame position information and the rising stem gate valve position information, and the opening and closing state of the rising stem gate valve is judged according to the opening degree of the rising stem gate valve. The method can improve the monitoring precision and response speed, and guarantees the safe and efficient operation of the rail transit system.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a method, system, device and storage medium for judging the opening and closing state of a rising-stem gate valve. Background Art

[0002] In the field of rail transit, the pump room is one of the key infrastructures, and its normal operation is related to the safety and efficiency of the entire rail transit system. As a commonly used control element in the pump room, the real-time and accurate judgment of the opening and closing state of the rising stem gate valve is an important guarantee for the smooth operation of the system. At present, the main methods for judging the opening and closing state of the rising stem gate valve include manual inspection, mechanical indicators, limit switches, and sensor-based monitoring systems. However, manual inspection is time-consuming and labor-intensive, and has poor safety; mechanical indicators may cause inaccurate readings due to wear or environmental factors; limit switches may not respond in time in extreme cases, and maintenance is not easy. This is contrary to the current demand for real-time monitoring and high reliability of pump room equipment status. In modern rail transit systems, with the continuous advancement of technology, the requirements for the intelligence and automation of equipment are getting higher and higher. Therefore, a new method for judging the opening and closing state of rising stem gate valves is urgently needed to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to provide a method, system, device and storage medium for judging the opening and closing state of a rising-stem gate valve, which can improve the monitoring accuracy and response speed and ensure the safe and efficient operation of a rail transit system.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for judging the opening and closing state of a rising stem gate valve comprises:

[0006] A training data set of rising-stem gate valves is constructed, and the training data set is input into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model; wherein the pre-training network is constructed based on an improved CenterNet network; the improved CenterNet network comprises: a ResNet50 backbone network, a deconvolution network, a rising-stem gate valve recognition branch, and a key point recognition branch; wherein the ResNet50 backbone network and the deconvolution network are connected in sequence, and the deconvolution network is also connected to the rising-stem gate valve recognition branch and the key point recognition branch, respectively;

[0007] Collecting a real-time image of the rising-stem gate valve, and inputting the real-time image into the rising-stem gate valve recognition model for calculation, to obtain detection frame position information and rising-stem gate valve position information; the detection frame position information includes the horizontal coordinate of the center point, the vertical coordinate of the center point, the height and the width; the rising-stem gate valve position information includes the horizontal coordinate and the vertical coordinate of the handwheel center, and the horizontal coordinate and the vertical coordinate of the valve stem end;

[0008] The rising stem gate valve opening is calculated according to the detection frame position information and the rising stem gate valve position information, and the opening and closing state of the rising stem gate valve is determined according to the rising stem gate valve opening.

[0009] Optionally, constructing a training data set for rising-stem gate valves, and inputting the training data set into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model specifically includes:

[0010] The rising stem gate valve image is used as training input data, and the corresponding annotation information is used as training labels to construct a training data set for the rising stem gate valve; the annotation information includes the center coordinates of the rising stem gate valve, the real frame height of the rising stem gate valve, the real frame width of the rising stem gate valve, the center coordinates of the handwheel, and the coordinates of the valve stem end;

[0011] Build a pre-trained network;

[0012] Based on the Adam optimization algorithm, the training data set is input into the pre-trained network, training is performed with the goal of minimizing the loss between the network output and the training label, and the trained network is determined as a rising-stem gate valve recognition model.

[0013] Optionally, the loss is specifically:

[0014] L total =L det +λ kpt L kpt +λ geo L geo +λ joint L joint

[0015] Among them, L det Identify loss for rising stem gate valve; L kpt is the key point identification loss; L geo is the geometric loss; L joint is the coupling loss; kpt is the loss weight coefficient for key point identification; geo is the geometric loss weight coefficient; joint is the coupling loss weight coefficient.

