Multi-point collaborative early warning system of emulsion pump station

Abstract

The invention discloses a multi-point cooperative early warning system of an emulsion pump station, and relates to the technical field of cooperative early warning, and the system comprises a main and branch pipeline determination module which is used for determining a main pipeline and a plurality of branch pipelines in the emulsion pump station; the monitoring module configuration module is used for configuring a main pipeline monitoring module and a branch pipeline monitoring module; the pipeline monitoring module is used for connecting the main pipeline monitoring data set and the branch pipeline monitoring data set; the collaborative anomaly analysis module is used for carrying out collaborative anomaly analysis; and the abnormity alarm module is used for generating a collaborative abnormal pipeline source, marking the collaborative abnormal pipeline source, and generating an abnormity early warning signal for alarming. The technical problems that in the prior art, the emulsion state of the main pipeline and the branch pipeline of the emulsion pump station is not monitored comprehensively, the cooperative abnormal condition of the main pipeline and the branch pipeline cannot be analyzed effectively, and potential faults are difficult to find and warn in time are solved, and comprehensive monitoring of the emulsion state of the main pipeline and the branch pipeline of the emulsion pump station is achieved. And safe and stable operation of the pump station is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative early warning, and particularly to a multi-point collaborative early warning system for an emulsion pump station. Background Art

[0002] In the field of industrial production, emulsion pump stations are widely used in various processing technologies, such as metal cutting, pressure forming, etc., to provide functions such as cooling, lubrication, and chip removal for equipment, and play a key role in ensuring the smooth progress of the production process. However, there are many problems in the monitoring of the main and branch pipelines of emulsion pump stations in the prior art. On the one hand, most monitoring systems only monitor the main pipeline or branch pipelines separately, lacking the collaborative analysis of the emulsion state of the main and branch pipelines, and unable to comprehensively grasp the operating conditions of the entire pipeline system. For example, only focusing on the flow rate of the main pipeline, but not considering the impact of abnormal flow rate in the branch pipeline on the overall emulsion distribution, making it difficult to detect potential failures caused by collaborative problems between the main and branch pipelines. On the other hand, traditional monitoring means often focus on the threshold judgment of a single parameter, lacking in-depth analysis of the complex relationships between multiple parameters. For example, simply judging abnormalities based on whether the emulsion temperature exceeds the standard, while ignoring the mutual correlations between parameters such as temperature, pressure, and flow rate, making it impossible to detect some abnormal situations hidden in the comprehensive changes of parameters in a timely manner.

[0003] The prior art has technical problems such as incomplete monitoring of the emulsion state of the main and branch pipelines of the emulsion pump station, inability to effectively analyze the collaborative abnormalities of the main and branch pipelines, and difficulty in timely detecting and warning potential failures. Summary of the Invention

[0004] The present application provides a multi-point collaborative early warning system for an emulsion pump station, which is used to solve the technical problems in the prior art of incomplete monitoring of the emulsion state of the main and branch pipelines of the emulsion pump station, inability to effectively analyze the collaborative abnormalities of the main and branch pipelines, and difficulty in timely detecting and warning potential failures.

[0005] In view of the above problems, the present application provides a multi-point collaborative early warning system for an emulsion pump station.

[0006] The present application provides a multi-point collaborative early warning system for an emulsion pump station, and the system includes: The main branch pipeline determination module is used to determine the main pipeline and multiple branch pipelines in the emulsion pump station; the monitoring module configuration module is used to configure the main pipeline monitoring module and multiple branch pipeline monitoring modules for the main pipeline and the multiple branch pipelines respectively; the pipeline monitoring module is used to connect to the main pipeline monitoring module to obtain the main pipeline monitoring data set of the main pipeline, and connect to the multiple branch pipeline monitoring modules to obtain the multiple branch pipeline monitoring data sets of the multiple branch pipelines; the collaborative anomaly analysis module is used to perform collaborative anomaly analysis on the emulsion state of the main and branch pipelines according to the main pipeline monitoring data set and the multiple branch pipeline monitoring data sets, and generate independent main pipeline anomaly data and independent anomaly data for any branch pipeline; the anomaly alarm module is used to perform collaborative anomaly impact analysis on the main pipeline and the multiple branch pipelines with the independent main pipeline anomaly data and the independent anomaly data for any branch pipeline, generate and mark the collaborative anomaly pipeline source, and generate an anomaly warning signal for the marked pipeline for alarm.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The main branch pipeline determination module is used to determine the main pipeline and multiple branch pipelines in the emulsion pump station; the monitoring module configuration module is used to configure the main pipeline monitoring module and multiple branch pipeline monitoring modules for the main pipeline and the multiple branch pipelines respectively; the pipeline monitoring module is used to connect to the main pipeline monitoring data set and the branch pipeline monitoring data set; the collaborative anomaly analysis module is used to perform collaborative anomaly analysis on the emulsion state of the main and branch pipelines, and generate independent main pipeline anomaly data and independent anomaly data for any branch pipeline; the anomaly alarm module is used to perform collaborative anomaly impact analysis, generate and mark the collaborative anomaly pipeline source, and generate an anomaly warning signal for the marked pipeline for alarm. It achieves the technical effect of realizing the comprehensive monitoring of the emulsion state of the main and branch pipelines of the emulsion pump station and ensuring the safe and stable operation of the pump station. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic structural diagram of a multi-point collaborative early warning system for an emulsion pump station provided by an embodiment of this application; Figure 2 It is a schematic structural execution diagram of a collaborative anomaly analysis module of a multi-point collaborative early warning system for an emulsion pump station provided by an embodiment of this application.

[0010] Description of the attached drawing reference numerals: main branch pipeline determination module 10, monitoring module configuration module 20, pipeline monitoring module 30, collaborative anomaly analysis module 40, anomaly alarm module 50. Detailed implementation manners

[0011] This application provides a multi-point collaborative early warning system for an emulsion pump station, which is used to solve the technical problems in the prior art that the monitoring of the emulsion state of the main and branch pipelines of the emulsion pump station is not comprehensive, the collaborative anomalies of the main and branch pipelines cannot be effectively analyzed, and potential faults are difficult to be discovered and warned in time.

