Conveying pipe control method, device and storage medium

Abstract

The application discloses a conveying pipeline control method and device and a storage medium, relates to the technical field of pipelines, and aims to provide an intelligent conveying pipeline control method, improve the control effect of an oil and gas conveying pipeline, and ensure the safe and stable operation of the oil and gas conveying pipeline. The method comprises the following steps: acquiring conveying demand data of a target pipeline reference area; determining a conveying demand type corresponding to the conveying demand data; the target pipeline reference area comprises at least one conveying pipeline; based on the conveying demand data and the conveying demand type, predicting an optimal operation data group of the target pipeline reference area; the operation data group comprises equipment operation data and pipeline operation data; based on the optimal operation data group, generating a control strategy of the target pipeline reference area; and the control strategy is used for controlling the operation of the pipeline in the target pipeline reference area so as to meet the conveying demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline, in particular to a conveying pipeline control method, device and storage medium. BACKGROUND

[0002] Due to the long pipeline line of oil and gas, the multiple stations and warehouses and the close connection of the whole line, only relying on the on-site inspection of the staff, the workload is large and the whole pipeline state and equipment state cannot be accurately judged, which cannot guarantee the safe and stable operation of the oil and gas pipeline.

[0003] In summary, the existing oil and gas pipeline control effect is poor. SUMMARY

[0004] The present application aims to provide a conveying pipeline control method, device and storage medium to improve the control effect of oil and gas pipeline and guarantee the safe and stable operation of oil and gas pipeline.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a conveying pipeline control method, comprising: obtaining conveying demand data of a target pipeline reference area; and determining a conveying demand type corresponding to the conveying demand data; the target pipeline reference area comprises at least one conveying pipeline; based on the conveying demand data and the conveying demand type, predicting an optimal operation data set of the target pipeline reference area; the operation data set comprises equipment operation data and pipeline operation data; based on the optimal operation data set, generating a control strategy of the target pipeline reference area; the control strategy is used to control the operation of the pipeline in the target pipeline reference area to meet the conveying demand.

[0007] The conveying pipeline control method provided by the present application can accurately predict the pipeline in the target pipeline area through the conveying demand data and the conveying demand type, obtain the optimal operation data set that can complete the conveying demand, realize the automation of pipeline inspection and control, reduce the workload of the staff, avoid the problem of poor accuracy of manual judgment, improve the control effect of oil and gas pipeline and guarantee the safe and stable operation of oil and gas pipeline.

[0008] In a possible implementation manner, based on the conveying demand data and the conveying demand type, the optimal operation data set of the target pipeline reference area is predicted, comprising: inputting the conveying demand data and the conveying demand type into an optimal operation data prediction model to obtain the optimal operation data set of the target pipeline reference area; the optimal operation data prediction model is used to predict the optimal operation data set based on the conveying demand data of the pipeline reference area and the conveying demand type corresponding to the conveying demand data.

[0009] In a second aspect, the application discloses a model training method, which comprises the following steps: obtaining a plurality of historical running data sets of a target pipeline reference area under different transportation demand types; wherein each historical running data set comprises historical pipeline running data and historical equipment running data; analyzing the plurality of historical running data sets of the target pipeline reference area under different transportation demand types to obtain a plurality of historical running evaluation values of the target pipeline reference area under different transportation demand types; determining an optimal historical running evaluation value of each transportation demand type, wherein the optimal historical running evaluation value is the maximum value in the plurality of historical running evaluation values; and training an optimal running data prediction model to be trained based on transportation demand data of the target pipeline reference area, a transportation demand type corresponding to the transportation demand data, and an optimal historical running data set corresponding to the optimal historical running evaluation value of the transportation demand type, to obtain a trained optimal running data prediction model.

[0010] In a possible implementation, the training of the optimal running data prediction model to be trained based on the transportation demand data of the target pipeline reference area, the transportation demand type corresponding to the transportation demand data, and the optimal historical running data set corresponding to the optimal historical running evaluation value of the transportation demand type comprises the following steps: taking the transportation demand data of the target pipeline reference area and the transportation demand type corresponding to the transportation demand data as sample data, and taking the optimal historical running data set corresponding to the optimal historical running evaluation value of the transportation demand type as a sample label to form a training sample; and training the optimal running data prediction model to be trained based on the training sample.

