Training method for determining model of effusion working condition, method and device for determining effusion working condition

Abstract

The present application discloses a training method for a model for determining a fluid accumulation condition, a method for determining a fluid accumulation condition, and a device, and belongs to the technical field of gathering and transportation pipeline networks. The training method for a model for determining a fluid accumulation condition provided by the present application first obtains the characteristic values ​​of the pipeline network operation parameters within a sample period and the sample condition labels corresponding to the sample time nodes, obtains the characteristic values ​​of the pipeline network operation parameters at the sample time nodes based on the characteristic values ​​of the pipeline network operation parameters within the sample period, and obtains the correlation coefficients corresponding to the sample time nodes, and performs model training based on the sample condition labels, characteristic values, and correlation coefficients corresponding to the sample time nodes to obtain a model for determining a fluid accumulation condition. It can be seen that this method performs model training based on real pipeline network operation data and fluid accumulation conditions. Therefore, the trained model can accurately determine the fluid accumulation conditions of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation conditions.

Description

Technical Field

[0001] The present application relates to the technical field of gathering and transportation pipeline networks, and in particular to a training method for a model for determining a liquid accumulation operating condition, and a method and device for determining a liquid accumulation operating condition. Background Art

[0002] When natural gas is transported through a natural gas pipeline network, free water and heavy hydrocarbons in the natural gas are easily liquefied and precipitated due to temperature and pressure along the pipeline. These components are then retained in low-lying pipelines, forming liquid accumulation. This accumulation not only increases pipeline energy consumption and reduces pipeline transportation efficiency, but also accelerates pipeline corrosion and poses a risk of gas leakage. While pigging can effectively control liquid accumulation, it also carries risks, such as wear and tear on the pipeline's inner wall. Therefore, it is necessary to assess the liquid accumulation conditions and determine the timing of pigging.

[0003] In related technologies, simulation software is mainly used to simulate the operating parameters of the pipeline, such as temperature and pressure distribution, to obtain the simulated amount of liquid accumulation, and then the liquid accumulation working condition is determined based on the simulated amount of liquid accumulation.

[0004] However, the fluid accumulation condition in the related art is determined based on the fluid accumulation simulation quantity, which is not real data and is not accurate enough, resulting in a low accuracy rate of the determined fluid accumulation condition. Summary of the Invention

[0005] The present invention provides a method for training a model for determining a fluid accumulation condition, a method for determining a fluid accumulation condition, and a device for determining a fluid accumulation condition, which can improve the accuracy of determining a fluid accumulation condition. The specific technical solution is as follows:

[0006] In one aspect, a method for training a fluid accumulation condition determination model is provided, the method comprising:

[0007] Acquire multiple sample operating condition labels; wherein the sample operating condition labels are used to indicate whether there is fluid accumulation in the sample pipe network at the sample time node;

[0008] For each sample time node, a plurality of pipe network operating parameters of the sample pipe network within a sample period are obtained; wherein the sample period is located before the sample time node in terms of time sequence;

[0009] For each pipe network operation parameter, obtain a characteristic value of the pipe network operation parameter in the sample period; based on the characteristic value of the pipe network operation parameter in the sample period, obtain a characteristic value of the pipe network operation parameter at the sample time node;

[0010] Obtaining a correlation coefficient corresponding to the sample time node; wherein the correlation coefficient is used to represent the degree of correlation between any two pipe network operation parameters within the sample period;

[0011] Based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node, model training is performed to obtain a fluid accumulation operating condition determination model; wherein, the fluid accumulation operating condition determination model is used to determine the fluid accumulation operating condition of the target pipeline network.

[0012] In another aspect, a method for determining a fluid accumulation operating condition is provided, the method comprising:

[0013] Obtain multiple pipeline network operating parameters of the target pipeline network within the target period before the current time node;

[0014] For each pipe network operation parameter, obtain a characteristic value of the pipe network operation parameter within the target time period; based on the characteristic value of the pipe network operation parameter within the target time period, obtain a characteristic value of the pipe network operation parameter at the current time node;

[0015] Obtaining the correlation coefficient corresponding to the current time node;

[0016] Inputting the characteristic value and correlation coefficient corresponding to the current time node into the fluid accumulation operating condition determination model to obtain a first fluid accumulation operating condition;

[0017] Among them, the first fluid accumulation condition is the fluid accumulation condition corresponding to the target pipeline network at the current time node, and the fluid accumulation condition determination model is obtained by training based on the sample condition label, eigenvalue and correlation coefficient corresponding to the sample time node. The sample condition label is used to indicate whether there is fluid accumulation in the sample pipeline network at the sample time node.

[0018] In another aspect, a training device for a fluid accumulation condition determination model is provided, the device comprising:

[0019] A first acquisition module is configured to acquire a plurality of sample operating condition labels, wherein the sample operating condition labels are used to indicate whether there is fluid accumulation in the sample pipe network at a sample time node;

[0020] A second acquisition module is configured to acquire, for each sample time node, a plurality of pipe network operating parameters of the sample pipe network within a sample period; wherein the sample period is located before the sample time node in terms of time sequence;

[0021] A third acquisition module is configured to acquire, for each pipe network operation parameter, a characteristic value of the pipe network operation parameter within the sample period; and based on the characteristic value of the pipe network operation parameter within the sample period, acquire a characteristic value of the pipe network operation parameter at the sample time node;

[0022] A fourth acquisition module is used to obtain a correlation coefficient corresponding to the sample time node; wherein the correlation coefficient is used to represent the degree of correlation between any two pipe network operation parameters within the sample time period;

[0023] The training module is used to perform model training based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node to obtain a fluid accumulation condition determination model; wherein, the fluid accumulation condition determination model is used to determine the fluid accumulation condition of the target pipeline network.

[0024] In a possible implementation, the sample period includes a first sample period and a second sample period, and a boundary value of the first sample period is greater than a boundary value of the second sample period;

[0025] The third acquisition module is used to obtain the mean value of the pipeline network operation parameter in the first sample period to obtain a first mean value feature; obtain the changing trend of the pipeline network operation parameter in the first sample period to obtain a first trend feature; obtain the difference between the pipeline network operation parameter corresponding to the sample time node and the mean value to obtain a first deviation feature; based on the first mean feature, the first trend feature and the first deviation feature, obtain the first characteristic value of the pipeline network operation parameter in the first sample period; obtain the second characteristic value of the pipeline network operation parameter in the second sample period.

[0026] In another possible implementation, the fourth acquisition module is used to obtain the covariance between the first pipeline network operation parameter and the second pipeline network operation parameter within the sample period; wherein the first pipeline network operation parameter and the second pipeline network operation parameter are any two parameters among the multiple pipeline network operation parameters; obtain the first standard deviation and the second standard deviation; wherein the first standard deviation is the standard deviation of the first pipeline network operation parameter within the sample period, and the second standard deviation is the standard deviation of the second pipeline network operation parameter within the sample period; obtain the product of the first standard deviation and the second standard deviation to obtain a first product value; obtain the ratio of the covariance to the first product value to obtain the correlation coefficient between the first pipeline network operation parameter and the second pipeline network operation parameter within the sample period.

[0027] In another possible implementation, the training module is used to determine the type of fluid accumulation corresponding to the sample time node when the sample operating condition label indicates that there is fluid accumulation in the sample pipe network at the sample time node; and perform model training based on the sample operating condition label, fluid accumulation type, characteristic value and correlation coefficient corresponding to each sample time node to obtain the fluid accumulation condition determination model.

[0028] In another possible implementation, the second acquisition module is used to obtain the gas and water delivery volumes of the sample pipe network during the sample period; determine the ratio of the water delivery volume to the gas delivery volume to obtain the water-gas ratio; determine a first pressure value and a second pressure value of the sample pipe network, the first pressure value being the pressure value of the sample pipe network at the starting end, and the second pressure value being the pressure value of the sample pipe network at the ending end; determine the difference between the first pressure value and the second pressure value to obtain the delivery pressure difference; wherein the multiple pipe network operating parameters include the gas delivery volume, the water delivery volume, the water-gas ratio and the delivery pressure difference.

[0029] In another aspect, a device for determining a fluid accumulation condition is provided, the device comprising:

[0030] A fifth acquisition module is used to acquire multiple pipeline network operating parameters of the target pipeline network within a target period before the current time node;

[0031] a sixth acquisition module, configured to acquire, for each pipeline network operation parameter, a characteristic value of the pipeline network operation parameter within the target time period; and acquire, based on the characteristic value of the pipeline network operation parameter within the target time period, a characteristic value of the pipeline network operation parameter at the current time node;

[0032] A seventh acquisition module, configured to acquire a correlation coefficient corresponding to the current time node;

[0033] A first determination module is configured to input the characteristic value and correlation coefficient corresponding to the current time node into a fluid accumulation operating condition determination model to obtain a first fluid accumulation operating condition;

[0034] Among them, the first fluid accumulation condition is the fluid accumulation condition corresponding to the target pipeline network at the current time node, and the fluid accumulation condition determination model is obtained by training based on the sample condition label, eigenvalue and correlation coefficient corresponding to the sample time node. The sample condition label is used to indicate whether there is fluid accumulation in the sample pipeline network at the sample time node.