[0012] Next, the technical solutions in the embodiments of this application will be clearly and completely described with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0013] Embodiment, as Figure 1 shown, this application provides a multi-point collaborative early warning system for an emulsion pump station, and the system includes: A main branch pipeline determination module 10, which is used to determine the main pipeline and multiple branch pipelines in the emulsion pump station.

[0014] Specifically, the main branch pipeline determination module 10 determines the main pipeline and multiple branch pipelines therein through a detailed investigation and precise analysis of the complex pipeline layout inside the pump station. In the pump station facilities, the configuration of multiple pipelines can flexibly perform pipeline switching operations or flow rate adjustments according to actual production requirements by means of distribution valves and switching valve devices. For example, in some working conditions, when the demand for emulsion suddenly increases, the emulsion in the main pipeline is more directed to the connected branch pipelines through the switching valve to ensure the normal operation of the equipment. The main pipeline undertakes the initial transportation task of the emulsion, stably transporting the emulsion from the source of the pump station to each key node. The branch pipelines, according to the diverse requirements of different equipment in different production links, precisely distribute the emulsion to ensure that each piece of equipment that requires emulsion can obtain appropriate supply, thereby maintaining the smooth progress of the entire production process and providing an indispensable prerequisite for the efficient and stable operation of the emulsion pump station.

[0015] A monitoring module configuration module 20, which is used to respectively configure a main pipeline monitoring module and multiple branch pipeline monitoring modules for the main pipeline and the multiple branch pipelines.

[0016] Specifically, for the main pipeline, the monitoring module configuration module 20 installs a series of adapted monitoring devices to form the main pipeline monitoring module. For example, at the key nodes and parts prone to problems in the main pipeline, temperature sensors are arranged. These sensors can real-time sense the temperature changes of the emulsion when flowing in the main pipeline. Once the temperature exceeds the normal range, it indicates that the performance of the emulsion is abnormal or there are potential faults in the pipeline. At the same time, the flow sensor will accurately measure the flow rate of the emulsion in the main pipeline to ensure that the flow rate is stable within a reasonable range. If the flow rate suddenly changes, it means there is a leakage or blockage in the pipeline. The flow velocity sensor is responsible for monitoring the flow velocity of the emulsion, and its data helps to analyze the smoothness of the pipeline and the operation efficiency of the pumping station.

[0017] Regarding multiple branch pipelines, according to the specific characteristics of each branch pipeline and the requirements of the connected equipment, a branch pipeline monitoring module is customized for each branch pipeline. Since the requirements for the emulsion by the equipment served by different branch pipelines are different, the configuration of the branch pipeline monitoring module will also be different. For example, for the branch pipeline that transports the emulsion to high-precision processing equipment, the sensors in its monitoring module have higher precision, so as to more sensitively capture the subtle changes in the state of the emulsion, thereby timely discovering potential hazards that may affect the normal operation of the equipment, ensuring the stable and safe operation of the entire emulsion pumping station system, and providing a reliable data source for subsequent data analysis and anomaly judgment.

[0018] The pipeline monitoring module 30 is used to connect to the main pipeline monitoring module to obtain the main pipeline monitoring data set of the main pipeline, and connect to the multiple branch pipeline monitoring modules to obtain the multiple branch pipeline monitoring data sets of the multiple branch pipelines.

[0019] Specifically, the pipeline monitoring module 30 establishes a close communication link with the main pipeline monitoring module through connection lines. These connection lines use high-reliability industrial-grade cables, which have good anti-interference ability and signal transmission stability, ensuring that various data from the main pipeline monitoring module can be continuously and accurately received. Devices such as temperature sensors, flow sensors, and flow velocity sensors in the main pipeline monitoring module collect data according to the set frequency. These data cover key indicators such as the temperature change of the emulsion in the main pipeline, the real-time value of the flow rate, and the dynamic information of the flow velocity. Receive and integrate these data with extremely high efficiency, so as to form a comprehensive and detailed main pipeline monitoring data set, providing a solid data basis for subsequent analysis of the operating state of the main pipeline.

[0020] Meanwhile, the pipeline monitoring module 30 is efficiently connected to multiple branch pipeline monitoring modules. For the unique layouts and operating characteristics of different branch pipelines, connection technologies with good adaptability are adopted to ensure that the data collected by each branch pipeline monitoring module can be stably obtained. Each branch pipeline monitoring module is also equipped with sensors such as temperature, flow rate, and flow velocity. The data collected by them reflects the specific state of the emulsion liquid in each branch pipeline. The pipeline monitoring module 30 will classify, organize, and summarize the monitoring data from each branch pipeline to form multiple branch pipeline monitoring data sets. These data sets completely record the operating conditions of different branch pipelines, providing indispensable data support for comprehensively understanding the working state of the entire emulsion pump station, so as to timely detect potential abnormalities and problems and ensure the safe and stable operation of the pump station.

[0021] The collaborative anomaly analysis module 40 is used to perform collaborative anomaly analysis on the emulsion liquid state of the main pipeline and the branch pipelines according to the main pipeline monitoring data set and the multiple branch pipeline monitoring data sets, and generate main pipeline independent anomaly data and any branch pipeline independent anomaly data.

[0022] Specifically, based on the main pipeline monitoring dataset and multiple branch pipeline monitoring datasets, the collaborative anomaly analysis module 40 conducts a comprehensive and in-depth collaborative anomaly analysis on the emulsion state of the main and branch pipelines. The main pipeline monitoring dataset contains rich data collected by temperature sensors, flow sensors, and flow velocity sensors at various points on the main pipeline. These data reflect key information such as the temperature, flow rate, and flow velocity of the emulsion in the main pipeline at different times. The multiple branch pipeline monitoring datasets are similar, respectively recording the data of the corresponding sensors on each branch pipeline. During the analysis process, the main pipeline monitoring data is carefully sorted out, and data mining techniques and statistical methods are used to analyze the changing trends of parameters such as temperature, flow rate, and flow velocity over time, as well as the mutual relationships between the parameters. For example, under normal circumstances, the flow rate and flow velocity should show a certain proportional relationship. If within a certain period, the flow rate suddenly drops significantly, while the flow velocity does not decrease correspondingly, and even shows abnormal fluctuations, and the temperature also deviates from the normal range, these abnormal situations are identified and extracted, and the independent abnormal data of the main pipeline is sorted out. For the multiple branch pipeline monitoring datasets, in-depth analysis is carried out. Considering the functional characteristics of each branch pipeline, the connection method with the main pipeline, and the different specific equipment or areas it serves, the data of each branch pipeline is analyzed separately. For example, a certain branch pipeline provides emulsion for a specific processing equipment, and its flow rate and temperature need to meet the specific process requirements of the equipment. If the flow rate of this branch pipeline continuously falls below the flow rate required for the normal operation of the equipment during a certain period, and at the same time the temperature also exceeds the range that the equipment can withstand, this is recorded as an abnormal situation of this branch pipeline, forming the independent abnormal data of any branch pipeline. Through this collaborative analysis of the main and branch pipeline data, the abnormal situations of the emulsion state of the main and branch pipelines are accurately identified, and the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline are respectively generated, providing important data support for subsequent anomaly tracing and fault handling.