[0011] In another possible implementation, before the step of obtaining the plurality of historical running data sets of the target pipeline reference area under different transportation demand types, the method further comprises the following steps: obtaining structural features and transportation features of a pipeline included in a pipeline reference structure diagram; the structural features of the pipeline comprise at least one of the following: a number of sub-pipelines, a number of branch nodes; the transportation features of the pipeline comprise at least one of the following: a transportation efficiency, a transportation failure frequency, and a transportation failure influence coefficient mean value; and based on the structural features and the transportation features of the pipeline, at least one target pipeline reference area is determined, and a range of the at least one target pipeline reference area is marked on the pipeline reference structure diagram.

[0012] In still another possible implementation, the determination of the at least one target pipeline reference area based on the structural features and the transportation features of the pipeline comprises the following steps: based on the structural features of the pipeline, at least one initial pipeline reference area is obtained; for each initial pipeline reference area in the at least one initial pipeline reference area, each initial pipeline reference area is corrected based on transportation features of the pipeline in each initial pipeline reference area to obtain the at least one target pipeline reference area.

[0013] In another possible implementation, the multiple sets of historical operation data of the target pipeline reference region under different transportation demand types include multiple sets of historical operation data of the target pipeline reference region under multiple historical transportation periods for each transportation demand type. The analysis of the multiple sets of historical operation data of the target pipeline reference region under different transportation demand types obtains multiple historical operation evaluation values of the target pipeline reference region under different transportation demand types, including: for each transportation demand type, comparing each historical pipeline operation data of the same historical transportation period with the preset pipeline operation data to obtain a first operation evaluation value; generating a first compensation coefficient according to a fluctuation range of all historical pipeline operation data of the same historical transportation period; comparing each historical equipment operation data of the same historical transportation period with the preset equipment operation data to obtain a second operation evaluation value; generating a second compensation coefficient according to a fluctuation range of all historical equipment operation data of the same historical transportation period; determining a third operation evaluation value according to the first operation evaluation value, the first compensation coefficient, the second operation evaluation value, and the second compensation coefficient; performing mean value processing on multiple third operation evaluation values of the same historical transportation period to obtain a fourth operation evaluation value; and obtaining the historical operation evaluation value based on the fourth operation evaluation values of multiple historical transportation periods.

[0014] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory stores instructions executable by the processor; and the processor is configured to execute the instructions, so that the electronic device implements the method of the first aspect or the second aspect.

[0015] In a fourth aspect, the present application provides a chip applied to an electronic device; the chip comprises one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected through a circuit; the interface circuit is used to receive signals from the memory of the electronic device and send signals to the processor of the electronic device, and the signals include software instructions stored in the memory. When the processor executes the software instructions, the electronic device executes the method of the first aspect or the second aspect.

[0016] In a fifth aspect, the present application provides a readable storage medium, comprising: software instructions; when the software instructions run in an electronic device, the electronic device implements the method of the first aspect or the second aspect.

[0017] In a sixth aspect, the present application provides a computer program product, when the computer program product runs on an electronic device, the electronic device executes the steps of the related method described in the first aspect or the second aspect to implement the method of the first aspect or the second aspect.

[0018] The beneficial effects of the second aspect to the sixth aspect are described in the corresponding description of the first aspect, and will not be repeated. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0020] Figure 1 A flowchart of a conveying pipeline control method provided by the present application is shown in the figure.

[0021] Figure 2 A flowchart of a model training method provided by the present application is shown in the figure.

[0022] Figure 3 A flowchart of another conveying pipeline control method provided by the present application is shown in the figure.

[0023] Figure 4 A composition diagram of a conveying pipeline control device provided by the present application is shown in the figure.

[0024] Figure 5 A composition diagram of a model training device provided by the present application is shown in the figure.

[0025] Figure 6 A composition diagram of an electronic device provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative effort belong to the scope of protection of the present application.

[0027] In the embodiments of the present application, the term “comprising” or “including” or any other variant thereof is intended to cover the non-exclusive inclusion, so that the process, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, article or device. Without more limitation, the element defined by the sentence “including a…” does not exclude the presence of another identical element in the process, article or device including the element.