[0016] Optionally, the deconvolution network includes 3 convolution layers and 3 deconvolution layers; wherein, the first layer is a convolution layer, the convolution kernel is 3×3, the step size is 1, and the output channel is 256; the second layer is a deconvolution layer, the convolution kernel is 4×4, the step size is 2, and the output channel is 256; the third layer is a convolution layer, the convolution kernel is 3×3, the step size is 1, and the output channel is 128; the fourth layer is a deconvolution layer, the convolution kernel is 4×4, the step size is 2, and the output channel is 128; the fifth layer is a convolution layer, the convolution kernel is 3×3, the step size is 1, and the output channel is 128; the sixth layer is a deconvolution layer, the convolution kernel is 4×4, the step size is 2, and the output channel is 64; each layer is padded with 1, and a ReLU activation function is used.

[0017] Optionally, the rising stem gate valve identification branch includes three prediction heads, namely a rising stem gate valve heat map prediction head, a rising stem gate valve size prediction head and a rising stem gate valve offset prediction head;

[0018] Each prediction head includes two convolution layers, specifically: the first layer output channel of the rising stem gate valve heat map prediction head is 64; the second layer output channel is 1; the first layer output channel of the rising stem gate valve size prediction head is 64; the second layer output channel is 2; the first layer output channel of the rising stem gate valve offset prediction head is 64; the second layer output channel is 2; the convolution kernel of each layer of the rising stem gate valve heat map prediction head, the rising stem gate valve size prediction head and the rising stem gate valve offset prediction head are 3×3, the step size is 1, the padding is 1, and the ReLU activation function is used.

[0019] 6. The method for judging the opening and closing state of a rising stem gate valve according to claim 1 is characterized in that the key point identification branch includes two prediction heads, namely a key point heat map prediction head and a key point offset prediction head; wherein the key points include the hand wheel center and the valve stem end;

[0020] Each prediction head includes two convolution layers, specifically: the first layer output channel of the key point heat map prediction head is 64; the second layer output channel is 2; the first layer output channel of the key point offset prediction head is 64; the second layer output channel is 4; the convolution kernel of each layer is 3×3, the step size is 1, the padding is 1, and the ReLU activation function is used.

[0021] Optionally, the calculating the rising stem gate valve opening according to the detection frame position information and the rising stem gate valve position information, and judging the opening and closing state of the rising stem gate valve according to the rising stem gate valve opening specifically includes:

[0022] Set the parameters for all rising stem gate valves to be at the maximum opening: the horizontal coordinate x of the handwheel center a,i With the vertical coordinate x a,i , the horizontal coordinate x of the valve stem end b,i With the vertical coordinate x b,i ;

[0023] Calculate the valve stem extension ratio The opening and closing status of the rising stem gate valve is determined according to the preset opening and closing thresholds.

[0024] The present invention also provides a rising stem gate valve opening and closing state judgment system, comprising:

[0025] A model building unit is used to build a training data set for rising-stem gate valves, and input the training data set into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model; wherein the pre-training network is built based on an improved CenterNet network; the improved CenterNet network includes: a ResNet50 backbone network, a deconvolution network, a rising-stem gate valve recognition branch, and a key point recognition branch; wherein the ResNet50 backbone network and the deconvolution network are connected in sequence, and the deconvolution network is also connected to the rising-stem gate valve recognition branch and the key point recognition branch respectively;

[0026] An image recognition unit is used to collect a real-time image of a rising-stem gate valve, and input the real-time image into the rising-stem gate valve recognition model for calculation to obtain detection frame position information and rising-stem gate valve position information; the detection frame position information includes the horizontal coordinate of the center point, the vertical coordinate of the center point, the height and the width; the rising-stem gate valve position information includes the horizontal coordinate and the vertical coordinate of the handwheel center, and the horizontal coordinate and the vertical coordinate of the valve stem end;

[0027] The state judgment unit is used to calculate the opening of the rising stem gate valve according to the detection frame position information and the rising stem gate valve position information, and judge the opening and closing state of the rising stem gate valve according to the rising stem gate valve opening.

[0028] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned method for judging the opening and closing state of a rising-stem gate valve.

[0029] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for determining the opening and closing state of a rising-stem gate valve.