[0035] In one possible implementation, the first determination module is used to input the characteristic value and correlation coefficient corresponding to the current time node into the fluid effusion operating condition determination model to obtain the second fluid effusion operating condition; based on at least one of the third fluid effusion operating condition and the fourth fluid effusion operating condition, the second fluid effusion operating condition is corrected to obtain the first fluid effusion operating condition; wherein, the target period includes a first target period and a second target period, the third fluid effusion operating condition is the fluid effusion operating condition corresponding to each time node within the first target period, and the fourth fluid effusion operating condition is the fluid effusion operating condition corresponding to each time node within the second target period.

[0036] In another possible implementation, the first determination module is used to determine at least one of a first quantity and a second quantity when the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node; wherein the first quantity is the quantity of a third fluid accumulation condition indicating that there is fluid accumulation, and the second quantity is the quantity of a fourth fluid accumulation condition indicating that there is fluid accumulation; when at least one of the first quantity and the second quantity is greater than a corresponding preset threshold, the second fluid accumulation condition is corrected to obtain the first fluid accumulation condition; wherein the first fluid accumulation condition indicates that there is fluid accumulation at the current time node.

[0037] In another possible implementation, the first determination module is used to determine a third quantity when the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node; wherein the third quantity is the number of the third fluid accumulation condition or the fourth fluid accumulation condition in which fluid accumulation exists continuously before the current time node; when the third quantity is greater than a first preset threshold, the second fluid accumulation condition is corrected to obtain the first fluid accumulation condition; wherein, the first fluid accumulation condition indicates that there is fluid accumulation at the current time node.

[0038] In another possible implementation, the apparatus further includes:

[0039] An eighth acquisition module, configured to acquire effusion types corresponding to multiple time nodes;

[0040] A modification module is used to modify the effusion type corresponding to the current time node to the effusion type corresponding to the previous time node if the effusion type corresponding to the current time node is different from the effusion type corresponding to the previous time node and the next time node, and the effusion type corresponding to the previous time node and the next time node are the same.

[0041] On the other hand, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the training method for determining the effusion working condition model or the effusion working condition determination method described in any of the above items.

[0042] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the training method for determining the effusion operating condition model or the effusion operating condition determination method described in any of the above items.

[0043] On the other hand, a computer program product is provided, in which at least one program code is stored, and the at least one program code is loaded and executed by a processor to implement the training method for determining the effusion working condition model or the effusion working condition determination method described in any of the above items.

[0044] The embodiment of the present application provides a training method for a model for determining a fluid accumulation condition. The method first obtains the characteristic values ​​of the pipeline network operation parameters within a sample period and the sample condition labels corresponding to the sample time nodes. Based on the characteristic values ​​of the pipeline network operation parameters within the sample period, the characteristic values ​​of the pipeline network operation parameters at the sample time nodes are obtained. Furthermore, the correlation coefficients corresponding to the sample time nodes are obtained. The model is trained based on the sample condition labels, characteristic values, and correlation coefficients corresponding to the sample time nodes to obtain a model for determining a fluid accumulation condition. It can be seen that the method performs model training based on real pipeline network operation data and fluid accumulation conditions. Therefore, the trained model can accurately determine the fluid accumulation condition of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of an implementation environment of a method for training a fluid accumulation condition determination model provided in an embodiment of the present application;

[0046] Figure 2 This is a flow chart of a method for training a fluid accumulation condition determination model provided in an embodiment of the present application;

[0047] Figure 3 This is a flow chart of a method for training a fluid accumulation condition determination model provided in an embodiment of the present application;

[0048] Figure 4 This is a flow chart of a method for determining a fluid accumulation condition provided in an embodiment of the present application;

[0049] Figure 5 This is a flow chart of a method for determining a fluid accumulation condition provided in an embodiment of the present application;

[0050] Figure 6 This is a schematic diagram of determining a fluid accumulation condition provided by an embodiment of the present application;

[0051] Figure 7 1 is a schematic structural diagram of a training device for determining a fluid accumulation working condition model provided in an embodiment of the present application;

[0052] Figure 8 This is a schematic structural diagram of a device for determining a fluid accumulation condition provided in an embodiment of the present application;

[0053] Figure 9 This is a structural block diagram of a terminal provided in an embodiment of the present application;

[0054] Figure 10 This is a structural block diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the technical solutions and advantages of the present application clearer, the implementation methods of the present application are described in further detail below.

[0056] The terms "first," "second," "third," and "fourth," etc. in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0057] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the pipeline network operating parameters, characteristic values, and correlation coefficients involved in this application are all obtained with full authorization.

[0058] Figure 1 Schematic diagram of the implementation environment of a training method for determining a fluid accumulation condition model provided in an embodiment of the present application. Figure 1 The implementation environment includes: an electronic device, which can be provided as a terminal 101, a server 102, or a terminal 101 and a server 102. If the electronic device is provided as a terminal 101 and a server 102, the terminal 101 and the server 102 can be connected via a wireless or wired network. In the embodiments of the present application, the electronic device is not specifically limited.

[0059] If the electronic device is provided as terminal 101, terminal 101 performs model training to obtain a fluid accumulation condition determination model. The fluid accumulation condition determination model can be deployed in terminal 101 or in another terminal, so that terminal 101 or another terminal can determine the fluid accumulation condition of the target pipe network using the fluid accumulation condition determination model.

[0060] If the electronic device is provided as server 102, server 102 performs model training to obtain a fluid accumulation condition determination model. The fluid accumulation condition determination model can be deployed in terminal 101, so that terminal 101 can determine the fluid accumulation condition of the target pipe network through the fluid accumulation condition determination model.

[0061] If the electronic device is provided as a terminal 101 and a server 102, the server 102 performs model training to obtain a fluid accumulation condition determination model, and then the fluid accumulation condition determination model is deployed in the terminal 101, so that the terminal 101 can determine the fluid accumulation condition of the target pipeline network through the fluid accumulation condition determination model.

[0062] The terminal 101 is at least one of a mobile phone, a tablet computer, a PC (Personal Computer), an intelligent voice interaction device, and an in-vehicle terminal. The server 102 can be at least one of a single server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0063] Figure 2 This is a flowchart of a training method for a fluid accumulation condition determination model provided by an embodiment of the present application, which is executed by a first electronic device, see Figure 2 , the method comprising:

[0064] Step 201: A first electronic device obtains a plurality of sample operating condition labels.

[0065] The sample operating condition label is used to indicate whether there is liquid accumulation in the sample pipeline network at the sample time node. The sample pipeline network may be a pipeline network for transporting natural gas or a pipeline network for transporting other gases, and there is no specific limitation on this.

[0066] The first electronic device may obtain multiple sample operating condition tags from a sample library, or may obtain multiple sample operating condition tags sent by other devices, which is not specifically limited.

[0067] Step 202: For each sample time node, the first electronic device obtains a plurality of pipe network operating parameters of the sample pipe network within the sample period.

[0068] The sample period is located before the sample time node in terms of time sequence. The sample period includes one or more periods, which are not specifically limited.

[0069] The multiple pipeline network operating parameters include gas flow, water flow, water-gas ratio, and pressure differential. For example, if the sample pipeline network is a natural gas pipeline network, the gas flow is the amount of natural gas delivered by the sample pipeline network, the water flow is the amount of water in the natural gas delivered by the sample pipeline network, the water-gas ratio is the proportion of water in the natural gas, and the pressure differential is the pressure difference between the two ends of the sample pipeline network. Of course, pipeline network operating parameters may also include other parameters, which are not specifically limited to these parameters.

[0070] The first electronic device may obtain multiple pipe network operating parameters of the sample pipe network within the sample period from the sample library, or may obtain multiple pipe network operating parameters of the sample pipe network within the sample period sent by other devices, which is not specifically limited.

[0071] Step 203: For each pipe network operation parameter, the first electronic device obtains a characteristic value of the pipe network operation parameter within a sample period; based on the characteristic value of the pipe network operation parameter within the sample period, obtains a characteristic value of the pipe network operation parameter at a sample time node.

[0072] If the sample period includes multiple periods, for example, the sample period includes a first sample period and a second sample period, the first electronic device obtains a characteristic value of the pipe network operation parameter in the first sample period and a characteristic value in the second sample period. The characteristic value of the pipe network operation parameter in the first sample period includes at least one of a mean characteristic, a trend characteristic, and a deviation characteristic of the pipe network operation parameter in the first sample period, and the characteristic value of the pipe network operation parameter in the second sample period includes at least one of a mean characteristic, a trend characteristic, and a deviation characteristic of the pipe network operation parameter in the second sample period. The first electronic device uses the characteristic value of the pipe network operation parameter in the first sample period and the characteristic value of the pipe network operation parameter in the second sample period as the characteristic value of the pipe network operation parameter at the sample time node.

[0073] If the sample period includes one time period, the first electronic device obtains a characteristic value of the pipe network operation parameter in the time period, and uses the characteristic value of the pipe network operation parameter in the time period as the characteristic value of the pipe network operation parameter at the sample time node.

[0074] Step 204: The first electronic device obtains the correlation coefficient corresponding to the sample time node.