[0023] The anomaly alarm module 50 is used to conduct a collaborative anomaly impact analysis on the main pipeline and the multiple branch pipelines with the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline, generate and mark the collaborative abnormal pipeline source, and generate an abnormal warning signal for the marked pipeline for alarm.

[0024] Specifically, after the abnormal alarm module 50 obtains the main pipeline independent abnormal data and any branch pipeline independent abnormal data generated by the collaborative abnormal analysis module, it quickly starts the work process. First, through the built-in data analysis engine, it deeply mines and correlates these abnormal data. For the independent abnormal data of the main pipeline, it comprehensively sorts out all parameter changes related to this abnormality, such as the range of flow rate fluctuations that may be caused by sudden temperature changes, the impact of reduced flow velocity on the system pressure, etc., and combines the handling experience of similar abnormal situations in historical data to establish a preliminary impact model. For the independent abnormal data of the branch pipeline, it also analyzes in detail its potential impact on the connected equipment and the part of the main pipeline that may be affected. For example, if the flow rate of a certain branch pipeline increases abnormally, track the impact of this change on the operating stability of the downstream equipment and whether it will interfere with the normal transportation of the main pipeline due to factors such as back pressure. When comprehensively considering the complex interaction relationship between the main and branch pipelines, the method of system dynamics is used to construct a collaborative impact network model of the main and branch pipelines. In this model, the relationships such as energy transfer, flow distribution, and pressure coupling between each pipeline are accurately quantified. By simulating the system response under different abnormal situations, the root cause and propagation path of the abnormality are accurately located, thereby generating the collaborative abnormal pipeline source. When the collaborative abnormal pipeline source is determined, it is immediately marked, and the marking information covers key contents such as the abnormal type, severity, and possible impact range. Subsequently, according to the preset alarm rules and priority system, corresponding abnormal warning signals are generated for the marked pipelines. These signals can be alarmed in various ways such as through audible and visual alarms, pop-up windows on the in-station monitoring system, and sending text messages or push notifications to relevant maintenance personnel, ensuring that relevant personnel are notified in a timely manner so that they can quickly take measures to deal with the abnormal situation, minimize the impact on the operation of the emulsion pump station, and ensure production safety.

[0025] In a possible implementation manner, as Figure 2 shown, the collaborative abnormal analysis module 40 further includes: A demand parameter receiving unit, configured to receive the preset operation demand parameters of the emulsion pump station, where the preset operation scenario includes the emulsion demand parameters of the emulsion supply target connected by the multiple branch pipelines.

[0026] An operation parameter generating unit, configured to call the main and branch pipeline dispatching center to analyze the emulsion demand parameters, and generate the main pipeline emulsion operation parameters of the main pipeline and the multiple branch pipeline emulsion operation parameters of the multiple branch pipelines.

[0027] A twin main and branch pipeline operation model establishing unit, configured to model with the main pipeline emulsion operation parameters and the multiple branch pipeline emulsion operation parameters to establish a twin main and branch pipeline operation model.

[0028] An independent abnormal data generation unit is used to train a main pipeline collaborative anomaly recognition model with the twin main pipeline operation model, call the main pipeline collaborative anomaly recognition model to perform collaborative analysis on the main pipeline monitoring data set and the multiple branch pipeline monitoring data sets, and generate the main pipeline independent abnormal data and the independent abnormal data of any branch pipeline.

[0029] Specifically, the demand parameter receiving unit has specially designed data receiving interfaces and communication protocols, and can establish stable connections with the control system of the pump station, the production process planning system, and the relevant equipment management database, etc. Through these connection channels, it continuously collects and filters out the preset operation demand parameters of the emulsion pump station. Especially for the emulsion supply targets connected by multiple branch pipelines, it can accurately obtain the key demand parameters such as the flow rate, flow volume, and temperature of the emulsion. For example, in some high-precision machining operation scenarios, the processing equipment connected to the branch pipeline requires the emulsion to have a specific temperature range to ensure machining accuracy, and at the same time requires a stable flow rate and an appropriate flow volume to achieve good lubrication and cooling effects. The demand parameter receiving unit will update these parameter information in a timely manner according to the predetermined time interval or event trigger mechanism, and transmit it to the subsequent processing module, providing indispensable basic data support for the generation of operation parameters and anomaly analysis of the entire pump station, ensuring that the pump station can operate efficiently and stably according to the actual production requirements.

[0030] The operating parameter generation unit is closely connected to the main pipeline scheduling center. Once it receives the emulsion demand parameters from the demand parameter receiving unit, including flow rate, flow volume, and temperature, it quickly initiates the analysis process. For the main pipeline, based on the basic principles of fluid mechanics and the actual equipment performance parameters of the pump station, by comprehensively considering factors such as the pipe diameter of the main pipeline, the roughness of the pipeline material, the output power of the pump station power equipment, and the pressure loss of the entire system, precise calculations are carried out using the Bernoulli equation and the continuity equation to determine the optimal operating parameters of the emulsion in the main pipeline. For example, according to the power output of the pump station and the physical characteristics of the main pipeline, the flow rate range that can ensure stable emulsion transportation with the lowest energy consumption and the flow volume value that meets the requirements of the entire pump station are calculated, and a suitable temperature range is determined in combination with environmental factors and the physical and chemical properties of the emulsion itself. For multiple branch pipelines, the unique process requirements of the emulsion supply targets connected to each branch pipeline are analyzed. If a certain branch pipeline is connected to high-precision processing equipment that is extremely sensitive to temperature, then when generating the operating parameters of the emulsion in the branch pipeline, emphasis is placed on how to adjust the flow volume and flow rate to ensure that the temperature of the emulsion transported to this equipment always remains within the extremely small fluctuation range allowed by the equipment. Through this personalized analysis and calculation for different branch pipelines, the operating parameter generation unit generates accurately matching emulsion operating parameters for each branch pipeline, providing a key data basis for subsequent model establishment and system operation, and ensuring the efficient and safe operation of the emulsion pump station under various working conditions.