[0030] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0031] The present invention discloses a method, system, device and storage medium for judging the opening and closing state of a rising-stem gate valve. The method comprises training a CenterNet network improved by a training data set of a rising-stem gate valve to construct a rising-stem gate valve recognition model; collecting a real-time image of the rising-stem gate valve, and inputting the real-time image into the rising-stem gate valve recognition model for calculation to obtain detection frame position information and rising-stem gate valve position information; calculating the opening of the rising-stem gate valve according to the detection frame position information and the rising-stem gate valve position information, and judging the opening and closing state of the rising-stem gate valve according to the opening of the rising-stem gate valve. Compared with traditional methods, the present invention has lower cost and convenient maintenance; at the same time, customized improvements are made for the identification of rising-stem gate valves and their key points, and it has excellent recognition effect, can improve monitoring accuracy and response speed, and ensure the safe and efficient operation of rail transit systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0033] Figure 1 It is a flow chart of the method for judging the opening and closing state of a rising stem gate valve of the present invention;

[0034] Figure 2 Schematic diagram of the structure of the rising stem gate valve identification model in this embodiment;

[0035] Figure 3 Schematic diagram of the detection and judgment results in this embodiment. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0037] The purpose of the present invention is to provide a method, system, device and storage medium for judging the opening and closing state of a rising-stem gate valve, which can improve the monitoring accuracy and response speed and ensure the safe and efficient operation of a rail transit system.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1-Figure 2As shown, the present invention provides a method for judging the opening and closing state of a rising stem gate valve, comprising:

[0040] Step 100: construct a training data set for rising-stem gate valves, and input the training data set into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model; wherein the pre-training network is constructed based on an improved CenterNet network; the improved CenterNet network includes: a ResNet50 backbone network, a deconvolution network, a rising-stem gate valve recognition branch, and a key point recognition branch; wherein the ResNet50 backbone network and the deconvolution network are connected in sequence, and the deconvolution network is also connected to the rising-stem gate valve recognition branch and the key point recognition branch, respectively.

[0041] Step 200: Collect a real-time image of the rising-stem gate valve, and input the real-time image into the rising-stem gate valve recognition model for calculation to obtain detection frame position information and rising-stem gate valve position information; the detection frame position information includes the horizontal coordinate of the center point, the vertical coordinate of the center point, the height and the width; the rising-stem gate valve position information includes the horizontal coordinate and the vertical coordinate of the handwheel center, and the horizontal coordinate and the vertical coordinate of the valve stem end.

[0042] Step 300: Calculate the rising-stem gate valve opening according to the detection frame position information and the rising-stem gate valve position information, and determine the opening and closing state of the rising-stem gate valve according to the rising-stem gate valve opening.

[0043] As a specific implementation method, the construction of the rising stem gate valve identification model in step 100 includes:

[0044] The rising stem gate valve data set in step (110) includes a rising stem gate valve image and annotation information, wherein the annotation information is composed of the center coordinates of the rising stem gate valve, the real frame height of the rising stem gate valve, the real frame width of the rising stem gate valve, the center coordinates of the handwheel, and the coordinates of the end of the rising stem;

[0045] Step (120) uses the rising-stem gate valve data set as input to train the improved CenterNet network until a high-precision rising-stem gate valve identification model is obtained.

[0046] Furthermore, in the step (120), improving the CenterNet network training includes:

[0047] Step (121): The improved CenterNet network uses ResNet50 as the main network, and after passing through the deconvolution network, it is divided into a rising stem gate valve recognition branch and a key point recognition branch.

[0048] The deconvolution network includes 3 convolutional layers and 3 deconvolutional layers, as follows:

[0049] The first layer is a convolution layer with a convolution kernel of 3×3, a stride of 1, and an output channel of 256; the second layer is a deconvolution layer with a convolution kernel of 4×4, a stride of 2, and an output channel of 256; the third layer is a convolution layer with a convolution kernel of 3×3, a stride of 1, and an output channel of 128; the fourth layer is a deconvolution layer with a convolution kernel of 4×4, a stride of 2, and an output channel of 128; the fifth layer is a convolution layer with a convolution kernel of 3×3, a stride of 1, and an output channel of 128; the sixth layer is a deconvolution layer with a convolution kernel of 4×4, a stride of 2, and an output channel of 64; the above layers are padded with 1, and the ReLU activation function is used.