[0075] The correlation coefficient is used to represent the degree of correlation between any two pipe network operation parameters within a sample period. In the embodiment of the present application, the first electronic device can represent the degree of correlation between the two pipe network operation parameters using the Pearson correlation coefficient.

[0076] If the sample period includes a first sample period and a second sample period, the first electronic device obtains the correlation coefficient of any two pipe network operation parameters in the first sample period and the correlation coefficient in the second sample period, and uses the correlation coefficient of any two pipe network operation parameters in the first sample period and the correlation coefficient in the second sample period as the correlation coefficient corresponding to the sample time node.

[0077] Step 205: The first electronic device performs model training based on the sample operating condition label, characteristic value, and correlation coefficient corresponding to each sample time node to obtain a fluid accumulation operating condition determination model.

[0078] The first electronic device uses the sample operating condition label corresponding to each sample time node as a training target, and uses the eigenvalue and correlation coefficient corresponding to each sample time node as a training sample to perform model training to obtain a fluid accumulation operating condition determination model.

[0079] The embodiment of the present application provides a training method for a model for determining a fluid accumulation condition. The method first obtains the characteristic values ​​of the pipeline network operation parameters within a sample period and the sample condition labels corresponding to the sample time nodes. Based on the characteristic values ​​of the pipeline network operation parameters within the sample period, the characteristic values ​​of the pipeline network operation parameters at the sample time nodes are obtained. Furthermore, the correlation coefficients corresponding to the sample time nodes are obtained. The model is trained based on the sample condition labels, characteristic values, and correlation coefficients corresponding to the sample time nodes to obtain a model for determining a fluid accumulation condition. It can be seen that the method performs model training based on real pipeline network operation data and fluid accumulation conditions. Therefore, the trained model can accurately determine the fluid accumulation condition of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation condition.

[0080] Figure 2 The figure shows a process for training a fluid accumulation condition determination model provided by the present application. The training method for the fluid accumulation condition determination model provided by the present application is further elaborated below. Figure 3 This is a flowchart of a training method for a fluid accumulation condition determination model provided by an embodiment of the present application, which is executed by a first electronic device, see Figure 3 , the method comprising:

[0081] Step 301: The first electronic device obtains a plurality of sample operating condition labels.

[0082] This step is similar to step 201 and will not be described again here.

[0083] Step 302: For each sample time node, the first electronic device obtains a plurality of pipe network operating parameters of the sample pipe network within the sample period.

[0084] The sample period is located before the sample time node in terms of time sequence. The sample period includes one or more periods. When the sample period includes multiple periods, the number of multiple periods can be set and changed as needed. In the embodiment of the present application, only the sample period including the first sample period and the second sample period is used as an example for explanation.

[0085] If the sample period includes a first sample period and a second sample period, the boundary value of the first sample period is greater than the boundary value of the second sample period. For example, if the sample time node is A, the second sample period is the period from 7 days before time node A to time node A, and the first sample period is the period from 30 days before time node A to time node A.

[0086] If the sample period includes a first sample period and a second sample period, the first electronic device obtains a plurality of pipe network operating parameters of the sample pipe network in the first sample period and a plurality of pipe network operating parameters of the sample pipe network in the second sample period.

[0087] For a first sample period, the first electronic device obtains the gas and water flow rates of the sample pipe network during the first sample period; determines the ratio of the water flow rate to the gas flow rate to obtain a water-gas ratio; and determines the difference between a pressure value at a starting end and a pressure value at an ending end of the sample pipe network to obtain a pressure differential. The multiple pipe network operating parameters include gas flow rate, water flow rate, water-gas ratio, and pressure differential.

[0088] The process of the first electronic device acquiring the multiple pipe network operating parameters in the second sample period is the same as the process of acquiring the multiple pipe network operating parameters in the first sample period, which will not be repeated here.

[0089] If the sample period includes one period, the first electronic device directly obtains the gas and water delivery volumes within the sample period; determines the water-gas ratio based on the gas and water delivery volumes; and determines the pressure difference at both ends of the sample pipe network to obtain the delivery pressure difference.

[0090] One thing that needs to be explained is that after the first electronic device obtains multiple pipeline network operating parameters, it can directly execute step 303, or it can first pre-process the multiple pipeline network operating parameters, for example, eliminate abnormal values, missing values, etc., and execute step 303 based on the pre-processed multiple pipeline network operating parameters.

[0091] Step 303: For each pipe network operation parameter, the first electronic device obtains a first characteristic value of the pipe network operation parameter within a first sample period.

[0092] This step can be achieved by following the steps (1) to (4), including:

[0093] (1) For each pipe network operation parameter, the first electronic device obtains the mean value of the pipe network operation parameter within a first sample period to obtain a first mean value feature.

[0094] For example, if the pipe network operation parameter is gas transmission volume, the first electronic device determines the gas transmission volume for each day in the 30 days prior to the sample time node, determines the average of the gas transmission volume during these 30 days, and obtains the first average characteristic of the gas transmission volume. If the pipe network operation parameter is water transmission volume, the first electronic device determines the water transmission volume for each day in the 30 days prior to the sample time node, determines the average of the water transmission volume during these 30 days, and obtains the first average characteristic of the water transmission volume. If the pipe network operation parameter is water-gas ratio, the first electronic device determines the water-gas ratio for each day in the 30 days prior to the sample time node, determines the average of the water-gas ratio during these 30 days, and obtains the first average characteristic of the water-gas ratio. If the pipe network operation parameter is transmission pressure difference, the first electronic device determines the transmission pressure difference for each day in the 30 days prior to the sample time node, determines the average of the transmission pressure difference during these 30 days, and obtains the first average characteristic of the transmission pressure difference.

[0095] (2) The first electronic device obtains a change trend of the pipe network operating parameter within a first sample period to obtain a first trend feature.

[0096] The first electronic device determines whether the pipeline network operation parameter shows an upward trend or a downward trend within the first sample period. If it shows an upward trend, the first electronic device assigns a first trend characteristic of the pipeline network operation parameter to 1; if it shows a downward trend, the first electronic device assigns a first trend characteristic of the pipeline network operation parameter to -1.

[0097] For example, if the pipeline network operation parameter is gas transmission volume, the first electronic device determines the changing trend of gas transmission volume in the 30 days before the sample time node. If it is an upward trend, the first trend characteristic of gas transmission volume is determined to be 1; if it is a downward trend, the first trend characteristic of gas transmission volume is determined to be -1. Similarly, if the pipeline network operation parameter is water transmission volume, the first electronic device determines the changing trend of water transmission volume in the 30 days before the sample time node and obtains the first trend characteristic of water transmission volume. If the pipeline network operation parameter is water-gas ratio, the first electronic device determines the changing trend of water-gas ratio in the 30 days before the sample time node and obtains the first trend characteristic of water-gas ratio. If the pipeline network operation parameter is transmission pressure difference, the first electronic device determines the changing trend of transmission pressure difference in the 30 days before the sample time node and obtains the first trend characteristic of transmission pressure difference.

[0098] (3) The first electronic device obtains the difference between the pipe network operation parameter corresponding to the sample time node and the mean value to obtain a first deviation feature.

[0099] For example, if the pipe network operation parameter is gas transmission volume, the first electronic device obtains the difference between the gas transmission volume corresponding to the sample time node and the average gas transmission volume to obtain the first deviation characteristic of the gas transmission volume. If the pipe network operation parameter is water transmission volume, the first electronic device obtains the difference between the water transmission volume corresponding to the sample time node and the average water transmission volume to obtain the first deviation characteristic of the water transmission volume. If the pipe network operation parameter is water-gas ratio, the first electronic device obtains the difference between the water-gas ratio corresponding to the sample time node and the average water-gas ratio to obtain the first deviation characteristic of the water-gas ratio. If the pipe network operation parameter is transmission pressure difference, the first electronic device obtains the difference between the transmission pressure difference corresponding to the sample time node and the average transmission pressure difference to obtain the first deviation characteristic of the transmission pressure difference.

[0100] (4) The first electronic device obtains a first feature value based on the first mean feature, the first trend feature, and the first deviation feature.

[0101] The first electronic device uses the first mean feature, the first trend feature, and the first deviation feature as the first feature value, that is, the first feature value includes the first mean feature, the first trend feature, and the first deviation feature. Alternatively, the first electronic device uses the sum of the first mean feature, the first trend feature, and the first deviation feature as the first feature value. Alternatively, the first electronic device uses the average value of the first mean feature, the first trend feature, and the first deviation feature as the first feature value. In the embodiments of the present application, this is not specifically limited.

[0102] Step 304: The first electronic device obtains a second characteristic value of the pipe network operation parameter within a second sample period.

[0103] In one possible implementation, the first electronic device obtains a second mean characteristic, a second trend characteristic, and a second deviation characteristic of the pipeline network operation parameter within a second sample period, and determines a second characteristic value based on the second mean characteristic, the second trend characteristic, and the second deviation characteristic.