[0031] Twin main and branch pipeline operation model establishment unit First of all, at the physical modeling level, based on basic scientific principles such as fluid mechanics and thermodynamics, a detailed mathematical description of the flow process of the emulsion in the main pipeline and branch pipelines is carried out. For the main pipeline, considering its geometric characteristics such as pipe diameter, length, roughness, and the connection relationship with the pump station power source, a mathematical model that can reflect the changes in the flow velocity, flow rate, and pressure of the emulsion along the way is constructed. For the branch pipelines, combined with their respective different pipe diameters, orientations, and special working condition requirements of the connected equipment, corresponding local flow models are established and coupled with the main pipeline model through boundary conditions. Secondly, in terms of data-driven modeling, a large amount of historical operation data is used to supplement and optimize the above physical model. Collect the actual operation parameters of the emulsion in the main pipeline and branch pipelines under different past working conditions, such as the flow velocity, pressure, etc. data when the flow rate demand and temperature environment change, and through the neural network in machine learning algorithms, explore the potential laws and characteristics in the data, correct and improve the parameters in the physical model, and improve the accuracy and adaptability of the model. Through the deep integration and repeated iterative optimization of physical modeling and data-driven modeling, a twin main and branch pipeline operation model is finally successfully established. This model can highly simulate the operation state of the emulsion in the actual pump station's main and branch pipelines, not only can accurately predict the changes in operation parameters under normal working conditions, but also can simulate and analyze various abnormal working conditions and potential faults, providing a powerful virtual test platform and decision-making support tool for subsequent abnormal identification and system optimization.

[0032] The independent abnormal data generation unit divides the rich and representative normal operation data and various simulated abnormal data obtained from the twin main branch pipeline operation model into a training set, a validation set, and a test set according to a specific ratio. The main branch pipeline collaborative anomaly recognition model is trained using the training set. During the training process, based on the neural network algorithm, the model continuously learns and adjusts its own parameters to establish a discrimination mechanism that can accurately distinguish normal and abnormal states. The validation set is used to evaluate and optimize the model during the training process to ensure that problems such as overfitting do not occur, and to achieve a good balance between generalization ability and accuracy. After sufficient training and optimization, the main branch pipeline collaborative anomaly recognition model has powerful analysis capabilities. When receiving the main pipeline monitoring data set and multiple branch pipeline monitoring data sets from the pipeline monitoring module, the model quickly conducts in-depth analysis on the change trends, fluctuation ranges, and mutual relationships of parameters such as temperature, flow rate, and flow velocity in the main pipeline data, and compares them with the corresponding parameters in the normal state predicted by the twin main branch pipeline operation model. If it is found that the main pipeline data significantly deviates from the normal range and cannot be explained by normal operating conditions, and through correlation analysis, it is determined that the anomaly has no direct association or little impact on the branch pipelines, independent abnormal data for the main pipeline will be generated. Similarly, for multiple branch pipeline monitoring data sets, the model will analyze the parameter conditions of each branch pipeline one by one, considering the interactions between branch pipelines and with the main pipeline. Once the parameters of a certain branch pipeline are abnormal, and through further analysis, it is determined that the anomaly is not caused by the conduction of the main pipeline or indirectly caused by other branch pipelines, but is due to problems with the branch pipeline itself, such as local blockage, sensor failure, etc., independent abnormal data for any branch pipeline will be generated, providing key evidence for subsequent fault troubleshooting and handling, and ensuring the safe and stable operation of the emulsion pump station.

[0033] In a possible implementation manner, the independent abnormal data generation unit further includes: A pipeline collaborative abnormal data generation unit, configured to debug the twin main branch pipeline operation model and generate pipeline collaborative abnormal data.

[0034] A collaborative effect training unit, configured to perform collaborative effect training when the main branch pipeline is abnormal according to the pipeline collaborative abnormal data, and generate the main branch pipeline collaborative anomaly recognition model.

[0035] Specifically, for the first pipeline coordination anomaly data with the main pipeline as the anomaly source, based on the analysis of the pumping station operation principle and historical fault data, specific abnormal operating conditions of the main pipeline are set in the twin main-branch pipeline operation model. For example, simulate the failure of the flow regulating valve in the main pipeline, resulting in a sudden significant drop in the flow rate of the main pipeline or abnormal fluctuations in the flow velocity. During the operation of the model, according to the principles of fluid mechanics and the physical characteristics of the pipeline system, the impact of the main pipeline anomaly on the entire system is simulated. At this time, the key parameter changes of the main pipeline itself and each branch pipeline are monitored and recorded in real time, such as the sudden change in the pressure of the main pipeline, and the data of the chain reaction of the flow rate, pressure, and temperature of each branch pipeline due to the change in the flow rate of the main pipeline. These data constitute the first pipeline coordination anomaly data, which can clearly reflect the system coordination changes caused by the main pipeline anomaly. When generating the second pipeline coordination anomaly data with any branch pipeline as the anomaly source, select a certain branch pipeline. For example, assume that a certain branch pipeline has faults such as local blockage or valve damage due to long-term use, and input these abnormal situations into the twin main-branch pipeline operation model. After the model runs, it will show the impact of the anomaly of this branch pipeline on the main pipeline and other branch pipelines, record the adjustments made by the main pipeline to balance the system pressure or flow rate, and the parameter changes of other branch pipelines caused by factors such as the re-distribution of the flow rate and the change in pressure, such as the adjustment of the flow velocity and the fluctuation of the temperature. These detailed data together constitute the second pipeline coordination anomaly data, providing a basis for analyzing the coordinated impact of a single branch pipeline anomaly on the entire system. For the third pipeline coordination anomaly data with the main pipeline and any branch pipeline as parallel anomaly sources, design a more complex abnormal scenario. For example, simultaneously set the unstable flow rate of the main pipeline due to equipment aging and the sudden leakage fault of a certain branch pipeline in the model. When the model simulates this complex abnormal situation, the unit will comprehensively monitor the dynamic changes of the main pipeline, the abnormal branch pipeline, and other branch pipelines after the anomaly occurs, including the complex transmission and fluctuation of the pressure, the re-distribution of the flow rate, and the non-linear change of the temperature. The changes of these parameters at different time nodes are detailedly recorded to form the third pipeline coordination anomaly data covering the entire system under the parallel anomaly of the main pipeline and the branch pipelines, providing rich data support for in-depth research on the coordinated effect of the system under complex abnormal conditions.