[0028] In the embodiments of the present application, the words "exemplarily" or "for example" are used to represent an example, illustration, or description. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplarily" or "for example" are used to present the relevant concept in a specific manner.

[0029] In the description of the specification, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0030] Because the oil and gas pipeline is long, the station warehouse is multiple, and the whole line is closely connected, only relying on the on-site inspection of the staff has a large workload and cannot accurately judge the state of all pipelines and equipment, and cannot guarantee the safe and stable operation of the oil and gas pipeline.

[0031] Based on this, the conveying pipeline control method provided by the present application is based on conveying demand data and conveying demand type analysis, uses an algorithm to accurately predict the target pipeline area, generates an optimal operation data set that meets the conveying requirements, and controls the pipeline in the target pipeline area to complete the conveying work according to the optimal operation data set. This method realizes the intelligentization of pipeline inspection and regulation, effectively reduces the workload of the staff, improves the judgment accuracy, thereby optimizing the oil and gas conveying control effect, and guaranteeing the safe and stable operation of the pipeline system.

[0032] Exemplarily, the conveying pipeline control method of the present application can be executed by an electronic device.

[0033] Exemplarily, the electronic device can be a server, for example, a single server, or a server cluster composed of multiple servers. In some embodiments, the server cluster can also be a distributed cluster.

[0034] Exemplarily, the electronic device can be a terminal device, for example, a mobile phone, a tablet computer, a desktop computer, a laptop computer, a handheld computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, and a cellular phone, a personal digital assistant (PDA), an augmented reality (AR) \ virtual reality (VR) device, etc. The embodiments of the present application do not specially limit the specific form of the terminal device.

[0035] The embodiments of the present application provide a conveying pipeline control method, as shown in Figure 1 The specific steps include the following steps:

[0036] S11, obtain transportation demand data of a target pipeline reference region, and determine a transportation demand type corresponding to the transportation demand data.

[0037] The target pipeline reference region includes at least one transportation pipeline.

[0038] S12, based on the transportation demand data and the transportation demand type, predict an optimal operation data set of the target pipeline reference region.

[0039] The operation data set includes equipment operation data and pipeline operation data.

[0040] In some embodiments, step S12 includes:

[0041] inputting the transportation demand data and the transportation demand type into an optimal operation data prediction model to obtain the optimal operation data set of the target pipeline reference region.

[0042] The optimal operation data prediction model is used to predict the optimal operation data set based on the transportation demand data of the pipeline reference region and the transportation demand type corresponding to the transportation demand data.

[0043] S13, based on the optimal operation data set, generate a control strategy for the target pipeline reference region.

[0044] In some embodiments, the control strategy is used to control the operation of the pipeline in the target pipeline reference region to meet the transportation demand.

[0045] The embodiments of the present application provide a model training method, as shown in Figure 2 The method includes the following steps:

[0046] S21, obtain multiple historical operation data sets of a target pipeline reference region under different transportation demand types.

[0047] Each historical operation data set includes historical pipeline operation data and historical equipment operation data.

[0048] Exemplarily, step S21 includes:

[0049] obtain multiple historical transportation periods of the target pipeline reference region under each transportation demand type; and obtain historical pipeline operation data and historical equipment operation data collected by different data collection nodes in each historical transportation period.

[0050] S22, analyze the multiple historical operation data sets of the target pipeline reference region under different transportation demand types to obtain multiple historical operation evaluation values of the target pipeline reference region under different transportation demand types.

[0051] S23, for each of the different types of transportation demand, determine an optimal historical operation evaluation value of each type of transportation demand.

[0052] wherein the optimal historical operation evaluation value is the maximum value among the plurality of historical operation evaluation values.

[0053] S24, based on the transportation demand data of the target pipeline reference area, the transportation demand type corresponding to the transportation demand data, and the optimal historical operation data set corresponding to the optimal historical operation evaluation value of the transportation demand type, training the optimal operation data prediction model to be trained to obtain a trained optimal operation data prediction model.

[0054] Illustratively, the historical operation evaluation values of the same type of transportation demand are sorted, and the historical equipment operation data and historical pipeline operation data corresponding to the first sorted historical operation evaluation value are set as the optimal historical operation data set of the corresponding transportation demand type, and a transportation demand type-optimal historical operation data set mapping table is generated.

