Method, device and equipment for evaluating operation state of booster station and medium

Abstract

The invention provides a booster station operation state evaluation method, device and equipment and a medium, relates to the technical field of pipeline monitoring, and aims to solve the problem of how to accurately and effectively evaluate the operation state of a booster station. The method for evaluating the operation state of the booster station comprises the following steps: determining a pipeline state monitoring point set in the booster station, and setting a pipeline state monitoring area according to the pipeline state monitoring point set; acquiring real-time monitoring operation information and historical calibration standard information of the pipeline state monitoring area, and generating a state comparison fusion matrix according to the real-time monitoring operation information and the historical calibration standard information; generating a pipeline state evaluation value according to the state comparison fusion matrix; and determining the operation state evaluation result of the booster station according to the pipeline state evaluation value.

Description

Technical Field

[0001] The present application relates to the field of pipeline monitoring technology, and in particular to a method, device, equipment and medium for evaluating the operating status of a booster station. Background Art

[0002] Long-distance pipelines enable large-scale, long-distance transport of energy (such as fluids like chemicals, oil, and natural gas). Due to their longer transport distances, higher capacity, and more complex engineering requirements, operational monitoring of these pipelines is crucial. Booster stations, at the heart of these pipelines, require even more rigorous and detailed monitoring.

[0003] However, existing technologies rely on empirical rules or fixed sensor layouts when selecting monitoring points for booster station pipelines, and usually focus on real-time data (basic parameters such as pipeline pressure and flow obtained in real time). This makes it impossible to accurately and effectively assess the operating status of the booster station. Summary of the Invention

[0004] The embodiments of the present disclosure provide a method, apparatus, device, and medium for evaluating the operating status of a boosting station, aiming to solve the problem of how to accurately and effectively evaluate the operating status of a boosting station.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a method for evaluating the operating status of a booster station is provided, comprising: determining a pipeline status monitoring point set in the booster station, and setting a pipeline status monitoring area based on the pipeline status monitoring point set; obtaining real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area, and generating a state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information; generating a pipeline status evaluation value based on the state comparison fusion matrix; and determining a booster station operating status evaluation result based on the pipeline status evaluation value.

[0007] In some embodiments, the historical calibration standard information includes historical maintenance records of the booster station; a state comparison fusion matrix is ​​generated based on the real-time monitoring operation information and the historical calibration standard information, including: generating a historical operation parameter vector based on the historical calibration standard information, and generating a multidimensional operation parameter vector based on the real-time monitoring operation information; generating a nonlinear mapping matrix based on the multidimensional operation parameter vector; determining weighted state monitoring points from the pipeline state monitoring point set based on the historical maintenance records, and setting a diagonal weighted matrix based on the weighted state monitoring points; generating a state comparison fusion matrix based on the nonlinear mapping matrix, the diagonal weighted matrix, the multidimensional operation parameter vector and the historical operation parameter vector.

[0008] In some embodiments, the diagonal weighting matrix satisfies the following formula:

[0009]

[0010] Where W1 is the diagonal weight matrix; κ 1,1 is the weight corresponding to the first weighted state monitoring point; κ 1,2 is the weight corresponding to the second weighted state monitoring point; κ 1,n is the weight corresponding to the nth weighted state monitoring point; the nonlinear mapping matrix satisfies the following formula:

[0011]

[0012] Among them, y r (t) is the multidimensional operating parameter vector corresponding to time t in the real-time monitoring period; y s (t) is the historical operating parameter vector corresponding to time t in the historical monitoring period; Ψ(y r (t),y s (t)) is the nonlinear mapping matrix; Kur(y r (t)) is the kurtosis of the multidimensional operating parameter vector corresponding to time t; Ske(y r (t)) is the skewness of the multidimensional operating parameter vector corresponding to time t; Kur(y s (t)) is the kurtosis of the historical operating parameter vector corresponding to time t; Ske(y s (t)) is the skewness of the historical operating parameter vector corresponding to time t; the length of the real-time monitoring period and the historical monitoring period are the same, and the monitoring frequency within the real-time monitoring period and the historical monitoring period are also the same; the multidimensional operating parameter vector corresponding to time t in the real-time monitoring period and the historical operating parameter vector corresponding to time t in the historical monitoring period are in a corresponding relationship; the state comparison fusion matrix satisfies the following formula:

[0013] Y(t)=W1·(y r (t)-y s (t))+W2·Ψ(y r (t),y s (t));

[0014] Among them, Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; W2 is the mapping association matrix.