[0036] The synergy training unit then utilizes these pipeline synergy anomaly data and employs the Recurrent Neural Network (RNN) and its variant, the Long Short-Term Memory Network (LSTM) in deep learning for synergy training when the main pipeline has anomalies. The RNN can effectively process time series data, and the LSTM solves the problem of gradient vanishing in the RNN and is suitable for capturing dependency relationships in long sequences. The pipeline synergy anomaly data is input into the LSTM network according to the time series, and the network weights are adjusted through the backpropagation algorithm, enabling the network to learn the synergy pattern of parameter changes when the main pipeline has anomalies, such as the response law of the branch pipeline flow rate after the sudden change of the main pipeline pressure. Finally, a main pipeline synergy anomaly recognition model that can accurately identify the main pipeline synergy anomalies is generated.

[0037] In a possible implementation manner, the pipeline synergy anomaly data generation unit further includes: The pipeline synergy anomaly data includes first pipeline synergy anomaly data with the main pipeline as the anomaly source, second pipeline synergy anomaly data with any branch pipeline as the anomaly source, and third pipeline synergy anomaly data with the main pipeline and any branch pipeline as parallel anomaly sources.

[0038] Specifically, the pipeline synergy anomaly data is a data set generated from a comprehensive simulation and analysis of the anomaly conditions of the main and branch pipelines of the emulsion pump station, which includes three types. The first pipeline synergy anomaly data is generated with the main pipeline as the anomaly source. When debugging the twin main and branch pipeline operation model, by setting abnormal changes in specific parameters of the main pipeline, such as a sudden significant reduction in the main pipeline flow rate or a sharp increase in temperature, the chain reactions of other parameters of each branch pipeline and the main pipeline itself are observed and recorded, such as the pressure fluctuations and flow rate changes in the branch pipeline caused by the change in the main pipeline flow rate. These data constitute the first pipeline synergy anomaly data, reflecting the impact of the main pipeline anomaly on the entire system. The second pipeline synergy anomaly data takes any branch pipeline as the anomaly source. In the model, an anomaly is set for a certain branch pipeline alone, for example, a blockage occurs due to a valve failure in a certain branch pipeline, and then the responses of the main pipeline and other branch pipelines are analyzed, including the flow rate adjustment made by the main pipeline to balance the pressure and the changes in other branch pipelines due to the reallocation of the flow rate. The data recorded are the second pipeline synergy anomaly data, reflecting the synergy changes in the system when a single branch pipeline has an anomaly. The third pipeline synergy anomaly data simulates the situation where the main pipeline and any branch pipeline are used as parallel anomaly sources in the model, such as the simultaneous occurrence of a failure of the main pipeline flow rate regulating valve and a local leakage in a certain branch pipeline. At this time, the parameter changes of the main pipeline, the abnormal branch pipeline, and other branch pipelines under this complex anomaly situation are collected, such as the dynamic change process of data such as pressure, flow rate, and temperature. These data form the third pipeline synergy anomaly data, showing a more complex synergy anomaly situation of the entire system when the main pipeline and the branch pipeline have parallel anomalies.

[0039] In a possible implementation manner, the abnormal alarm module 50 further includes: An abnormal source traceback model establishment unit, configured to establish an abnormal source traceback model for the main pipeline and multiple branch pipelines, wherein the abnormal source traceback model is obtained by training with any one of the flow regulating valves and cooling devices in the main and branch pipelines independently or in combination as the traceback target.

[0040] An adaptation analysis unit, configured to call the abnormal source traceback model to collect the operation data of any main branch pipeline corresponding to any traceback target, perform adaptation analysis on the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline, generate the collaborative abnormal pipeline source and mark it.

[0041] Specifically, first, collect the operation data of a large amount of main branch pipelines under different working conditions. These data include the flow rate and pressure values when the flow regulating valve is at different opening degrees, the temperature data under different refrigeration efficiencies of the cooling device, and the flow velocity, flow rate, temperature and other parameters of the main pipeline and each branch pipeline. At the same time, mark the operation status corresponding to these data, normal or abnormal, and for abnormal situations, clarify the abnormal type and possible reasons. In the training stage, use the decision tree algorithm to analyze the training data. When considering the flow regulating valve alone as the traceback target, the decision tree will divide according to the influence degree of the change of the opening degree of the flow regulating valve on the parameters such as the flow rate and pressure of the main branch pipeline. If a small change in the opening degree of the flow regulating valve leads to a large fluctuation in the flow rate of the main pipeline, and this fluctuation is highly correlated with the known abnormal situation, then this will become an important branch node of the decision tree. For the cooling device, also analyze the influence of the change of its refrigeration efficiency on the temperature parameter, and how these changes are related to the abnormal state of the pipeline system. When considering the combination of the flow regulating valve and the cooling device, comprehensively analyze the change rules of the parameters of the main pipeline when both change at the same time. For example, when the opening degree of the flow regulating valve increases and the refrigeration efficiency of the cooling device decreases, if the temperature of the main pipeline continues to rise and exceeds the normal range, and at the same time the flow distribution of the branch pipeline also appears abnormal, the decision tree will combine these conditions to form a composite branch node. By continuously splitting the data and growing the decision tree, an abnormal source traceback model that can accurately judge whether the abnormal source is the flow regulating valve, the cooling device acting alone or the combined action of both based on the operation data of the main branch pipeline is finally constructed. This model is presented in a tree structure, where each internal node represents a test on an attribute (such as the opening degree of the flow regulating valve, the refrigeration efficiency of the cooling device, etc.), the branch represents the test output, and the leaf node represents the category (i.e., the type of the abnormal source).