[0055] Illustratively, the optimal operation data prediction model to be trained is trained based on the data in the transportation demand type-optimal historical operation data set mapping table.

[0056] In some embodiments, step S24 comprises:

[0057] A1, taking the transportation demand data of the target pipeline reference area and the transportation demand type corresponding to the transportation demand data as sample data, and taking the optimal historical operation data set corresponding to the optimal historical operation evaluation value of the transportation demand type as sample label, to form a training sample.

[0058] A2, training the optimal operation data prediction model to be trained based on the training sample.

[0059] In some embodiments, before step S21, the method further comprises:

[0060] B1, obtaining the structural features and transportation features of the pipelines included in the pipeline reference structure diagram.

[0061] In some embodiments, the structural features of the pipelines include at least one of the following: the number of sub-pipelines, the number of branch nodes; and the transportation features of the pipelines include at least one of the following: transportation efficiency, transportation failure frequency, and average transportation failure impact coefficient.

[0062] In some embodiments, a reference structure diagram of the oil and gas pipeline is constructed, and the reference structure diagram of the oil and gas pipeline includes a plurality of data acquisition nodes; the structural features of the pipelines are obtained through the reference structure diagram of the oil and gas pipeline; and the transportation features of the pipelines are obtained through the data acquisition nodes.

[0063] Exemplarily, based on the length of the pipeline in the pipeline structure reference diagram, the structural relationship of the pipeline trunk and the pipeline branch, the oil and gas pipeline is divided into a plurality of concerned sub-pipelines, and the structural features of different regions are constructed according to the number of the concerned sub-pipelines and the number of branch nodes.

[0064] Exemplarily, the transportation efficiency of the corresponding concerned sub-pipeline in the preset period is generated according to the pipeline length, the pipe diameter, the oil and gas transportation amount in the preset period and the average transportation pressure of each concerned sub-pipeline.

[0065] Exemplarily, the transportation failure log of each concerned sub-pipeline in the preset period is obtained, and the transportation failure frequency and the average transportation failure influence coefficient of the corresponding concerned sub-pipeline are determined according to the transportation failure log.

[0066] Exemplarily, the transportation features of each region in the pipeline structure reference diagram are constructed according to the transportation efficiency, the transportation failure frequency and the average transportation failure influence coefficient of each concerned sub-pipeline.

[0067] B2, based on the structural features and the transportation features of the pipeline, at least one target pipeline reference region is determined, and the range of the at least one target pipeline reference region is marked on the pipeline reference structure diagram.

[0068] In some embodiments, step B2 comprises:

[0069] B21, based on the structural features of the pipeline, at least one initial pipeline reference region is obtained.

[0070] Exemplarily, if the number of the concerned sub-pipeline in the structural features of the region is greater than a preset first number threshold or the number of branch nodes is greater than a preset second number threshold, the region is set as an initial pipeline reference region.

[0071] B22, for each initial pipeline reference region in the at least one initial pipeline reference region, each initial pipeline reference region is corrected based on the transmission features of the pipeline in each initial pipeline reference region, and at least one target pipeline reference region is obtained.

[0072] Exemplarily, the transportation features of the same initial pipeline reference region are constructed according to the transportation efficiency, the transportation failure frequency and the average transportation failure influence coefficient of each concerned sub-pipeline in the initial pipeline reference region; the region correction coefficient is set according to the transportation features of the initial pipeline reference region; and the initial pipeline reference region is corrected according to the region correction coefficient, and the target pipeline reference region is obtained.

[0073] Exemplarily, the transportation features satisfy the following formula (I):

[0074] Formula (I)

[0075] wherein, T is the delivery feature, a1 is the first weight coefficient, a2 is the second weight coefficient, n is the number of the concerned sub-pipeline in the same initial pipeline reference area, gi is the number of delivery failure of the i-th concerned sub-pipeline, yi is the average delivery failure influence coefficient of the i-th concerned sub-pipeline, and pi is the delivery efficiency of the i-th concerned sub-pipeline.