[0050] The rising stem gate valve identification branch includes three prediction heads: rising stem gate valve heat map prediction head, rising stem gate valve size prediction head, and rising stem gate valve offset prediction head; each prediction head includes two convolutional layers, as follows:

[0051] The first layer output channel of the rising stem gate valve heat map prediction head is 64; the second layer output channel is 1; the first layer output channel of the rising stem gate valve size prediction head is 64; the second layer output channel is 2; the first layer output channel of the rising stem gate valve offset prediction head is 64; the second layer output channel is 2; the above convolution kernel is 3×3, the step size is 1, the padding is 1, and the ReLU activation function is used.

[0052] The key point recognition branch includes two prediction heads: a key point heat map prediction head and a key point offset prediction head; each prediction head includes two convolutional layers, as follows:

[0053] The first layer output channel of the key point heat map prediction head is 64; the second layer output channel is 2; the first layer output channel of the key point offset prediction head is 64; the second layer output channel is 4; the above convolution kernel is 3×3, the step size is 1, the padding is 1, and the ReLU activation function is used; the key points include the center of the handwheel and the end of the valve stem.

[0054] Step (122): Loss function L total for:

[0055] L total =L det +λ kpt L kpt +λ geo L geo +λ joint L joint

[0056] Among them, L det Identify loss for rising stem gate valve; L kpt is the key point identification loss; L geo is the geometric loss; L joint is the coupling loss; kpt is the loss weight coefficient for key point identification;geo is the geometric loss weight coefficient; joint is the coupling loss weight coefficient.

[0057] The rising stem gate valve identification loss L det for:

[0058] L det =L hm +λ wh L wh +λ off L off

[0059] Among them, L hm , L wh , L off They are the thermal map loss, size loss and offset loss of rising stem gate valve respectively; λ wh , off are the weight coefficients of size loss and offset loss of rising stem gate valve respectively.

[0060] The rising stem gate valve thermal diagram loss L hm for:

[0061]

[0062] Wherein, N is the target number of rising stem gate valves; Y xyc are the predicted value and true value of the heat map of the rising stem gate valve respectively; α and β are the Focal Loss parameters.

[0063] The rising stem gate valve size loss L wh for:

[0064]

[0065] in, W i They are the predicted size and actual size of rising stem gate valve respectively; ||·|| 1 represents L1 loss.

[0066] The rising stem gate valve offset loss L off for:

[0067]

[0068] in, O i They are the predicted offset and actual offset of the center point of the rising stem gate valve respectively.

[0069] The key point recognition loss function L kpt for:

[0070] Lkpt =L kpt_hm +λ kpt_off L kpt_off

[0071] Among them, L kpt_hm , L kpt_off are the heat map loss and offset loss of key points respectively; kpt_off is the weight coefficient of the key point offset loss.

[0072] The key point heat map loss L kpt_hm for:

[0073]

[0074] Among them, N kpt is the number of key points; Y xyk They are the heat map prediction value and the true value of the key points respectively.

[0075] The keypoint offset loss L kpt_off for:

[0076]

[0077] in, O k,j are the predicted offset and true offset of the jth key point respectively.

[0078] The geometric loss L geo for:

[0079]

[0080] in, d k are the predicted value and the actual value of the distance between the handwheel center and the valve stem end, respectively.

[0081] The coupling loss L joint for:

[0082]

[0083] Among them, P(kpt i |det i ) is the conditional probability that the i-th rising stem gate valve detection frame contains key points.

[0084] Step (123): Using the Adam optimization algorithm, adjust the network parameters until the loss value is stable or reaches a predetermined number of iterations, thereby realizing the construction of a rising stem gate valve identification model.

[0085] As a specific implementation, obtaining the detection result in step 200 includes:

[0086] Step (210): collecting a real-time image of the rising-stem gate valve and inputting it into the rising-stem gate valve identification model.