[0104] For example, if the second sample period is the period from 7 days before the sample time node to the sample time node, and the pipeline network operation parameter is gas transmission volume, the first electronic device obtains the gas transmission volume for each of the 7 days before the sample time node, determines the average of the gas transmission volume for these 7 days, and obtains the second average characteristic of the gas transmission volume. If the pipeline network operation parameter is water transmission volume, the first electronic device obtains the water transmission volume for each of the 7 days before the sample time node, determines the average of the water transmission volume for these 7 days, and obtains the second average characteristic of the water transmission volume. If the pipeline network operation parameter is water-gas ratio, the first electronic device obtains the water-gas ratio for each of the 7 days before the sample time node, determines the average of the water-gas ratio for these 7 days, and obtains the second average characteristic of the water-gas ratio. If the pipeline network operation parameter is transmission pressure difference, the first electronic device obtains the transmission pressure difference for each of the 7 days before the sample time node, determines the average of the transmission pressure difference for these 7 days, and obtains the second average characteristic of the transmission pressure difference.

[0105] The process of the first electronic device acquiring the second trend feature and the second deviation feature is the same as the process of acquiring the first trend feature and the first deviation feature in step 303, and is not repeated here.

[0106] In another possible implementation, the first electronic device obtains a second mean characteristic of the pipeline network operation parameter within a second sample period as the second characteristic value.

[0107] Since the second sample period includes a shorter time period, the change trend and deviation may not be obvious, and therefore the first electronic device may directly use the second mean feature as the second feature value.

[0108] Step 305: The first electronic device obtains a characteristic value of the pipe network operation parameter at a sample time node based on the first characteristic value and the second characteristic value.

[0109] The first electronic device uses the first characteristic value and the second characteristic value as the characteristic value of the pipe network operation parameter at the sample time node, that is, the characteristic value of the pipe network operation parameter at the sample time node includes the first characteristic value and the second characteristic value.

[0110] Step 306: The first electronic device obtains the correlation coefficient corresponding to the sample time node.

[0111] The correlation coefficient is used to indicate the degree of correlation between any two pipe network operating parameters within a sample period.

[0112] This step can be achieved by following the steps (1) to (4), including:

[0113] (1) The first electronic device obtains the covariance between the first pipe network operating parameter and the second pipe network operating parameter within a sample period.

[0114] The first pipe network operation parameter and the second pipe network operation parameter are any two parameters among a plurality of pipe network operation parameters.

[0115] If the sample period includes a first sample period and a second sample period, the first electronic device obtains the covariance between the first pipe network operating parameter and the second pipe network operating parameter in the first sample period and the covariance between the first pipe network operating parameter and the second pipe network operating parameter in the second sample period.

[0116] In the embodiment of the present application, the manner in which the first electronic device determines the covariance is not specifically limited.

[0117] (2) The first electronic device obtains the first standard deviation and the second standard deviation.

[0118] The first standard deviation is the standard deviation of the first pipe network operating parameter within the sample period, and the second standard deviation is the standard deviation of the second pipe network operating parameter within the sample period.

[0119] If the sample period includes a first sample period and a second sample period, the first standard deviation includes the standard deviation of the first pipeline network operation parameter in the first sample period and the standard deviation in the second sample period, and the second standard deviation includes the standard deviation of the second pipeline network operation parameter in the first sample period and the standard deviation in the second sample period.

[0120] (3) The first electronic device obtains the product of the first standard deviation and the second standard deviation to obtain a first product value.

[0121] If the sample period includes a first sample period and a second sample period, the first electronic device determines the product of the standard deviation of the first pipe network operation parameter in the first sample period and the standard deviation of the second pipe network operation parameter in the first sample period to obtain a second product value; determines the product of the standard deviation of the first pipe network operation parameter in the second sample period and the standard deviation of the second pipe network operation parameter in the second sample period to obtain a third product value, and the first product value includes the second product value and the third product value.

[0122] (4) The first electronic device obtains the ratio of the covariance to the first product value, and obtains the correlation coefficient between the first pipe network operation parameter and the second pipe network operation parameter within the sample period.

[0123] If the sample period includes a first sample period and a second sample period, the first electronic device obtains the ratio of the covariance between the first pipe network operating parameter and the second pipe network operating parameter in the first sample period to the second product value, and obtains the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter in the first sample period. The first electronic device obtains the ratio of the covariance between the first pipe network operating parameter and the second pipe network operating parameter in the second sample period to the third product value, and obtains the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter in the second sample period. Accordingly, the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter in the sample period includes the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter in the first sample period and the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter in the second sample period.

[0124] For example, the first sample period is a period from 30 days before the sample time node to the sample time node, the second sample period is a period from 7 days before the sample time node to the sample time node, the first pipe network operation parameter and the second pipe network operation parameter are the pressure difference and the water delivery volume, respectively. Then, the first electronic device obtains the correlation coefficient of the pressure difference and the water delivery volume within 30 days before the sample time node and the correlation coefficient within 7 days before the sample time node through steps (1) to (4).

[0125] The above description uses the example of the first electronic device obtaining the correlation coefficient between any two pipe network operating parameters within a sample period. In the embodiment of the present application, since the water-gas ratio can, to a certain extent, reflect the correlation between the water and gas delivery volumes, the first electronic device may also obtain only the correlation coefficient between the pressure difference and the water delivery volume, the correlation coefficient between the pressure difference and the gas delivery volume, and the correlation coefficient between the pressure difference and the water-gas ratio within the sample period.

[0126] Step 307: The first electronic device performs model training based on the sample operating condition label, characteristic value, and correlation coefficient corresponding to each sample time node to obtain a fluid accumulation operating condition determination model.

[0127] In one possible implementation, the first electronic device directly performs model training based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node to obtain a fluid accumulation operating condition determination model. The fluid accumulation operating condition determination model can determine the fluid accumulation condition of the target pipeline network, that is, whether there is fluid accumulation in the target pipeline network at the current time node.

[0128] In another possible implementation, the first electronic device performs model training based on the type of fluid accumulation. The process may be: when the sample operating condition label indicates that there is fluid accumulation in the sample pipe network at the sample time node, the first electronic device determines the type of fluid accumulation corresponding to the sample time node; based on the sample operating condition label, fluid accumulation type, characteristic value and correlation coefficient corresponding to each sample time node, the model is trained to obtain a fluid accumulation condition determination model.

[0129] In this implementation, the fluid accumulation condition determination model can not only determine whether fluid accumulation exists in the target pipe network at the current time point, but also determine the type of fluid accumulation if fluid accumulation exists, for example, whether the fluid accumulation type is temporary fluid accumulation or regular fluid accumulation.

[0130] In the embodiments of the present application, a single machine learning model is prone to overfitting. Ensemble learning methods combine multiple machine learning models to achieve better results, giving the combined model stronger generalization capabilities. The XGBoost (Extreme Gradient Boosting) algorithm is a type of boosting method within the ensemble method. It has demonstrated a strong influence in the field of machine learning, achieving high accuracy while being less prone to overfitting. Therefore, the embodiments of the present application can use the XGBoost algorithm for model training.

[0131] The XGBoost algorithm builds k regression trees so that the predicted value of the tree group is as close to the true value as possible and has the greatest generalization ability. The final prediction function is: in, Indicates the prediction result.

[0132] The objective function can be defined as: The objective function consists of two parts: loss function and regularization term, where i represents the i-th sample, Represents the prediction error of the i-th sample, the smaller the error, the better.

[0133] Ω(f k ) is a function that represents the complexity of the tree. The smaller it is, the lower the complexity and the stronger the generalization ability. Among them, T represents the number of nodes in the tree, w j Represents the set of scores of the leaf nodes of the tree, γ and λ are coefficients.

[0134] In an embodiment of the present application, after the first electronic device obtains the fluid effusion operating condition determination model, it can test the fluid effusion operating condition determination model. If the test passes, the fluid effusion operating condition can be determined using the fluid effusion operating condition determination model. Furthermore, during the use of the model, training samples can be added periodically or irregularly to update the model, thereby improving the accuracy of the fluid effusion operating condition determination.

[0135] The embodiment of the present application provides a training method for a model for determining a fluid accumulation condition. The method first obtains the characteristic values ​​of the pipeline network operation parameters within a sample period and the sample condition labels corresponding to the sample time nodes. Based on the characteristic values ​​of the pipeline network operation parameters within the sample period, the characteristic values ​​of the pipeline network operation parameters at the sample time nodes are obtained. Furthermore, the correlation coefficients corresponding to the sample time nodes are obtained. The model is trained based on the sample condition labels, characteristic values, and correlation coefficients corresponding to the sample time nodes to obtain a model for determining a fluid accumulation condition. It can be seen that the method performs model training based on real pipeline network operation data and fluid accumulation conditions. Therefore, the trained model can accurately determine the fluid accumulation condition of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation condition.

[0136] In addition, the model trained by the method provided in this application has high migration and reusability, and does not require the use of professional software and does not require additional software costs.

[0137] Figure 4 This is a flow chart of a method for determining a fluid accumulation condition provided by an embodiment of the present application, which is executed by a second electronic device, wherein the second electronic device and the first electronic device can be the same electronic device or different electronic devices, which is not specifically limited. Figure 4 , the method comprising:

[0138] Step 401: The second electronic device obtains a plurality of pipe network operating parameters of the target pipe network within a target period before the current time node.