[0015] In some embodiments, the pipeline status evaluation value satisfies the following formula:

[0016]

[0017] Among them, Φ(t) is the pipeline status evaluation value corresponding to time t in the real-time monitoring period; H is the weighted matrix operator; Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; Δt is the time window length; M is the mapping matrix operator; Y(τ) is the state comparison fusion matrix corresponding to time τ in the real-time monitoring period.

[0018] In some embodiments, the booster station operation status assessment result is determined based on the pipeline status assessment value, including: when the pipeline status assessment value is greater than or equal to a preset pipeline status assessment threshold, counting the actual duration of the pipeline status assessment value being greater than or equal to the pipeline status assessment threshold; when the actual duration is greater than or equal to the preset duration threshold, determining that the booster station operation status assessment result is abnormal, and issuing a booster station status warning.

[0019] In some embodiments, determining a pipeline status monitoring point set in a boosting station includes: obtaining a pipeline circulation model, a maximum number of monitoring points, and a minimum monitoring distance of the boosting station; setting a potential monitoring point set according to the pipeline circulation model; the potential monitoring point set includes multiple potential feasible monitoring points; the potential feasible monitoring points correspond to a set of spatial coordinates; setting a key monitoring point set according to the pipeline circulation model; the key monitoring point set includes multiple key pending monitoring points; the key pending monitoring points correspond to a set of spatial coordinates; and setting the pipeline status monitoring point set based on the monitoring point coverage model according to the potential monitoring point set, the key monitoring point set, the maximum number of monitoring points, and the minimum monitoring distance.

[0020] In some embodiments, the pipeline status monitoring point set is set based on the monitoring point coverage model according to the potential monitoring point set, the key monitoring point set, the maximum number of monitoring points, and the minimum monitoring distance, including: the monitoring point coverage model is the following formula:

[0021]

[0022] Among them, A is the feasibility indicator set corresponding to the subset of potential monitoring points, A={a1,a2,…,a i ,…};a i is the feasibility indicator corresponding to the i-th potentially feasible monitoring point, a i The value is 0 or 1; C(A) is the monitoring point coverage assessment value corresponding to the subset of the potential monitoring point set; j is the jth key monitoring point to be determined; K is the number of key monitoring points to be determined; i:a i =1 is The index of a i The i-th potential feasible monitoring point when the value of is 1; ω j is the weight corresponding to the jth key monitoring point to be determined; α is the distance attenuation coefficient; X i is the spatial coordinate corresponding to the i-th potential feasible monitoring point; Yj is the spatial coordinate corresponding to the jth key undetermined monitoring point, d(X i ,Y j ) is the distance between the spatial coordinates corresponding to the i-th potential feasible monitoring point and the spatial coordinates corresponding to the j-th key pending monitoring point; the feasibility indicator sets corresponding to different subsets of potential monitoring point sets are input into the monitoring point coverage model to obtain the monitoring point coverage evaluation value corresponding to the subset of the potential monitoring point set; the number of potential feasible monitoring points in the subset of the potential monitoring point set meets the maximum number of monitoring points; the distance between the potential feasible monitoring points in the subset of the potential monitoring point set meets the minimum monitoring distance; when the monitoring point coverage evaluation value corresponding to the subset of the potential monitoring point set is the largest, the subset of the potential monitoring point set is determined to be the pipeline status monitoring point set.

[0023] In a second aspect, a booster station operation status assessment device is provided, the booster station operation status assessment device comprising: a pipeline state monitoring area setting module, a state comparison fusion matrix generation module, a pipeline state assessment value generation module, and a booster station operation status assessment result generation module;

[0024] The pipeline status monitoring area setting module is used to determine the pipeline status monitoring point set in the booster station and set the pipeline status monitoring area according to the pipeline status monitoring point set.