[0042] The adaptation analysis unit works based on the abnormal source tracing model. It calls the established abnormal source tracing model, and for each tracing target, collects the operation data of the corresponding main pipeline, which covers key parameters such as flow rate, pressure, and temperature. At the same time, combining the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline generated by the collaborative abnormal analysis module, it uses the correlation analysis algorithm to conduct a comprehensive and detailed analysis. During the analysis process, it compares the actual abnormal data with the expected abnormal pattern based on the tracing target in the model to judge the matching degree between the abnormal data and each tracing target. If it is found that the adaptation index of a certain tracing target and the current abnormal data exceeds the preset threshold, then this tracing target is determined as the collaborative abnormal pipeline source and is marked. The marked content includes the specific information of the tracing target, the possible time of the abnormality occurrence, the impact degree of the abnormality on the pipeline system, etc., so that the subsequent maintenance personnel can quickly locate and handle the abnormal situation and ensure the stable operation of the emulsion pump station.

[0043] In a possible implementation manner, the adaptation analysis unit further includes: Any adaptation index generation unit, which is used to fuse the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline, and then conduct an adaptation comparison of the abnormal data with any main pipeline operation data to generate any adaptation index.

[0044] The tracing target addition unit is used to add the any tracing target into the collaborative abnormal pipeline source if the any adaptation index is greater than the preset adaptation index.

[0045] Specifically, when any adaptation index generation unit conducts abnormal data adaptation comparison, it fuses the main pipeline independent abnormal data with any branch pipeline independent abnormal data. It integrates the abnormal data in all aspects of the main pipeline, such as flow abnormal values, pressure abnormal fluctuation ranges, temperature abnormal change amplitudes, etc., with the corresponding flow, pressure, temperature and other abnormal data of any branch pipeline. For example, if the flow rate of the main pipeline suddenly decreases by a certain value while the pressure of the branch pipeline rises by a specific proportion, these abnormal data will be aggregated together to form a new comprehensive abnormal data set. Any main-branch pipeline operation data is obtained from the abnormal source traceback model, and these data contain various parameter information during the normal operation of the main-branch pipeline, also covering aspects such as flow rate, pressure, and temperature. Correlation analysis is carried out on the fused abnormal data and the main-branch pipeline operation data. Taking the flow parameter as an example, check the correlation between the abnormal change of the flow rate in the fused abnormal data and the normal change of the flow rate in the main-branch pipeline operation data. If in the main-branch pipeline operation data, the flow rate usually changes smoothly within a certain range, while the flow rate in the fused abnormal data shows a large fluctuation, analyze the closeness of the relationship between this fluctuation and the normal flow rate change of the main-branch pipeline. Similarly, analyze other parameters such as pressure and temperature one by one. During the analysis process, comprehensively consider the correlation between the abnormal change of each parameter and the change of the normal operation parameters of the main-branch pipeline, and assign different weights according to the importance of each parameter in the pipeline operation. For example, for some key equipment, the importance of the flow parameter may be higher than that of the temperature parameter, so the weight corresponding to the flow parameter will be larger. Finally, through the weighted calculation of the correlation analysis results of all parameters, a comprehensive value is obtained as any adaptation index. This index can reflect the overall correlation degree between the fused abnormal data and the main-branch pipeline operation data, that is, the adaptation situation of the abnormal data. The higher the index value, the stronger the correlation between the abnormal data and the main-branch pipeline operation data, and the higher the adaptation degree; on the contrary, the lower the index value, the lower the adaptation degree.

[0046] After any adaptation index generation unit generates any adaptation index, the traceability target addition unit will immediately compare this index with the preset adaptation index. The preset adaptation index is determined based on multiple factors such as a large amount of historical data, equipment operation standards, and industry experience, and is an important threshold for judging whether the abnormal source matches. If any adaptation index is greater than the preset adaptation index, it indicates that there is a high degree of matching between the currently analyzed abnormal data and the corresponding main-branch pipeline operation data, meaning that this traceability target is the source causing the pipeline collaborative abnormality. At this time, the traceability target addition unit will add this any traceability target into the collaborative abnormal pipeline source. This operation continuously improves the collaborative abnormal pipeline source, provides a clear and accurate direction for subsequent fault troubleshooting and repair work, and greatly improves the efficiency and accuracy of fault handling.

[0047] In a possible implementation manner, the pipeline monitoring module 30 further includes: The main pipeline monitoring module includes temperature sensors, flow sensors and flow velocity sensors arranged at various points on the main pipeline, and the multiple branch pipeline monitoring modules respectively include temperature sensors, flow sensors and flow velocity sensors at different points on each branch pipeline.

[0048] Specifically, in the monitoring system of the emulsion pump station, the main pipeline monitoring module is composed of multiple precise sensors, which are arranged at various key points on the main pipeline. Among them, the temperature sensor is responsible for capturing the temperature information of the emulsion in the main pipeline at different positions in real time, accurately measuring the temperature change of the emulsion, and providing an important basis for judging the thermal stability of the emulsion and the heat dissipation situation during the operation of the equipment. The flow sensor is used to measure the flow data of the emulsion in the main pipeline. By monitoring the flow at different points, it is possible to understand whether the delivery volume of the emulsion in the entire main pipeline is stable, whether there are sudden changes or abnormal fluctuations in the flow, which is crucial for ensuring the normal liquid supply of the pump station and the stable operation of the subsequent production process. The flow velocity sensor can accurately sense the flow velocity of the emulsion in the main pipeline. By monitoring the change of the flow velocity, it can not only assist in judging whether there are problems such as blockage or leakage in the pipeline, but also provide more accurate parameter support for the accurate calculation of the flow. At the same time, the multiple branch pipeline monitoring modules respectively correspond to each branch pipeline, and their composition structures are similar to those of the main pipeline monitoring module, and are also composed of temperature sensors, flow sensors and flow velocity sensors. The difference is that these sensors are installed at different points on each branch pipeline. The selection of these points fully considers the equipment layout, process requirements and potential failure risk points on the branch pipeline. For example, a temperature sensor will be installed near a specific processing equipment on the branch pipeline to timely understand whether the temperature of the emulsion entering the equipment meets the equipment operation requirements; a flow sensor will be arranged near the flow regulating valve on the branch pipeline to monitor whether the flow after valve regulation reaches the expected value; and the flow velocity sensor is set at a narrow part of the pipeline or an area prone to turbulent flow to accurately measure the change of the flow velocity, and then analyze whether the flow state of the emulsion in the pipeline is normal. Through these branch pipeline monitoring modules, it is possible to comprehensively and meticulously master the real-time operation parameters of the emulsion in each branch pipeline, providing a solid data guarantee for the stable and efficient operation of the entire emulsion pump station system.