[0139] The multiple pipe network operating parameters obtained in this step are the same as the multiple pipe network operating parameters obtained in step 302, and the process of obtaining the multiple pipe network operating parameters is the same as step 302, which will not be repeated here.

[0140] Step 402: For each pipe network operation parameter, the second electronic device obtains a characteristic value of the pipe network operation parameter within a target period; based on the characteristic value of the pipe network operation parameter within the target period, obtains a characteristic value of the pipe network operation parameter at the current time node.

[0141] The target period includes one or more periods. When the target period includes one period, the second electronic device obtains the characteristic value of the pipeline network operation parameter within the period, and obtains the characteristic value of the pipeline network operation parameter at the current time node based on the characteristic value of the pipeline network operation parameter within the period.

[0142] When the target period includes multiple time periods, the target period includes a first target period and a second target period as an example. If the target period includes the first target period and the second target period, the boundary value of the first target period is greater than the boundary value of the second target period. For example, the second target period is the time period from 7 days before the current time node to the current time node, and the first target period is the time period from 30 days before the current time node to the current time node.

[0143] The second electronic device obtains the characteristic value of the pipeline network operation parameter in the first target period and the characteristic value of the pipeline network operation parameter in the second target period, and obtains the characteristic value of the pipeline network operation parameter at the current time node based on the characteristic value of the pipeline network operation parameter in the first target period and the characteristic value of the pipeline network operation parameter in the second target period.

[0144] Among them, the process of the second electronic device obtaining the characteristic value of the pipeline network operation parameter in the first target time period is the same as step 303, the process of the second electronic device obtaining the characteristic value of the pipeline network operation parameter in the second target time period is the same as step 304, and the process of the second electronic device obtaining the characteristic value of the pipeline network operation parameter at the current time node based on the characteristic value of the pipeline network operation parameter in the first target time period and the characteristic value in the second target time period is the same as step 305, which will not be repeated here.

[0145] Step 403: The second electronic device obtains the correlation coefficient corresponding to the current time node.

[0146] If the target period includes a first target period and a second target period, the second electronic device obtains a correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter within the first target period and a correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter within the second target period. The correlation coefficient corresponding to the current time node includes the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter within the first target period and the correlation coefficient between the first pipe network operating parameter and the second pipe network operating parameter within the second target period.

[0147] The process of the second electronic device obtaining the correlation coefficients of the first pipe network operating parameter and the second pipe network operating parameter in the first target period and in the second target period is the same as step 306 and will not be repeated here.

[0148] Step 404: The second electronic device inputs the characteristic value and the correlation coefficient corresponding to the current time node into the fluid accumulation operating condition determination model to obtain a first fluid accumulation operating condition.

[0149] The first liquid accumulation condition is the liquid accumulation condition corresponding to the target pipe network at the current time node. The liquid accumulation condition determination model is obtained by training using the method in steps 301 to 307.

[0150] The second electronic device inputs the characteristic value and correlation coefficient corresponding to the current time node into the fluid accumulation condition determination model to obtain a first fluid accumulation condition. If the first fluid accumulation condition indicates that there is fluid accumulation in the target pipeline network at the current time node, the fluid accumulation condition determination model will also output the fluid accumulation type.

[0151] An embodiment of the present application provides a method for determining a fluid accumulation condition. The method first obtains a characteristic value and correlation coefficient corresponding to a current time node, then inputs the characteristic value corresponding to the current time node into a fluid accumulation condition determination model to obtain a first fluid accumulation condition corresponding to the current time node. Because the fluid accumulation condition determination model is trained based on real pipeline network operating data and fluid accumulation conditions, the fluid accumulation condition determination model can accurately determine the fluid accumulation condition of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation condition.

[0152] Figure 4 The figure shows a process for determining the fluid accumulation working condition provided by the present application. The method for determining the fluid accumulation working condition provided by the present application is further elaborated below. Figure 5 This is a flow chart of a method for determining a fluid accumulation condition provided by an embodiment of the present application, which is executed by the second electronic device. Figure 5 , the method comprising:

[0153] Step 501: The second electronic device obtains a plurality of pipe network operating parameters of a target pipe network within a target period before a current time node.

[0154] This step is similar to step 401 and will not be described again here.

[0155] Step 502: For each pipe network operation parameter, the second electronic device obtains a characteristic value of the pipe network operation parameter within a target period; based on the characteristic value of the pipe network operation parameter within the target period, obtains a characteristic value of the pipe network operation parameter at the current time node.

[0156] This step is similar to step 402 and will not be described again here.

[0157] Step 503: The second electronic device obtains the correlation coefficient corresponding to the current time node.

[0158] This step is similar to step 403 and will not be described again here.

[0159] Step 504: The second electronic device inputs the characteristic value and the correlation coefficient corresponding to the current time node into the fluid effusion operating condition determination model to obtain a second fluid effusion operating condition.

[0160] The second electronic device inputs the characteristic value and the correlation coefficient corresponding to the current time node into the fluid effusion operating condition determination model, and outputs a second fluid effusion operating condition through the fluid effusion operating condition determination model.

[0161] For example, the first electronic device and the second electronic device are the same electronic device, see Figure 6 , the second electronic device can perform model training based on the sample data in the sample library to obtain a fluid accumulation condition determination model. Then, for the pipe network whose fluid accumulation condition is to be determined, the fluid accumulation condition of the pipe network is determined using the fluid accumulation condition determination model.

[0162] Step 505: The second electronic device corrects the second fluid accumulation operating condition based on at least one of the third fluid accumulation operating condition and the fourth fluid accumulation operating condition to obtain the first fluid accumulation operating condition.

[0163] The third fluid accumulation condition is the fluid accumulation condition corresponding to each time point during the first target period, and the fourth fluid accumulation condition is the fluid accumulation condition corresponding to each time point during the second target period. Each time point corresponds to one fluid accumulation condition. For example, the third fluid accumulation condition is the fluid accumulation condition corresponding to each day within the 30 days prior to the current time point, and the fourth fluid accumulation condition is the fluid accumulation condition corresponding to each day within the 7 days prior to the current time point.

[0164] In one possible implementation, the second electronic device determines at least one of the first quantity and the second quantity when the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node; and corrects the second fluid accumulation condition to obtain the first fluid accumulation condition when at least one of the first quantity and the second quantity is greater than the corresponding threshold value.

[0165] In this implementation, the first number is the number of the third fluid accumulation operating condition indicating the presence of fluid accumulation, and the second number is the number of the fourth fluid accumulation operating condition indicating the presence of fluid accumulation.

[0166] When the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node, the second electronic device determines the cumulative number of third fluid accumulation conditions in which fluid accumulation exists within the first target time period, i.e., the first number, and / or determines the cumulative number of fourth fluid accumulation conditions in which fluid accumulation exists within the second target time period, i.e., the second number.

[0167] For example, the second electronic device determines a first quantity and a second quantity, and when the first quantity is greater than a second preset threshold and the second quantity is greater than a third preset threshold, the second fluid accumulation condition is modified to a first fluid accumulation condition, where the first fluid accumulation condition indicates that fluid accumulation exists at the current time node.

[0168] For example, there is no fluid accumulation at the current time node, but the number of third fluid accumulation conditions with fluid accumulation within 30 days before the current time node is 9, and the number of fourth fluid accumulation conditions with fluid accumulation within 7 days before the current time node is 4, both of which are greater than the corresponding preset thresholds. The second electronic device can modify the second fluid accumulation condition corresponding to the current time node to the first fluid accumulation condition.

[0169] In another possible implementation, when the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node, the second electronic device determines a third quantity; when the third quantity is greater than the first preset threshold, the second fluid accumulation condition is corrected to obtain the first fluid accumulation condition.

[0170] In this implementation, the third number is the number of third or fourth fluid accumulation conditions in which fluid accumulation continuously existed before the current time node. If the second fluid accumulation condition indicates that fluid accumulation does not exist at the current time node, the second electronic device may determine the number of third fluid accumulation conditions in which fluid accumulation continuously existed within the first target time period to obtain the third number; alternatively, the second electronic device may determine the number of fourth fluid accumulation conditions in which fluid accumulation continuously existed within the second target time period to obtain the third number. The second electronic device determines whether the third number is greater than a first preset threshold. If the third number is greater than the first preset threshold, the second fluid accumulation condition is modified to the first fluid accumulation condition, where the first fluid accumulation condition indicates that fluid accumulation exists at the current time node.

[0171] For example, if there is no fluid accumulation at the current time node, and the number of third fluid accumulation conditions with continuous fluid accumulation before the current time node is 4, which is greater than the first preset threshold, the second fluid accumulation condition corresponding to the current time node can be modified to the first fluid accumulation condition.

[0172] In an embodiment of the present application, the second electronic device can correct the fluid accumulation condition corresponding to the current time node based on the fluid accumulation condition in the target time period before the current time node, so as to more accurately determine the fluid accumulation condition.

[0173] If the second electronic device is provided as a terminal, or the second electronic device is provided as a terminal and a server, the terminal may display the first fluid accumulation condition on a display interface.

[0174] The second electronic device may display only the first fluid accumulation condition on the display interface, or may display multiple fluid accumulation conditions before the current time point. Furthermore, for a fluid accumulation condition in which fluid accumulation exists, the second electronic device may also display the type of fluid accumulation.