[0087] Step (220): Decode the output of the rising stem gate valve identification model to obtain a detection result; the detection result is a vector set R = {r i |i=1,2,...N};the i-th vector r i =(x d,i ,y d,i ,h i ,w i ,x s,i ,y s,i ,x e,i ,y e,i ), where x d,i ,y d,i ,h i ,w i They are the horizontal coordinate, vertical coordinate, height and width of the center point of the rising stem gate valve detection frame; x s,i ,y s,i are the horizontal and vertical coordinates of the handwheel center respectively; x e,i ,y e,i are the horizontal and vertical coordinates of the valve stem end respectively.

[0088] As a specific implementation, obtaining the opening and closing state of the rising stem gate valve in step 300 includes:

[0089] Step (310): Preset parameters for all rising stem gate valves to be at maximum opening, including: the horizontal coordinate x of the center of the hand wheel a,i With the vertical coordinate x a,i , the horizontal coordinate x of the valve stem end b,i With the vertical coordinate x b,i .

[0090] Step (32) Calculate the valve stem extension ratio The opening and closing status of the rising stem gate valve is determined according to the preset opening and closing thresholds.

[0091] Based on the above technical solution, the following is provided: Figure 3 The embodiment shown.

[0092] The steps of judging the opening and closing state of a rising-stem gate valve using the improved CenterNet mainly include: step (1) constructing a rising-stem gate valve data set, and obtaining a rising-stem gate valve recognition model by training the improved CenterNet network; step (2) collecting a real-time image of the rising-stem gate valve, inputting it into the rising-stem gate valve recognition model, and obtaining an output result; step (3) calculating the opening of the rising-stem gate valve based on the output result, and judging the opening and closing state of the rising-stem gate valve.

[0093] Specifically, the step (1) comprises:

[0094] Step (11): the rising stem gate valve data set includes a rising stem gate valve image and annotation information, wherein the annotation information consists of the rising stem gate valve center coordinates, the rising stem gate valve real frame height, the rising stem gate valve real frame width, the hand wheel center coordinates, and the rising stem end coordinates;

[0095] Step (12): Using the rising-stem gate valve dataset as input, the improved CenterNet network is trained until a high-precision rising-stem gate valve recognition model is obtained.

[0096] Specifically, the collection of rising-stem gate valve images in step (11) includes different angles under natural light and light illumination. The data set has 8,500 images and labels, and the training set, validation set, and test set account for 80%, 10%, and 10%, respectively.

[0097] Specifically, the improved CenterNet network training in step (12) includes:

[0098] Step (122): Key point recognition loss weight coefficient λ kpt is 1, the geometric loss weight coefficient λ geo is 0.1, the coupling loss weight coefficient λ joint is 0.1, the size loss weight coefficient λ of the rising stem gate valve wh is 0.1, the offset loss weight coefficient λ of the rising stem gate valve off is 1, the weight coefficient λ of the key point offset loss kpt_off is 1, the Focal Loss parameter α is 2, β is 4, the standard deviation of the Gaussian kernel when generating the heat map is 1 / 3, and the positive sample radius is 2.

[0099] Step (123): Use the Adam optimizer with a learning rate of 1.25×10 -5 The predetermined number of iterations is 100 steps, the first-order moment decay rate is 0.9, the second-order moment decay rate is 0.99, and the small constant is 10 -8 .

[0100] Further, the detection result is obtained by using step (22). The image of the embodiment contains two rising-stem gate valves, and the results include: the number of rising-stem gate valves N is 2, and the result vector corresponding to the first rising-stem gate valve is r 1 =(x d,1 ,y d,1 ,h 1 ,w 1 ,x s,1 ,y s,1 ,x e,1 ,y e,1 ), where x d,1 is 190, yd,1 is 192,h 1 is 143, w 1 =170, x s,1 is 245, y s,1 is 198, x e,1 is 121, y e,1 is 193; the corresponding result vector of the second rising stem gate valve is r 2 =(x d,2 ,y d,2 ,h 2 ,w 2 ,x s,2 ,y s,2 ,x e,2 ,y e,2 ), where x d,2 is 800, y d,2 391,h 2 is 313, w 2 is 391, x s,2 747,y s,2 is 381, x e,2 is 966, y e,2 It is 433.