[0025] The state comparison fusion matrix generation module is used to obtain the real-time monitoring operation information and historical calibration standard information of the pipeline state monitoring area, and generate the state comparison fusion matrix based on the real-time monitoring operation information and historical calibration standard information.

[0026] The pipeline state evaluation value generation module is used to generate a pipeline state evaluation value according to the state comparison fusion matrix.

[0027] The booster station operation status assessment result generation module is used to determine the booster station operation status assessment result based on the pipeline status assessment value.

[0028] In a third aspect, a booster station operation status assessment device is provided, comprising a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory via a bus; when the booster station operation status assessment device is running, the processor executes the computer execution instructions stored in the memory, so that the booster station operation status assessment device performs the booster station operation status assessment method of the first aspect.

[0029] The boosting station operating status assessment device can be an electronic device or a device within an electronic device, such as a chip system within the electronic device. The chip system is used to support the electronic device in implementing the functions involved in the first aspect and any possible implementation thereof, such as acquiring and determining the data and / or information involved in the above-mentioned boosting station operating status assessment method. The chip system includes a chip and may also include other discrete devices or circuit structures.

[0030] In a fourth aspect, a computer-readable storage medium is provided, the computer-readable storage medium including computer execution instructions, which, when executed on a computer, enable the computer to execute the method for evaluating the operating status of a boosting station described in the first aspect.

[0031] In a fifth aspect, a computer program product is also provided, which includes a computer program or instructions. When the computer instructions are run on the booster station operation status evaluation device, the booster station operation status evaluation device executes the booster station operation status evaluation method described in the first aspect above.

[0032] It should be noted that the aforementioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the boosting station operation status assessment device, or may be packaged separately from the processor of the boosting station operation status assessment device, and this is not limited in the present embodiment.

[0033] The description of the second, third, fourth and fifth aspects of this application can refer to the detailed description of the first aspect.

[0034] In the embodiments of this application, the name of the booster station operating status assessment device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. For example, the receiving unit may also be called a receiving module, a receiver, etc. As long as the functions of each device or functional module are similar to those of this application, they are within the scope of the claims of this application and their equivalents.

[0035] This application provides a method for evaluating the operating status of a booster station. This method can determine a set of pipeline status monitoring points in the booster station and set a pipeline status monitoring area based on the set of pipeline status monitoring points. Subsequently, real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area can be obtained, and a state comparison fusion matrix can be generated based on the real-time monitoring operation information and historical calibration standard information. Furthermore, a pipeline status evaluation value can be generated based on the state comparison fusion matrix. Subsequently, the booster station operating status evaluation result can be determined based on the pipeline status evaluation value.

[0036] As can be seen from the above, this application sets up pipeline status monitoring areas by determining a pipeline status monitoring point set, which can accurately obtain the operating status of key locations inside the pipeline (i.e., the locations monitored by the pipeline status monitoring point set), thereby ensuring that real-time monitoring operation information comes from key locations, thereby improving the pertinence and accuracy of monitoring. The pipeline status monitoring area is not limited to a fixed area, but can also be flexibly adjusted according to the pipeline status monitoring points to ensure that the operating status of each area of ​​the pipeline can be monitored, thereby reducing unnecessary pipeline status monitoring points and achieving the effect of reducing operating costs.

[0037] This application also obtains real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area, and generates a state comparison fusion matrix based on the real-time monitoring operation information and historical calibration standard information. The current operating status of the pipeline can be obtained in real time, and potential anomalies can be identified, thereby dynamically reflecting possible changes or anomalies in the pipeline. Historical calibration standard information can effectively identify long-term accumulated systemic problems and avoid misjudgments or omissions caused by relying solely on real-time monitoring operation information. The state comparison fusion matrix can combine real-time monitoring data and historical calibration data, comprehensively considering the characteristics of both, to obtain a more accurate pipeline status assessment value.