[0175] In an embodiment of the present application, the second electronic device can also modify the effusion type at the current time node based on the effusion types at the time nodes before and after the current time node. The process is as follows: the second electronic device obtains the effusion types corresponding to multiple time nodes; for the current time node, if the effusion type corresponding to the current time node is different from the effusion types corresponding to the previous time node and the next time node, and the effusion types corresponding to the previous time node and the next time node are the same, then the effusion type corresponding to the current time node is modified to the effusion type corresponding to the previous time node.

[0176] In this implementation, the second electronic device can determine the effusion type corresponding to multiple time nodes using the effusion working condition determination model, where the multiple time nodes include the current time node, the time node immediately before the current time node, and the time node immediately after the current time node. If the effusion type corresponding to the current time node is different from the effusion type corresponding to the previous and next time nodes, and the effusion types corresponding to the previous and next time nodes are the same, the effusion type corresponding to the current time node can be modified to the effusion type corresponding to the previous or next time node.

[0177] For example, if the effusion type corresponding to the current time node is temporary effusion, and the effusion types corresponding to the two previous and next time nodes are both regular effusion, the effusion type corresponding to the current time node can be modified to regular effusion.

[0178] An embodiment of the present application provides a method for determining a fluid accumulation condition. The method first obtains a characteristic value and correlation coefficient corresponding to a current time node, then inputs the characteristic value corresponding to the current time node into a fluid accumulation condition determination model to obtain a first fluid accumulation condition corresponding to the current time node. Because the fluid accumulation condition determination model is trained based on real pipeline network operating data and fluid accumulation conditions, the fluid accumulation condition determination model can accurately determine the fluid accumulation condition of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation condition.

[0179] Figure 7 This is a structural diagram of a training device for determining a fluid accumulation condition model provided in an embodiment of the present application, see Figure 7 , the device comprises:

[0180] The first acquisition module 701 is used to acquire multiple sample operating condition labels; wherein the sample operating condition label is used to indicate whether there is fluid accumulation in the sample pipe network at the sample time node;

[0181] The second acquisition module 702 is configured to acquire, for each sample time node, a plurality of pipe network operating parameters of the sample pipe network within a sample period; wherein the sample period is located before the sample time node in terms of time sequence;

[0182] The third acquisition module 703 is configured to acquire, for each pipe network operation parameter, a characteristic value of the pipe network operation parameter within a sample period; and based on the characteristic value of the pipe network operation parameter within the sample period, acquire a characteristic value of the pipe network operation parameter at a sample time node;

[0183] The fourth acquisition module 704 is used to obtain the correlation coefficient corresponding to the sample time node; wherein the correlation coefficient is used to represent the degree of correlation between any two pipe network operation parameters within the sample period;

[0184] The training module 705 is used to perform model training based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node to obtain a fluid accumulation operating condition determination model; wherein the fluid accumulation operating condition determination model is used to determine the fluid accumulation operating condition of the target pipeline network.

[0185] In a possible implementation, the sample period includes a first sample period and a second sample period, and a boundary value of the first sample period is greater than a boundary value of the second sample period;

[0186] The third acquisition module 703 is used to obtain the mean value of the pipeline network operation parameter in the first sample period to obtain the first mean value feature; obtain the changing trend of the pipeline network operation parameter in the first sample period to obtain the first trend feature; obtain the difference between the pipeline network operation parameter corresponding to the sample time node and the mean value to obtain the first deviation feature; based on the first mean feature, the first trend feature and the first deviation feature, obtain the first characteristic value of the pipeline network operation parameter in the first sample period; obtain the second characteristic value of the pipeline network operation parameter in the second sample period.

[0187] In another possible implementation, the fourth acquisition module 704 is used to obtain the covariance between the first pipeline network operating parameter and the second pipeline network operating parameter within the sample period; wherein the first pipeline network operating parameter and the second pipeline network operating parameter are any two parameters among the multiple pipeline network operating parameters; obtain the first standard deviation and the second standard deviation; wherein the first standard deviation is the standard deviation of the first pipeline network operating parameter within the sample period, and the second standard deviation is the standard deviation of the second pipeline network operating parameter within the sample period; obtain the product of the first standard deviation and the second standard deviation to obtain a first product value; obtain the ratio of the covariance to the first product value to obtain the correlation coefficient between the first pipeline network operating parameter and the second pipeline network operating parameter within the sample period.

[0188] In another possible implementation, the training module 705 is used to determine the type of fluid accumulation corresponding to the sample time node when the sample operating condition label indicates that there is fluid accumulation in the sample pipe network at the sample time node; based on the sample operating condition label, fluid accumulation type, characteristic value and correlation coefficient corresponding to each sample time node, model training is performed to obtain a fluid accumulation condition determination model.

[0189] In another possible implementation, the second acquisition module 702 is used to obtain the gas and water delivery volumes of the sample pipeline network during the sample period; determine the ratio of the water delivery volume to the gas delivery volume to obtain the water-gas ratio; determine a first pressure value and a second pressure value of the sample pipeline network, the first pressure value being the pressure value of the sample pipeline network at the starting end, and the second pressure value being the pressure value of the sample pipeline network at the ending end; determine the difference between the first pressure value and the second pressure value to obtain the delivery pressure difference; wherein the multiple pipeline network operating parameters include gas delivery volume, water delivery volume, water-gas ratio and delivery pressure difference.

[0190] An embodiment of the present application provides a training device for a model for determining a fluid accumulation condition. The device first obtains the characteristic values ​​of the pipeline network operating parameters within a sample period and the sample condition labels corresponding to the sample time nodes. Based on the characteristic values ​​of the pipeline network operating parameters within the sample period, the characteristic values ​​of the pipeline network operating parameters at the sample time nodes are obtained. Furthermore, the correlation coefficients corresponding to the sample time nodes are obtained. Model training is performed based on the sample condition labels, characteristic values, and correlation coefficients corresponding to the sample time nodes to obtain a model for determining a fluid accumulation condition. It can be seen that the device performs model training based on real pipeline network operation data and fluid accumulation conditions. Therefore, the trained model can accurately determine the fluid accumulation condition of the target pipeline network, thereby improving the accuracy of determining the fluid accumulation condition.

[0191] Figure 8 This is a schematic diagram of a device for determining a fluid accumulation condition according to an embodiment of the present application. Figure 8 , the device comprises:

[0192] A fifth acquisition module 801 is configured to acquire a plurality of pipeline network operating parameters of a target pipeline network within a target period before a current time node;

[0193] A sixth acquisition module 802 is configured to acquire, for each pipe network operation parameter, a characteristic value of the pipe network operation parameter within the target time period; and based on the characteristic value of the pipe network operation parameter within the target time period, acquire a characteristic value of the pipe network operation parameter at the current time node;

[0194] A seventh acquisition module 803 is configured to acquire a correlation coefficient corresponding to the current time node;

[0195] The first determination module 804 is used to input the characteristic value and correlation coefficient corresponding to the current time node into the fluid accumulation condition determination model to obtain a first fluid accumulation condition; wherein, the first fluid accumulation condition is the fluid accumulation condition corresponding to the target pipeline network at the current time node, and the fluid accumulation condition determination model is trained based on the sample condition label, characteristic value and correlation coefficient corresponding to the sample time node, and the sample condition label is used to indicate whether there is fluid accumulation in the sample pipeline network at the sample time node.

[0196] In one possible implementation, the first determination module 804 is used to input the characteristic value and correlation coefficient corresponding to the current time node into the fluid effusion operating condition determination model to obtain the second fluid effusion operating condition; based on at least one of the third fluid effusion operating condition and the fourth fluid effusion operating condition, the second fluid effusion operating condition is corrected to obtain the first fluid effusion operating condition; wherein, the target period includes a first target period and a second target period, the third fluid effusion operating condition is the fluid effusion operating condition corresponding to each time node within the first target period, and the fourth fluid effusion operating condition is the fluid effusion operating condition corresponding to each time node within the second target period.

[0197] In another possible implementation, the first determination module 804 is used to determine at least one of the first quantity and the second quantity when the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node; wherein the first quantity is the quantity of the third fluid accumulation condition indicating that there is fluid accumulation, and the second quantity is the quantity of the fourth fluid accumulation condition indicating that there is fluid accumulation; when at least one of the first quantity and the second quantity is greater than the corresponding preset threshold, the second fluid accumulation condition is corrected to obtain the first fluid accumulation condition; wherein the first fluid accumulation condition indicates that there is fluid accumulation at the current time node.

[0198] In another possible implementation, the first determination module 804 is used to determine a third quantity when the second fluid accumulation condition indicates that there is no fluid accumulation at the current time node; wherein the third quantity is the number of the third fluid accumulation condition or the fourth fluid accumulation condition in which fluid accumulation exists continuously before the current time node; when the third quantity is greater than a third preset threshold, the second fluid accumulation condition is corrected to obtain the first fluid accumulation condition; wherein, the first fluid accumulation condition indicates that there is fluid accumulation at the current time node.