[0101] Specifically, the opening and closing state of the rising stem gate valve is obtained by using step (3), and the results of the embodiment include:

[0102] Step (31): Preset the parameters of all rising stem gate valves at the maximum opening, the center coordinate of the handwheel (x a,1 ,y a,1 )=(251,195),(x a,2 ,y a,2 )=(748,379), stem end coordinate (x b,1 ,y b,1 )=(21,190),(x b,2 ,y b,2 )=(988,441).

[0103] Step (32): Calculate the valve stem extension ratio k 1 is 0.539, k 2 is 0.908, and the preset opening and closing threshold is 0.6. Therefore, the stem extension ratio k of the first rising stem gate valve 1 is less than the opening and closing threshold, the state is closed; the stem extension ratio k of the second rising stem gate valve 2 If it is greater than the opening and closing threshold, the state is open.

[0104] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0105] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for judging the opening and closing state of a rising stem gate valve, characterized in that: include: A training data set of rising-stem gate valves is constructed, and the training data set is input into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model; wherein the pre-training network is constructed based on an improved CenterNet network; the improved CenterNet network comprises: a ResNet50 backbone network, a deconvolution network, a rising-stem gate valve recognition branch, and a key point recognition branch; wherein the ResNet50 backbone network and the deconvolution network are connected in sequence, and the deconvolution network is also connected to the rising-stem gate valve recognition branch and the key point recognition branch, respectively; Collecting a real-time image of the rising-stem gate valve, and inputting the real-time image into the rising-stem gate valve recognition model for calculation, to obtain detection frame position information and rising-stem gate valve position information; the detection frame position information includes the horizontal coordinate of the center point, the vertical coordinate of the center point, the height and the width; the rising-stem gate valve position information includes the horizontal coordinate and the vertical coordinate of the handwheel center, and the horizontal coordinate and the vertical coordinate of the valve stem end; The rising stem gate valve opening is calculated according to the detection frame position information and the rising stem gate valve position information, and the opening and closing state of the rising stem gate valve is determined according to the rising stem gate valve opening.

2. The method for judging the opening and closing state of a rising stem gate valve according to claim 1, characterized in that: The method of constructing a training data set for rising-stem gate valves and inputting the training data set into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model specifically includes: The rising stem gate valve image is used as training input data, and the corresponding annotation information is used as training labels to construct a training data set for the rising stem gate valve; the annotation information includes the center coordinates of the rising stem gate valve, the real frame height of the rising stem gate valve, the real frame width of the rising stem gate valve, the center coordinates of the handwheel, and the coordinates of the valve stem end; Build a pre-trained network; Based on the Adam optimization algorithm, the training data set is input into the pre-trained network, training is performed with the goal of minimizing the loss between the network output and the training label, and the trained network is determined as a rising-stem gate valve recognition model.

3. The method for judging the opening and closing state of a rising stem gate valve according to claim 2, characterized in that: The losses are specifically: L total =L det +λ kpt L kpt +λ geo L geo +λ joint L joint Among them, L det Identify loss for rising stem gate valve; L kpt is the key point identification loss; L geo is the geometric loss; L joint is the coupling loss; kpt is the loss weight coefficient for key point identification; geo is the geometric loss weight coefficient; joint is the coupling loss weight coefficient.

4. The method for judging the opening and closing state of a rising stem gate valve according to claim 1, characterized in that: The deconvolution network includes 3 convolutional layers and 3 deconvolutional layers; wherein, the first layer is a convolutional layer, the convolution kernel is 3×3, the step size is 1, and the output channel is 256; the second layer is a deconvolutional layer, the convolution kernel is 4×4, the step size is 2, and the output channel is 256; the third layer is a convolutional layer, the convolution kernel is 3×3, the step size is 1, and the output channel is 128; the fourth layer is a deconvolutional layer, the convolution kernel is 4×4, the step size is 2, and the output channel is 128; the fifth layer is a convolutional layer, the convolution kernel is 3×3, the step size is 1, and the output channel is 128; the sixth layer is a deconvolutional layer, the convolution kernel is 4×4, the step size is 2, and the output channel is 64; each layer is padded with 1, and a ReLU activation function is used.