[0038] This application also determines the booster station operating status assessment results based on the pipeline status assessment values ​​generated by the status comparison fusion matrix. By setting reasonable pipeline status assessment thresholds, potential pipeline failures can be quickly identified, thus avoiding downtime losses and repair costs caused by potential failures developing into serious failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of the structure of a booster station operating status assessment system provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the hardware structure of a booster station operating status assessment device provided in an embodiment of the present application;

[0041] Figure 3 A flow chart of a method for evaluating the operating status of a booster station provided in an embodiment of the present application;

[0042] Figure 4 A schematic structural diagram of a booster station operating status assessment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0044] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0045] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0046] As described in the background, long-distance pipelines enable large-scale, long-distance transport of energy (e.g., fluid media such as chemicals, petroleum, and natural gas). Due to their longer transport distances, higher capacity, and more complex engineering requirements, operational monitoring of these pipelines is crucial. Booster stations, at the heart of these pipelines, require even more rigorous and detailed monitoring.

[0047] Conventional technology often relies on empirical rules or fixed sensor layouts to select monitoring points along booster station pipelines. This approach fails to fully account for the differences in actual operating conditions across the pipeline and the complexity of the pipeline layout. This can lead to overlooking monitoring data from critical locations, preventing timely detection of potential equipment failures or leaks.

[0048] Common technologies typically rely on real-time data to obtain basic parameters such as pipeline pressure and flow, but rarely use historical data as a basis for operational status assessment. Because booster stations operate over long periods of time, pipeline conditions change over time. Real-time data alone often fails to fully capture the pipeline's full picture, especially before a sudden failure occurs, often leading to high repair costs due to downtime.

[0049] From the above, we can see that the general technology has problems such as unreasonable setting of monitoring points and rarely uses historical data of booster stations as the basis for operating status evaluation.

[0050] To address the above issues, an embodiment of the present application provides a method for evaluating the operating status of a booster station. This method can determine a set of pipeline status monitoring points in the booster station and set a pipeline status monitoring area based on the set of pipeline status monitoring points. Next, real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area can be obtained, and a state comparison fusion matrix can be generated based on the real-time monitoring operation information and historical calibration standard information. A pipeline status evaluation value can then be generated based on the state comparison fusion matrix. Subsequently, the booster station operating status evaluation result can be determined based on the pipeline status evaluation value.

[0051] As can be seen from the above, this application sets up pipeline status monitoring areas by determining a pipeline status monitoring point set, which can accurately obtain the operating status of key locations inside the pipeline (i.e., the locations monitored by the pipeline status monitoring point set), thereby ensuring that real-time monitoring operation information comes from key locations, thereby improving the pertinence and accuracy of monitoring. The pipeline status monitoring area is not limited to a fixed area, but can also be flexibly adjusted according to the pipeline status monitoring points to ensure that the operating status of each area of ​​the pipeline can be monitored, thereby reducing unnecessary pipeline status monitoring points and achieving the effect of reducing operating costs.

[0052] This application also obtains real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area, and generates a state comparison fusion matrix based on the real-time monitoring operation information and historical calibration standard information. The current operating status of the pipeline can be obtained in real time, and potential anomalies can be identified, thereby dynamically reflecting possible changes or anomalies in the pipeline. Historical calibration standard information can effectively identify long-term accumulated systemic problems and avoid misjudgments or omissions caused by relying solely on real-time monitoring operation information. The state comparison fusion matrix can combine real-time monitoring data and historical calibration data, comprehensively considering the characteristics of both, to obtain a more accurate pipeline status assessment value.

[0053] This application also determines the booster station operating status assessment results based on the pipeline status assessment values ​​generated by the status comparison fusion matrix. By setting reasonable pipeline status assessment thresholds, potential pipeline failures can be quickly identified, thus avoiding downtime losses and repair costs caused by potential failures developing into serious failures.

[0054] The implementation environment of the above-mentioned booster station operating status assessment method can be the booster station operating status assessment system provided in the embodiment of the present application.

[0055] Figure 1 This is a schematic diagram of the structure of a booster station operating status evaluation system provided in an embodiment of the present application. Figure 1 As shown, the boosting station operation status evaluation system includes: a boosting station operation status evaluation device 101, a data storage device 102, a real-time monitoring operation information acquisition device 103 and a boosting station 104.