[0199] In another possible implementation, the apparatus further includes:

[0200] An eighth acquisition module, configured to acquire effusion types corresponding to multiple time nodes;

[0201] A modification module is used to modify the effusion type corresponding to the current time node to the effusion type corresponding to the previous time node if the effusion type corresponding to the current time node is different from the effusion type corresponding to the previous time node and the next time node, and the effusion type corresponding to the previous time node and the next time node are the same.

[0202] An embodiment of the present application provides a device for determining a fluid accumulation condition. The device first obtains a characteristic value and correlation coefficient corresponding to a current time node, then inputs the characteristic value corresponding to the current time node into a fluid accumulation condition determination model to obtain a first fluid accumulation condition corresponding to the current time node. Because the fluid accumulation condition determination model is trained based on real pipeline network operating data and fluid accumulation conditions, the fluid accumulation condition determination model can accurately determine the fluid accumulation condition of a target pipeline network, thereby improving the accuracy of determining fluid accumulation conditions.

[0203] If the electronic device is provided as a terminal, refer to Figure 9 , Figure 9 The following is a block diagram of a terminal 900 according to an exemplary embodiment of the present application. Terminal 900 may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. Terminal 900 may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other similar names.

[0204] Typically, the terminal 900 includes a processor 901 and a memory 902 .

[0205] The processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 901 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 901 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 901 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 901 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0206] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 902 is used to store at least one program code, which is used to be executed by the processor 901 to implement the training method of the effusion working condition determination model or the effusion working condition determination method provided in the method embodiment of the present application.

[0207] In some embodiments, terminal 900 may also optionally include a peripheral device interface 903 and at least one peripheral device. The processor 901, memory 902, and peripheral device interface 903 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 903 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 904, a display screen 905, a camera assembly 906, an audio circuit 907, and a power supply 908.

[0208] The peripheral device interface 903 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 901 and the memory 902. In some embodiments, the processor 901, the memory 902, and the peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 901, the memory 902, and the peripheral device interface 903 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0209] The RF circuit 904 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 904 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 904 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The RF circuit 904 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 904 may also include circuits related to Near Field Communication (NFC), which is not limited in this application.

[0210] The display screen 905 is used to display a user interface (UI). This UI may include graphics, text, icons, videos, or any combination thereof. When the display screen 905 is a touch screen, it is also capable of collecting touch signals on or above the surface of the display screen 905. These touch signals can be input as control signals to the processor 901 for processing. In this case, the display screen 905 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 905, located on the front panel of the terminal 900. In other embodiments, there can be at least two display screens 905, located on different surfaces of the terminal 900 or in a foldable design. In other embodiments, the display screen 905 can be a flexible display screen, located on a curved or foldable surface of the terminal 900. Furthermore, the display screen 905 can be configured as a non-rectangular irregular shape, i.e., a special-shaped screen. The display screen 905 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0211] The camera assembly 906 is used to capture images or videos. Optionally, the camera assembly 906 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 906 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0212] The audio circuit 907 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals to be input into the processor 901 for processing, or input into the radio frequency circuit 904 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there may be multiple microphones, each located in different parts of the terminal 900. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert electrical signals from the processor 901 or the radio frequency circuit 904 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into sound waves audible to humans, but also convert electrical signals into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 907 may also include a headphone jack.

[0213] Power supply 908 is used to power various components in terminal 900. Power supply 908 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 908 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is charged via a wired line, while a wireless rechargeable battery is charged via a wireless coil. The rechargeable battery can also support fast charging technology.

[0214] In some embodiments, the terminal 900 further includes one or more sensors 909 , including but not limited to: an acceleration sensor 910 , a gyroscope sensor 911 , a pressure sensor 912 , an optical sensor 913 , and a proximity sensor 914 .

[0215] The accelerometer 910 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the terminal 900. For example, the accelerometer 910 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 901 can control the display screen 905 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 910. The accelerometer 910 can also be used to collect game or user motion data.

[0216] The gyroscope sensor 911 can detect the orientation and rotation angle of the terminal 900. It can also work with the accelerometer 910 to collect the user's 3D movements of the terminal 900. Based on the data collected by the gyroscope sensor 911, the processor 901 can implement the following functions: motion sensing (such as changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0217] The pressure sensor 912 can be set on the side frame of the terminal 900 and / or the lower layer of the display screen 905. When the pressure sensor 912 is set on the side frame of the terminal 900, it can detect the user's grip signal of the terminal 900, and the processor 901 performs left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 912. When the pressure sensor 912 is set on the lower layer of the display screen 905, the processor 901 controls the operable controls on the UI interface based on the user's pressure operation on the display screen 905. The operable controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0218] The optical sensor 913 is used to detect ambient light intensity. In one embodiment, the processor 901 can control the display brightness of the display screen 905 based on the ambient light intensity detected by the optical sensor 913. Specifically, when the ambient light intensity is high, the display brightness of the display screen 905 is increased; when the ambient light intensity is low, the display brightness of the display screen 905 is decreased. In another embodiment, the processor 901 can also dynamically adjust the shooting parameters of the camera assembly 906 based on the ambient light intensity detected by the optical sensor 913.

[0219] The proximity sensor 914, also known as a distance sensor, is typically located on the front panel of the terminal 900. The proximity sensor 914 is used to detect the distance between the user and the front of the terminal 900. In one embodiment, when the proximity sensor 914 detects that the distance between the user and the front of the terminal 900 is gradually decreasing, the processor 901 controls the display screen 905 to switch from the screen-on state to the screen-off state. When the proximity sensor 914 detects that the distance between the user and the front of the terminal 900 is gradually increasing, the processor 901 controls the display screen 905 to switch from the screen-off state to the screen-on state.

[0220] Those skilled in the art will understand that Figure 9 The structure shown in the figure does not constitute a limitation on the terminal 900, and the terminal 900 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0221] If the electronic device is provided as a server, the structural block diagram of the server can be found in Figure 10The server 1000 may vary significantly due to different configurations or performance, and may include a processor (central processing unit, CPU) 1001 and a memory 1002. The memory 1002 stores at least one program code, which is loaded and executed by the processor 1001 to implement the aforementioned method for training a fluid effusion condition determination model or the method for determining a fluid effusion condition. Of course, the server 1000 may also have components such as a wired or wireless network interface, a keyboard, and input / output interfaces for input and output. The server 1000 may also include other components for implementing device functions, which will not be detailed here.

[0222] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the training method of the effusion operating condition determination model or the effusion operating condition determination method in the above embodiment.

[0223] In an exemplary embodiment, a computer program product is also provided, which stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the training method of the effusion operating condition determination model or the effusion operating condition determination method in the above embodiment.

[0224] The above description is only for the purpose of facilitating those skilled in the art to understand the technical solution of this application and is not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included in the scope of protection of this application.

Claims

1. A training method for a fluid accumulation condition determination model, characterized in that: The method comprises: Acquire multiple sample operating condition labels; wherein the sample operating condition labels are used to indicate whether there is fluid accumulation in the sample pipe network at the sample time node; For each sample time node, a plurality of pipe network operating parameters of the sample pipe network within a sample period are obtained; wherein the sample period is located before the sample time node in time sequence, the sample period includes a first sample period and a second sample period, and a boundary value of the first sample period is greater than a boundary value of the second sample period; For each pipeline network operation parameter, obtaining a mean value of the pipeline network operation parameter within the first sample period to obtain a first mean value feature; Obtaining a change trend of the pipeline network operating parameter within the first sample period to obtain a first trend feature; Obtaining a difference between the pipe network operation parameter corresponding to the sample time node and the mean value to obtain a first deviation feature; Obtaining a first characteristic value of the pipeline network operation parameter within the first sample period based on the first mean characteristic, the first trend characteristic, and the first deviation characteristic; Obtaining a second mean characteristic, a second trend characteristic, and a second deviation characteristic of the pipeline network operation parameter in the second sample period, and determining a second characteristic value of the pipeline network operation parameter in the second sample period based on the second mean characteristic, the second trend characteristic, and the second deviation characteristic; Based on the first characteristic value and the second characteristic value, obtaining the characteristic value of the pipeline network operation parameter at the sample time node; Obtaining a correlation coefficient corresponding to the sample time node; wherein the correlation coefficient is used to represent the degree of correlation between any two pipe network operation parameters within the sample period; Based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node, model training is performed to obtain a fluid accumulation operating condition determination model; wherein, the fluid accumulation operating condition determination model is used to determine the fluid accumulation operating condition of the target pipeline network.

2. The method according to claim 1, characterized in that The obtaining of the correlation coefficient corresponding to the sample time node includes: Obtaining a covariance between a first pipe network operating parameter and a second pipe network operating parameter within the sample period; wherein the first pipe network operating parameter and the second pipe network operating parameter are any two parameters among the plurality of pipe network operating parameters; Obtaining a first standard deviation and a second standard deviation; wherein the first standard deviation is the standard deviation of the first pipe network operating parameter within the sample period, and the second standard deviation is the standard deviation of the second pipe network operating parameter within the sample period; Obtaining a product of the first standard deviation and the second standard deviation to obtain a first product value; The ratio of the covariance to the first product value is obtained to obtain a correlation coefficient between the first pipe network operation parameter and the second pipe network operation parameter within the sample period.