[0076] Exemplarily, four delivery feature intervals are pre-set, i.e., a first pre-set delivery feature interval, a second pre-set delivery feature interval, a third pre-set delivery feature interval and a fourth pre-set delivery feature interval; and four pre-set area correction coefficients are pre-set, i.e., a first pre-set area correction coefficient, a second pre-set area correction coefficient, a third pre-set area correction coefficient and a fourth pre-set area correction coefficient.

[0077] For example, the delivery feature is calculated according to the formula (I), when the delivery feature is in the first pre-set delivery feature interval, the area correction coefficient corresponding to the initial pipeline reference area is set as the fourth pre-set area correction coefficient; when the delivery feature is in the second pre-set delivery feature interval, the area correction coefficient corresponding to the initial pipeline reference area is set as the third pre-set area correction coefficient; when the delivery feature is in the third pre-set delivery feature interval, the area correction coefficient corresponding to the initial pipeline reference area is set as the second pre-set area correction coefficient; and when the delivery feature is in the fourth pre-set delivery feature interval, the area correction coefficient corresponding to the initial pipeline reference area is set as the first pre-set area correction coefficient.

[0078] In some embodiments, before step S22, the method further comprises:

[0079] C1, extracting delivery demand data corresponding to a plurality of historical running data sets.

[0080] C2, analyzing the delivery demand data of a plurality of different historical delivery time periods to obtain a plurality of delivery demand types.

[0081] C3, performing delivery demand related analysis on the historical running data sets of different delivery demand types of each data acquisition node to obtain historical running data sets of each data acquisition node in each delivery demand type. The historical running data set includes historical pipeline running data and historical equipment running data.

[0082] Exemplarily, step C3 comprises:

[0083] C31, obtaining the delivery demand data, the historical pipeline running data and the historical equipment running data of each data acquisition node in different delivery demand types within a historical delivery time period, and constructing a delivery demand data change curve, a plurality of historical pipeline running data change curves and a plurality of historical equipment running data change curves within the corresponding historical delivery time period;

[0084] obtain a sequence of change time nodes M in the change curve of the delivery demand data of different delivery demand types, M (M1, M2, … Mf), wherein Ms is the s-th change time node, and set a change weight for each change time node.

[0085] C32, generate the degree of association between the corresponding historical pipeline operation data and historical equipment operation data and the delivery demand type according to the degree of change of each historical pipeline operation data change curve and each historical equipment operation data change curve at the multiple change time nodes.

[0086] Exemplarily, the degree of association satisfies formula (two):

[0087] Formula (two)

[0088] Wherein, W is the degree of association, f is the number of change time nodes, bs is the degree of change of the s-th change time node, and cs is the change weight of the s-th change time node.

[0089] C33, set the historical pipeline operation data and historical equipment operation data with the degree of association greater than the preset degree of association threshold as the historical pipeline operation data and historical equipment operation data of the corresponding delivery demand type.

[0090] In some embodiments, in the case that the multiple historical operation data groups of the target pipeline reference area under different delivery demand types include: multiple historical operation data groups of the target pipeline reference area in multiple historical delivery periods under each delivery demand type, step S22 includes:

[0091] D1, for each delivery demand type, compare each historical pipeline operation data of the same historical delivery period with the preset pipeline operation data to obtain a first operation evaluation value; and generate a first compensation coefficient according to the fluctuation interval of all historical pipeline operation data in the same historical delivery period.

[0092] D2, compare each historical equipment operation data of the same historical delivery period with the preset equipment operation data to obtain a second operation evaluation value; and generate a second compensation coefficient according to the fluctuation interval of all historical equipment operation data in the same historical delivery period.

[0093] D3, determine a third operation evaluation value according to the first operation evaluation value, the first compensation coefficient, the second operation evaluation value and the second compensation coefficient.

[0094] D4, perform mean value processing on multiple third operation evaluation values of the same historical delivery period to obtain a fourth operation evaluation value.

[0095] D5, a fourth operation evaluation value based on a plurality of historical transportation periods, to obtain a historical operation evaluation value.

[0096] The pipeline control method provided by the application will be introduced below with reference to a specific embodiment.

[0097] As shown in Figure 3 , the method specifically comprises the following steps:

[0098] S31, constructing a pipeline structure reference map.