[0049] In a possible implementation manner, the abnormal alarm module 50 further includes: A main pipeline particle sensing data monitoring unit, which is used to set particle sensors on the main pipeline and monitor the main pipeline particle sensing data.

[0050] A pipeline damage index establishment unit, which is used to perform pipeline damage analysis based on the main pipeline particle sensing data and establish a pipeline damage index.

[0051] An abnormal warning signal sending unit, configured to send a first abnormal warning signal for the emulsion particles if the pipeline damage index is greater than or equal to a preset damage index.

[0052] An operation damage index generating unit, configured to perform operation damage analysis on the emulsion supply targets connected to multiple branch pipelines with the main pipeline particle sensing data if the pipeline damage index is less than the preset damage index, and generate multiple operation damage indexes.

[0053] A damage threshold judgment unit, configured to send a second abnormal warning signal for the emulsion particles when the multiple operation damage indexes are greater than or equal to their respective damage thresholds.

[0054] Specifically, in the operation monitoring system of the emulsion pump station, the main pipeline particle sensing data monitoring unit is responsible for acquiring the data related to the emulsion particles in the main pipeline. Given that the emulsion in the branch pipeline originates from the transportation of the main pipeline, as long as there are particles in the main pipeline emulsion, the branch pipeline will inevitably have the same situation. Therefore, particle sensors are only set on the main pipeline. These particle sensors are deployed at specific positions on the main pipeline and can keenly sense various information about the particles in the emulsion, such as the concentration of the particles, the particle size, the material composition of the particles, etc. Through continuous and real-time monitoring, the sensors accurately collect this data, convert it into electrical signals or digital signals, and then transmit it to the subsequent data processing module. These main pipeline particle sensing data play a crucial fundamental supporting role in comprehensively understanding the cleanliness of the emulsion and evaluating its possible impact on the pipeline system and related equipment.

[0055] Deeply analyze the main pipeline particle sensing data, which cover detailed information such as the concentration of the particles in the emulsion, the particle size distribution, the particle hardness, and the chemical composition of the particles. Based on the principles of materials science, consider the wear effect of particles with different particle sizes and hardnesses on the pipeline material. For example, particles with a larger particle size and high hardness, when colliding with the inner wall of the pipeline in the high-speed flowing emulsion, will produce an effect similar to abrasive wear, gradually reducing the pipeline wall thickness over time. At the same time, the chemical composition of the particles may cause corrosion of the pipeline. For example, some corrosive particle components react with the pipeline material, weakening the structural strength of the pipeline. By using mathematical models and statistical analysis methods, these influencing factors are comprehensively considered. A multiple linear regression model is adopted, with the particle concentration, particle size, hardness, etc. as independent variables and the actual wear amount or corrosion degree of the pipeline as the dependent variable. The model parameters are calibrated through a large amount of historical data, so as to establish a pipeline damage index that can accurately reflect the pipeline damage degree. This index is not a simple numerical value, but a quantitative manifestation of the comprehensive influence of various particle factors on pipeline damage, providing a key basis for subsequent abnormal judgment and equipment maintenance.

[0056] The abnormal warning signal sending unit monitors the changes in the pipeline damage index in real time. After the pipeline damage index establishing unit generates the pipeline damage index, it immediately makes an accurate comparison with the preset damage index that is preset in advance. This preset damage index is comprehensively determined based on various factors such as the material properties of the pump station pipeline, the designed service life, the safe operation standards, and past operation experience, and it is an important threshold for measuring whether the pipeline is in a safe operation state. Once the abnormal warning signal sending unit determines that the pipeline damage index is greater than or equal to the preset damage index, it means that due to the action of particles in the emulsion, the damage degree of the pipeline has reached or exceeded the safe range, and continued operation may cause serious faults that affect the normal operation of the pump station, such as pipeline leakage, blockage, or even rupture. At this time, the abnormal warning signal sending unit will quickly and accurately send the first emulsion particle abnormal warning signal. This warning signal may be presented in the form of an audible and visual alarm in the monitoring center of the pump station. At the same time, the relevant alarm information will also be immediately pushed to the mobile terminal devices of the pump station management personnel to ensure that they can learn about the abnormal situation in the first time and take corresponding measures in a timely manner, such as arranging maintenance personnel to conduct a comprehensive inspection and repair of the pipeline to ensure the safe and stable operation of the pump station.

[0057] When the pipeline damage index is less than the preset damage index, the operation damage index generating unit focuses on the main pipeline particle sensing data and conducts a comprehensive operation damage analysis for various emulsion supply targets connected to multiple branch pipelines, such as processing equipment with different production processes and reaction kettles for specific chemical reactions. For each supply target, this unit selects the particle characteristic parameters closely related to the operation damage according to its working principle, performance parameters, and requirements for the cleanliness of the emulsion, such as particle concentration, particle size distribution, particle shape factor, hardness, etc. For example, for precision machining equipment, the particle size distribution and particle hardness have a greater impact on the machining accuracy; for chemical reaction devices, the particle concentration and chemical properties may play a key role in the reaction process. Then, a large amount of historical data is collected, which covers the operation effect data of each supply target under different particle characteristic parameters. Using these data, through statistical analysis methods, a quantitative relationship between the particle characteristic parameters and the operation damage is established. Association rule mining is used to find the association between a specific particle size range and the type of product quality defects. Finally, the current main pipeline particle sensing data is substituted into the established relationship model to calculate the operation damage index corresponding to each emulsion supply target. These indexes quantify the potential damage degree of the emulsion particles to each operation and provide a strong basis for evaluating the operation risk and taking targeted measures in advance. By performing the above operations on all the supply targets connected to the branch pipelines, multiple operation damage indexes are generated.