3. The method according to claim 1, characterized in that The model training is performed based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node to obtain the effusion operating condition determination model, including: When the sample operating condition label indicates that there is fluid accumulation in the sample pipe network at the sample time node, determining the type of fluid accumulation corresponding to the sample time node; Based on the sample operating condition label, effusion type, characteristic value and correlation coefficient corresponding to each sample time node, model training is performed to obtain the effusion operating condition determination model.

4. The method according to claim 1, wherein The obtaining of a plurality of pipe network operating parameters of the sample pipe network within a sample period includes: Obtaining the gas and water delivery volumes of the sample pipe network during the sample period; Determining the ratio of the water delivery amount to the gas delivery amount to obtain a water-gas ratio; Determining a first pressure value and a second pressure value of the sample pipe network, wherein the first pressure value is a pressure value of the sample pipe network at a starting end, and the second pressure value is a pressure value of the sample pipe network at a terminating end; determining a difference between the first pressure value and the second pressure value to obtain a pressure difference; Among them, the multiple pipeline network operation parameters include the gas transmission volume, the water transmission volume, the water-gas ratio and the transmission pressure difference.

5. A method for determining a fluid accumulation condition, characterized in that: The method comprises: Acquire multiple pipeline network operating parameters of the target pipeline network within a target period before the current time node, where the target period includes a first target period and a second target period, and a boundary value of the first target period is greater than a boundary value of the second target period; For each pipeline network operation parameter, obtaining a mean value of the pipeline network operation parameter within the first target period to obtain a third mean value feature; Obtaining a change trend of the pipeline network operating parameter within the first target time period to obtain a third trend feature; Obtaining a difference between the pipe network operation parameter corresponding to the current time node and the mean value to obtain a third deviation feature; Based on the third mean feature, the third trend feature, and the third deviation feature, obtaining a third feature value of the pipeline network operation parameter within the first target time period; Obtaining a fourth mean characteristic, a fourth trend characteristic, and a fourth deviation characteristic of the pipeline network operation parameter within the second target period, and determining a fourth characteristic value of the pipeline network operation parameter within the second target period based on the fourth mean characteristic, the fourth trend characteristic, and the fourth deviation characteristic; Based on the third characteristic value and the fourth characteristic value, obtaining the characteristic value of the pipeline network operation parameter at the current time node; Obtaining the correlation coefficient corresponding to the current time node; Inputting the characteristic value and correlation coefficient corresponding to the current time node into the fluid accumulation operating condition determination model to obtain a first fluid accumulation operating condition; Among them, the first fluid accumulation condition is the fluid accumulation condition corresponding to the target pipeline network at the current time node, and the fluid accumulation condition determination model is obtained by training based on the sample condition label, eigenvalue and correlation coefficient corresponding to the sample time node. The sample condition label is used to indicate whether there is fluid accumulation in the sample pipeline network at the sample time node.

6. The method according to claim 5, characterized in that The step of inputting the characteristic value and the correlation coefficient corresponding to the current time node into the fluid accumulation operating condition determination model to obtain the first fluid accumulation operating condition includes: Inputting the characteristic value and correlation coefficient corresponding to the current time node into the fluid accumulation operating condition determination model to obtain a second fluid accumulation operating condition; Based on at least one of the third fluid accumulation operating condition and the fourth fluid accumulation operating condition, the second fluid accumulation operating condition is corrected to obtain the first fluid accumulation operating condition; wherein, the target period includes a first target period and a second target period, the third fluid accumulation operating condition is the fluid accumulation operating condition corresponding to each time node within the first target period, and the fourth fluid accumulation operating condition is the fluid accumulation operating condition corresponding to each time node within the second target period.

7. The method according to claim 6, characterized in that The correcting the second fluid accumulation operating condition based on at least one of the third fluid accumulation operating condition and the fourth fluid accumulation operating condition to obtain the first fluid accumulation operating condition includes: When the second fluid accumulation operating condition indicates that there is no fluid accumulation at the current time node, determining at least one of a first number and a second number; wherein the first number is the number of the third fluid accumulation operating condition indicating that there is fluid accumulation, and the second number is the number of the fourth fluid accumulation operating condition indicating that there is fluid accumulation; When at least one of the first quantity and the second quantity is greater than the corresponding preset threshold, the second fluid accumulation operating condition is corrected to obtain the first fluid accumulation operating condition; wherein, the first fluid accumulation operating condition indicates that fluid accumulation exists at the current time node.

8. The method according to claim 6, characterized in that The correcting the second fluid accumulation operating condition based on at least one of the third fluid accumulation operating condition and the fourth fluid accumulation operating condition to obtain the first fluid accumulation operating condition includes: When the second fluid accumulation operating condition indicates that there is no fluid accumulation at the current time node, determining a third number; wherein the third number is the number of the third fluid accumulation operating condition or the fourth fluid accumulation operating condition in which fluid accumulation exists continuously before the current time node; When the third number is greater than the first preset threshold, the second fluid accumulation operating condition is corrected to obtain the first fluid accumulation operating condition; wherein, the first fluid accumulation operating condition indicates that fluid accumulation exists at the current time node.

9. The method according to claim 5, characterized in that The method further comprises: Get the effusion types corresponding to multiple time nodes; For the current time node, if the effusion type corresponding to the current time node is different from the effusion type corresponding to the previous time node and the next time node, and the effusion type corresponding to the previous time node and the next time node is the same, then the effusion type corresponding to the current time node is modified to the effusion type corresponding to the previous time node.

10. A training device for determining a model for a fluid accumulation condition, characterized in that: The device comprises: A first acquisition module is configured to acquire a plurality of sample operating condition labels, wherein the sample operating condition labels are used to indicate whether there is fluid accumulation in the sample pipe network at a sample time node; A second acquisition module is configured to acquire, for each sample time node, a plurality of pipe network operating parameters of the sample pipe network within a sample period; wherein the sample period is located before the sample time node in time sequence, the sample period includes a first sample period and a second sample period, and a boundary value of the first sample period is greater than a boundary value of the second sample period; The third acquisition module is configured to, for each pipeline network operation parameter, obtain the mean value of the pipeline network operation parameter within the first sample period to obtain a first mean value feature; obtain the changing trend of the pipeline network operation parameter within the first sample period to obtain a first trend feature; obtain the difference between the pipeline network operation parameter corresponding to the sample time node and the mean value to obtain a first deviation feature; obtain a first characteristic value of the pipeline network operation parameter within the first sample period based on the first mean feature, the first trend feature, and the first deviation feature; obtain a second mean feature, a second trend feature, and a second deviation feature of the pipeline network operation parameter within the second sample period, and determine a second characteristic value of the pipeline network operation parameter within the second sample period based on the second mean feature, the second trend feature, and the second deviation feature; and obtain a characteristic value of the pipeline network operation parameter at the sample time node based on the first characteristic value and the second characteristic value; A fourth acquisition module is used to obtain a correlation coefficient corresponding to the sample time node; wherein the correlation coefficient is used to represent the degree of correlation between any two pipe network operation parameters within the sample time period; The training module is used to perform model training based on the sample operating condition label, characteristic value and correlation coefficient corresponding to each sample time node to obtain a fluid accumulation condition determination model; wherein, the fluid accumulation condition determination model is used to determine the fluid accumulation condition of the target pipeline network.

11. A device for determining a fluid accumulation condition, characterized in that: The device comprises: a fifth acquisition module, configured to acquire a plurality of pipeline network operating parameters of the target pipeline network within a target period before the current time node, wherein the target period includes a first target period and a second target period, and a boundary value of the first target period is greater than a boundary value of the second target period; a sixth acquisition module, configured to, for each pipeline network operation parameter, acquire the mean value of the pipeline network operation parameter within the first target period to obtain a third mean value feature; acquire the changing trend of the pipeline network operation parameter within the first target period to obtain a third trend feature; acquire the difference between the pipeline network operation parameter corresponding to the current time node and the mean value to obtain a third deviation feature; acquire a third characteristic value of the pipeline network operation parameter within the first target period based on the third mean feature, the third trend feature, and the third deviation feature; acquire a fourth mean feature, a fourth trend feature, and a fourth deviation feature of the pipeline network operation parameter within the second target period, and determine a fourth characteristic value of the pipeline network operation parameter within the second target period based on the fourth mean feature, the fourth trend feature, and the fourth deviation feature; and acquire a characteristic value of the pipeline network operation parameter at the current time node based on the third characteristic value and the fourth characteristic value; A seventh acquisition module, configured to acquire a correlation coefficient corresponding to the current time node; A first determination module is configured to input the characteristic value and correlation coefficient corresponding to the current time node into a fluid accumulation operating condition determination model to obtain a first fluid accumulation operating condition; Among them, the first fluid accumulation condition is the fluid accumulation condition corresponding to the target pipeline network at the current time node, and the fluid accumulation condition determination model is obtained by training based on the sample condition label, eigenvalue and correlation coefficient corresponding to the sample time node. The sample condition label is used to indicate whether there is fluid accumulation in the sample pipeline network at the sample time node.

12. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the method according to any one of claims 1 to 4 or 5 to 9.

13. A computer-readable storage medium, characterized in that At least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by the processor to implement the method according to any one of claims 1 to 4 or 5 to 9.

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