[0099] S32, obtaining pipeline transportation characteristics and structural characteristics, dividing the pipeline into a plurality of target pipeline reference areas according to the transportation characteristics and the structural characteristics, and mapping the pipeline reference areas on the pipeline structure reference map.

[0100] S33, obtaining a plurality of historical pipeline operation data and historical equipment operation data of data acquisition nodes in the pipeline reference area, analyzing the plurality of historical pipeline operation data and historical equipment operation data, and obtaining a plurality of historical operation evaluation values of the pipeline reference area under different transportation demand types.

[0101] S34, sorting the historical operation evaluation values of the same transportation demand type, determining an optimal historical operation data set corresponding to the transportation demand type, and training an optimal operation data prediction model according to the plurality of optimal historical operation data sets.

[0102] S35, generating a predicted optimal operation data set corresponding to the pipeline reference area based on the optimal operation data prediction model, and generating a control strategy corresponding to the pipeline reference area based on the predicted optimal operation data set.

[0103] The application improves the pipeline control effect by constructing a pipeline structure reference map and dividing a plurality of target pipeline reference areas, calculates the operation evaluation value of each target pipeline reference area under different transportation demand types, selects the optimal operation data set under each transportation demand type according to the operation evaluation value, constructs an optimal operation data generation model corresponding to the target pipeline reference area, and obtains the predicted optimal operation data set of each target pipeline reference area, which can accurately judge the pipeline state and the equipment state, and ensure the safe and stable operation of the pipeline.

[0104] Figure 4The application provides a conveying pipeline control device, which comprises an acquisition module 401, a prediction module 402 and a control module 403. The acquisition module 401 is configured to acquire conveying demand data of a target pipeline reference area, and determine a conveying demand type corresponding to the conveying demand data. The target pipeline reference area comprises at least one conveying pipeline. The prediction module 402 is configured to predict an optimal operation data set of the target pipeline reference area based on the conveying demand data and the conveying demand type. The operation data set comprises equipment operation data and pipeline operation data. The control module 403 is configured to generate a control strategy of the target pipeline reference area based on the optimal operation data set, and control the operation of the pipeline in the target pipeline reference area to meet the conveying demand.

[0105] In a possible implementation, the prediction module 402 is specifically configured to input the conveying demand data and the conveying demand type into an optimal operation data prediction model to obtain the optimal operation data set of the target pipeline reference area. The optimal operation data prediction model is configured to predict the optimal operation data set based on the conveying demand data of the pipeline reference area and the conveying demand type corresponding to the conveying demand data.

[0106] Figure 5 The application further provides a model training device, which comprises an acquisition module 501, an analysis module 502 and a training module 503. The acquisition module 501 is configured to acquire a plurality of historical operation data sets of a target pipeline reference area under different conveying demand types. Each historical operation data set comprises historical pipeline operation data and historical equipment operation data. The analysis module 502 is configured to analyze the plurality of historical operation data sets of the target pipeline reference area under different conveying demand types to obtain a plurality of historical operation evaluation values of the target pipeline reference area under different conveying demand types. For each conveying demand type in the different conveying demand types, an optimal historical operation evaluation value of each conveying demand type is determined, wherein the optimal historical operation evaluation value is the maximum value in the plurality of historical operation evaluation values. The training module 503 is configured to train an optimal operation data prediction model to be trained based on the conveying demand data of the target pipeline reference area, the conveying demand type corresponding to the conveying demand data and an optimal historical operation data set corresponding to the optimal historical operation evaluation value of the conveying demand type, to obtain a trained optimal operation data prediction model.

[0107] In a possible implementation, the training module 503 is specifically configured to take the conveying demand data of the target pipeline reference area and the conveying demand type corresponding to the conveying demand data as sample data, and take an optimal historical operation data set corresponding to the optimal historical operation evaluation value of the conveying demand type as a sample label, to form a training sample. The training module 503 is further configured to train the optimal operation data prediction model to be trained based on the training sample.

[0108] In a further possible implementation, the acquisition module 501 is further configured to acquire structural features and transportation features of the pipelines included in the pipeline reference structure diagram, the structural features of the pipelines including at least one of the number of sub-pipelines, the number of branch nodes, and the transportation features of the pipelines including at least one of the transportation efficiency, the number of transportation failures, and the average value of the transportation failure influence coefficient, determine at least one target pipeline reference area based on the structural features and the transportation features of the pipelines, and mark the range of the at least one target pipeline reference area on the pipeline reference structure diagram.