5. The method for judging the opening and closing state of a rising stem gate valve according to claim 1, characterized in that: The rising stem gate valve identification branch includes three prediction heads, namely, a rising stem gate valve heat map prediction head, a rising stem gate valve size prediction head and a rising stem gate valve offset prediction head; Each prediction head includes two convolution layers, specifically: the first layer output channel of the rising stem gate valve heat map prediction head is 64; the second layer output channel is 1; the first layer output channel of the rising stem gate valve size prediction head is 64; the second layer output channel is 2; the first layer output channel of the rising stem gate valve offset prediction head is 64; the second layer output channel is 2; the convolution kernel of each layer of the rising stem gate valve heat map prediction head, the rising stem gate valve size prediction head and the rising stem gate valve offset prediction head are 3×3, the step size is 1, the padding is 1, and the ReLU activation function is used.

6. The method for judging the opening and closing state of a rising stem gate valve according to claim 1, characterized in that: The key point recognition branch includes two prediction heads, namely a key point heat map prediction head and a key point offset prediction head; wherein the key points include the hand wheel center and the valve stem end; Each prediction head includes two convolution layers, specifically: the first layer output channel of the key point heat map prediction head is 64; the second layer output channel is 2; the first layer output channel of the key point offset prediction head is 64; the second layer output channel is 4; the convolution kernel of each layer is 3×3, the step size is 1, the padding is 1, and the ReLU activation function is used.

7. The method for judging the opening and closing state of a rising stem gate valve according to claim 1, characterized in that: The calculating the rising stem gate valve opening according to the detection frame position information and the rising stem gate valve position information, and judging the opening and closing state of the rising stem gate valve according to the rising stem gate valve opening, specifically includes: Set the parameters for all rising stem gate valves to be at the maximum opening: the horizontal coordinate x of the handwheel center a,i With the vertical coordinate x a,i , the horizontal coordinate x of the valve stem end b,i With the vertical coordinate x b,i ; Calculate the stem extension ratio The opening and closing status of the rising stem gate valve is determined according to the preset opening and closing thresholds.

8. A rising stem gate valve opening and closing state judgment system, characterized in that: include: A model building unit is used to build a training data set for rising-stem gate valves, and input the training data set into a pre-training network for parameter optimization to obtain a trained rising-stem gate valve recognition model; wherein the pre-training network is built based on an improved CenterNet network; the improved CenterNet network includes: a ResNet50 backbone network, a deconvolution network, a rising-stem gate valve recognition branch, and a key point recognition branch; wherein the ResNet50 backbone network and the deconvolution network are connected in sequence, and the deconvolution network is also connected to the rising-stem gate valve recognition branch and the key point recognition branch respectively; An image recognition unit is used to collect a real-time image of a rising-stem gate valve, and input the real-time image into the rising-stem gate valve recognition model for calculation to obtain detection frame position information and rising-stem gate valve position information; the detection frame position information includes the horizontal coordinate of the center point, the vertical coordinate of the center point, the height and the width; the rising-stem gate valve position information includes the horizontal coordinate and the vertical coordinate of the handwheel center, and the horizontal coordinate and the vertical coordinate of the valve stem end; The state judgment unit is used to calculate the opening of the rising stem gate valve according to the detection frame position information and the rising stem gate valve position information, and judge the opening and closing state of the rising stem gate valve according to the rising stem gate valve opening.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for judging the opening and closing state of a rising-stem gate valve according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: It stores a computer program, and when the computer program is executed by a processor, the method for judging the opening and closing state of a rising-stem gate valve as described in any one of claims 1 to 7 is implemented.

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