[0058] After generating multiple job damage indicators, the damage threshold judgment unit carefully compares each job damage indicator with its respective pre-set damage threshold. These damage thresholds are determined based on various factors such as the equipment characteristics of the emulsion supply target, process standards, safe operation requirements, and long-term practical experience. They are the key boundaries for measuring whether a job faces unacceptable risks. Once the damage threshold judgment unit confirms that one or more of the multiple job damage indicators are greater than or equal to their respective corresponding damage thresholds, it means that the particles in the emulsion have caused significant potential risks to the corresponding job. If not dealt with in a timely manner, it may lead to adverse consequences such as reduced production efficiency, decreased product quality, and equipment failures. At this time, the damage threshold judgment unit will immediately issue a second emulsion particle abnormality warning signal. This warning signal may be presented in a prominent audible and visual form at the monitoring center, and at the same time, it will be quickly conveyed to relevant operators and managers through text messages, in-station notifications, etc., to ensure that the abnormality can be detected in the first time and effective measures can be taken in a timely manner, such as adjusting the emulsion filtration system, replacing equipment parts, etc., to reduce risks and ensure the stability and safety of production operations.

[0059] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be beneficial.

[0060] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, changes, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. The multi-point coordinated early warning system of the emulsion pump station is characterized by: include: A main and branch pipeline determination module, used to determine the main pipeline and multiple branch pipelines in the emulsion pump station; A monitoring module configuration module, used to configure a main pipeline monitoring module and multiple branch pipeline monitoring modules for the main pipeline and the multiple branch pipelines respectively; A pipeline monitoring module, used to connect to the main pipeline monitoring module to obtain the main pipeline monitoring data set of the main pipeline, and connect to the multiple branch pipeline monitoring modules to obtain multiple branch pipeline monitoring data sets of the multiple branch pipelines; A collaborative abnormality analysis module, used to perform collaborative abnormality analysis of the emulsion state of the main and branch pipelines according to the main pipeline monitoring data set and the plurality of branch pipeline monitoring data sets, and generate independent abnormality data of the main pipeline and independent abnormality data of any branch pipeline; The abnormal alarm module is used to analyze the coordinated abnormal impact of the main pipeline and the multiple branch pipelines based on the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline, generate and mark the coordinated abnormal pipeline source, and generate an abnormal warning signal for the marked pipeline to alarm.

2. The multi-point coordinated early warning system for an emulsion pump station according to claim 1, characterized in that: The collaborative anomaly analysis module also includes: A demand parameter receiving unit, configured to receive preset operation demand parameters of the emulsion pump station, wherein the preset operation scenario includes emulsion demand parameters of emulsion supply targets connected to the multiple branch pipelines; An operation parameter generating unit, used for calling a main-branch pipeline dispatching center to analyze the emulsion demand parameter, and generating a main pipeline emulsion operation parameter of the main pipeline and a plurality of branch pipeline emulsion operation parameters of the plurality of branch pipelines; A twin main-branch pipeline operation model establishing unit, used to establish a twin main-branch pipeline operation model by modeling the main pipeline emulsion operation parameters and the plurality of branch pipeline emulsion operation parameters; An independent abnormal data generation unit is used to train a main-branch pipeline collaborative abnormality recognition model with the twin main-branch pipeline operation model, call the main-branch pipeline collaborative abnormality recognition model to collaboratively analyze the main pipeline monitoring data set and the multiple branch pipeline monitoring data sets, and generate the main pipeline independent abnormality data and any branch pipeline independent abnormality data.

3. The multi-point coordinated early warning system for an emulsion pump station according to claim 2, characterized in that: The independent abnormal data generating unit also includes: A pipeline coordination abnormal data generating unit, used for debugging the twin main and branch pipeline operation model and generating pipeline coordination abnormal data; The synergy effect training unit is used to perform synergy effect training when the main and branch pipelines are abnormal based on the pipeline synergy abnormality data, and generate the main and branch pipeline synergy abnormality recognition model.

4. The multi-point coordinated early warning system for an emulsion pump station according to claim 3, characterized in that: The pipeline coordinated abnormality data includes first pipeline coordinated abnormality data with the main pipeline as the abnormality source, second pipeline coordinated abnormality data with any branch pipeline as the abnormality source, and third pipeline coordinated abnormality data with the main pipeline and any branch pipeline as parallel abnormality sources.

5. The multi-point coordinated early warning system for an emulsion pump station according to claim 1, characterized in that: The abnormal alarm module also includes: An abnormal source backtracking model establishing unit is used to establish an abnormal source backtracking model for the main pipeline and multiple branch pipelines, wherein the abnormal source backtracking model is used to be trained by using any device of the flow control valve and the cooling device of the main and branch pipelines independently or in combination as a tracing target; The adaptation and analysis unit is used to call the abnormal source backtracking model to collect any main and branch pipeline operation data corresponding to any traceability target, perform adaptation and analysis on the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline, generate the collaborative abnormal pipeline source and mark it.

6. The multi-point coordinated early warning system for an emulsion pump station according to claim 5, characterized in that: The adaptation analysis unit also includes: Any adaptation index generating unit is used to fuse the independent abnormal data of the main pipeline and the independent abnormal data of any branch pipeline, and then perform an adaptation comparison of the abnormal data with the operation data of any main and branch pipelines to generate any adaptation index; The traceability target adding unit is used to add any traceability target into the collaborative abnormal pipeline source if any adaptation index is greater than a preset adaptation index.

7. The multi-point coordinated early warning system for an emulsion pump station according to claim 1, characterized in that: The main pipeline monitoring module includes a temperature sensor, a flow sensor and a flow velocity sensor arranged at various points on the main pipeline, and the multiple branch pipeline monitoring modules respectively include a temperature sensor, a flow sensor and a flow velocity sensor located at different points on each branch pipeline.

8. The multi-point coordinated early warning system for an emulsion pump station according to claim 1, characterized in that: It also includes an abnormal emulsion warning module: A main line particle sensor data monitoring unit, used to set a particle sensor on the main line to monitor the main line particle sensor data; A pipeline damage index establishing unit, used to perform pipeline damage analysis based on the main pipeline particle sensing data and establish a pipeline damage index; an abnormal warning signal issuing unit, configured to issue a first emulsion particle abnormal warning signal if the pipeline damage index is greater than or equal to a preset damage index; an operation damage index generating unit, configured to perform operation damage analysis on emulsion supply targets connected to a plurality of branch pipelines using the particle sensing data of the main pipeline to generate a plurality of operation damage indexes if the pipeline damage index is less than a preset damage index; The damage threshold judgment unit is used to issue a second emulsion particle abnormality warning signal when the multiple operation damage indicators are greater than or equal to their respective damage thresholds.

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