[0109] In a further possible implementation, the acquisition module 501 is specifically configured to obtain at least one initial pipeline reference area based on the structural features of the pipelines, and correct each initial pipeline reference area in the at least one initial pipeline reference area based on the transportation features of the pipelines in each initial pipeline reference area to obtain the at least one target pipeline reference area.

[0110] In a further possible implementation, the multiple sets of historical operation data of the target pipeline reference area under different transportation demand types include multiple sets of historical operation data of the target pipeline reference area in multiple historical transportation time periods under each transportation demand type. The analysis module 502 is specifically configured to compare each historical pipeline operation data in the same historical transportation time period with the preset pipeline operation data to obtain a first operation evaluation value for each transportation demand type, generate a first compensation coefficient according to the fluctuation range of all historical pipeline operation data in the same historical transportation time period, compare each historical equipment operation data in the same historical transportation time period with the preset equipment operation data to obtain a second operation evaluation value, generate a second compensation coefficient according to the fluctuation range of all historical equipment operation data in the same historical transportation time period, determine a third operation evaluation value according to the first operation evaluation value, the first compensation coefficient, the second operation evaluation value, and the second compensation coefficient, perform mean value processing on multiple third operation evaluation values in the same historical transportation time period to obtain a fourth operation evaluation value, and obtain a historical operation evaluation value based on the fourth operation evaluation values of multiple historical transportation time periods.

[0111] In the case of implementing the functions of the above integrated modules in the form of hardware, the present embodiment of the disclosure provides a possible structure of the electronic device involved in the above embodiments. As shown in the figure, the electronic device 600 includes a processor 602 and a bus 604. Optionally, the electronic device 600 can further include a memory 601; and optionally, the electronic device 600 can further include a communication interface 603. Figure 6

[0112] ​The processor 602 can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic device, transistor logic, hardware components, or any combination thereof. The processor 602 can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the embodiments of the present disclosure. The processor 602 can also be a combination of computing functionality, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on.

[0113] The communication interface 603 is configured to connect with other devices through a communication network. The communication network can be an Ethernet, a radio access network, a wireless local area network (WLAN), and the like.

[0114] The memory 601 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium, or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0115] As a possible implementation, the memory 601 can exist independently of the processor 602, and the memory 601 can be connected to the processor 602 through the bus 604, for storing instructions or program codes. When the processor 602 invokes and executes the instructions or program codes stored in the memory 601, the functions involved in the delivery pipe control method provided by the embodiments of the present disclosure can be implemented. In another possible implementation, the memory 601 can also be integrated with the processor 602.

[0116] The bus 604 can be an extended industry standard architecture (EISA) bus or the like. The bus 604 can be divided into an address bus, a data bus, a control bus, and the like. For the convenience of representation, Figure 6 Only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0117] In the exemplary embodiments, the embodiments of the present application also provide a readable storage medium having program instructions stored thereon; when the program instructions are executed by an electronic device, the electronic device implements the method described in the foregoing embodiments. The readable storage medium can be a non-transitory readable storage medium, for example, the non-transitory readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0118] In the exemplary embodiments, the embodiments of the present application also provide a computer program product, when the computer program product runs on an electronic device, the electronic device executes the above-mentioned related method steps to realize the functions involved in the conveying pipeline control method in the above-mentioned embodiments.

[0119] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of controlling a delivery pipeline, characterized by, The method comprises: obtaining transportation demand data of a target pipeline reference area, and determining a transportation demand type corresponding to the transportation demand data; the target pipeline reference area comprises at least one transportation pipeline; inputting the transportation demand data and the transportation demand type into an optimal operation data prediction model to obtain an optimal operation data set of the target pipeline reference area; the optimal operation data prediction model is used to predict an optimal operation data set based on transportation demand data of a pipeline reference area and a transportation demand type corresponding to the transportation demand data; the operation data set comprises equipment operation data and pipeline operation data; generating a control strategy for the target pipeline reference area based on the optimal operation data set; the control strategy is used to control the operation of a pipeline in the target pipeline reference area to meet transportation demand; the training process of the optimal operation data prediction model comprises: obtaining a plurality of historical operation data sets of the target pipeline reference area under different transportation demand types; each historical operation data set comprises historical pipeline operation data and historical equipment operation data; analyzing a plurality of historical operation data sets of the target pipeline reference area under different transportation demand types to obtain a plurality of historical operation evaluation values of the target pipeline reference area under different transportation demand types; for each transportation demand type in the different transportation demand types, determining an optimal historical operation evaluation value of each transportation demand type, wherein the optimal historical operation evaluation value is the maximum value in the plurality of historical operation evaluation values; training the optimal operation data prediction model to be trained based on the transportation demand data of the target pipeline reference area, the transportation demand type corresponding to the transportation demand data, and the optimal historical operation data set corresponding to the optimal historical operation evaluation value of the transportation demand type, to obtain the trained optimal operation data prediction model.

2. The method of claim 1, wherein, The training of the optimal operation data prediction model to be trained based on the transportation demand data of the target pipeline reference area, the transportation demand type corresponding to the transportation demand data, and the optimal historical operation data set corresponding to the optimal historical operation evaluation value of the transportation demand type comprises: taking the transportation demand data of the target pipeline reference area and the transportation demand type corresponding to the transportation demand data as sample data, and taking the optimal historical operation data set corresponding to the optimal historical operation evaluation value of the transportation demand type as a sample label to form a training sample; training the optimal operation data prediction model to be trained based on the training sample.

3. The method of claim 1, wherein, Before the plurality of historical operation data sets of the target pipeline reference area under different transportation demand types are obtained, the method further comprises: obtaining structural features and transportation features of a pipeline included in a pipeline reference structure diagram; the structural features of the pipeline comprise at least one of the following: the number of sub-pipelines, the number of branch nodes; the transportation features of the pipeline comprise at least one of the following: transportation efficiency, transportation failure frequency, and average transportation failure influence coefficient; determine at least one target pipeline reference region based on the structural features and the transportation features of the pipeline, and mark a range of the at least one target pipeline reference region on the pipeline reference structure diagram.

4. The method of claim 3, wherein, The determining the at least one target pipeline reference region based on the structural features and the transportation features of the pipeline comprises: obtaining at least one initial pipeline reference region based on the structural features of the pipeline; for each initial pipeline reference region of the at least one initial pipeline reference region, correcting the each initial pipeline reference region based on the transportation features of the pipeline in the each initial pipeline reference region to obtain the at least one target pipeline reference region.

5. The method of claim 1, wherein, The multiple historical operation data sets of the target pipeline reference region under different transportation demand types include multiple historical operation data sets of multiple historical transportation periods of the target pipeline reference region under each transportation demand type. The analyzing the multiple historical operation data sets of the target pipeline reference region under different transportation demand types to obtain multiple historical operation evaluation values of the target pipeline reference region under different transportation demand types comprises: for each transportation demand type, comparing each historical pipeline operation data of a same historical transportation period with preset pipeline operation data to obtain a first operation evaluation value, and generating a first compensation coefficient according to a fluctuation interval of all historical pipeline operation data of the same historical transportation period; comparing each historical equipment operation data of the same historical transportation period with preset equipment operation data to obtain a second operation evaluation value, and generating a second compensation coefficient according to a fluctuation interval of all historical equipment operation data of the same historical transportation period; determining a third operation evaluation value according to the first operation evaluation value, the first compensation coefficient, the second operation evaluation value and the second compensation coefficient; performing mean value processing on multiple third operation evaluation values of the same historical transportation period to obtain a fourth operation evaluation value; obtaining the historical operation evaluation value based on the fourth operation evaluation values of the multiple historical transportation periods.

6. An electronic device, comprising: An electronic device comprises a processor and a memory. The memory stores instructions executable by the processor. The processor is configured to execute the instructions, so that the electronic device implements the method of any one of claims 1-5.

7. A readable storage medium, characterized by, The readable storage medium comprises software instructions. When the software instructions are executed in the electronic device, the electronic device implements the method of any one of claims 1-5.

8. A computer program product, characterised in that, The computer program product comprises computer instructions. When the computer instructions are executed in the electronic device, the electronic device implements the method of any one of claims 1-5.

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