[0056] Among them, the boosting station operation status evaluation device 101 and the data storage device 102 are communicated with each other, and the boosting station operation status evaluation device 101 and the real-time monitoring operation information acquisition device 103 are communicated with each other, and the real-time monitoring operation information acquisition device 103 obtains real-time monitoring operation information from the facilities (such as pipelines, etc.) in the boosting station 104.

[0057] In practical applications, the boosting station operation status evaluation device 101 can be connected to any number of data storage devices 102, and the boosting station operation status evaluation device 101 can be connected to any number of real-time monitoring operation information acquisition devices 103. For ease of understanding, Figure 1 An example is given in which a boosting station operation status evaluation device 101 is connected to a data storage device 102 , and a boosting station operation status evaluation device 101 is connected to a real-time monitoring operation information acquisition device 103 .

[0058] In an embodiment of the present application, the data storage device 102 is used to provide data for boosting station operation status evaluation (for example, historical calibration standard information, etc.) to the boosting station operation status evaluation device 101, and the real-time monitoring operation information acquisition device 103 is used to obtain real-time monitoring operation information from the boosting station 104, and provide the real-time monitoring operation information to the boosting station operation status evaluation device 101, so that the boosting station operation status evaluation device 101 can realize the boosting station operation status evaluation based on the data storage device 102 and the data sent by the real-time monitoring operation information acquisition device 103.

[0059] Optionally, the physical devices of the boosting station operation status evaluation device 101 and the data storage device 102 may be servers, terminals, or other types of electronic devices, which is not limited in the embodiments of the present application.

[0060] Optionally, the terminal may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection capability, or other processing device connected to a wireless modem. A wireless terminal may communicate with one or more core networks via a radio access network (RAN). A wireless terminal may be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal, or a portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile device that exchanges voice and / or data with a radio access network, such as a mobile phone, tablet computer, laptop computer, netbook, or personal digital assistant (PDA).

[0061] Optionally, the above-mentioned server can be a server in a server cluster (consisting of multiple servers), or a chip in the server, or a system on a chip in the server, or can be implemented through a virtual machine (VM) deployed on a physical machine. This embodiment of the present application does not limit this.

[0062] Optionally, the real-time monitoring operation information acquisition device 103 may be a sensor or a related dedicated monitoring instrument.

[0063] Optionally, the boosting station operation status evaluation device 101 and the data storage device 102 may be two independent devices, or may be integrated into the same device. When the boosting station operation status evaluation device 101 and the data storage device 102 are integrated into the same device, the data storage device 102 may be a storage module (e.g., a database, etc.) of the boosting station operation status evaluation device 101.

[0064] It is easy to understand that when the boosting station operation status assessment device 101 and the data storage device 102 are integrated into the same device, the communication method between the boosting station operation status assessment device 101 and the data storage device 102 is communication between the internal modules of the device. In this case, the communication process between the two is the same as the communication process between the boosting station operation status assessment device 101 and the data storage device 102 when they are independent of each other.

[0065] For ease of understanding, this application is described by taking the example that the boosting station operation status evaluation device 101 and the data storage device 102 are independent of each other.

[0066] The booster station operation status assessment equipment in the booster station operation status assessment system includes: Figure 2 The following are the components included. Figure 2 Taking the boosting station operation status evaluation device shown in FIG. 1 as an example, the hardware structure of the boosting station operation status evaluation device is introduced.

[0067] Figure 2 This is a hardware structure diagram of a booster station operating status evaluation device provided in an embodiment of the present application. Figure 2 As shown, the boosting station operation status assessment device includes: a processor 201, a memory 202, a communication interface 203, and a bus 204. The processor 201, the memory 202, and the communication interface 203 can be connected via the bus 204.

[0068] The processor 201 is the control center of the booster station operation status assessment device and can be a single processor or a collective term for multiple processing elements. For example, the processor 201 can be a general-purpose central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0069] As an embodiment, the processor 201 may include one or more CPUs, such as Figure 2 CPU0 and CPU1 are shown in the figure.

[0070] The memory 202 may 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 can be accessed by a computer, but is not limited thereto.

[0071] The memory 202 may also be an internal storage unit, such as a hard disk or memory. Alternatively, it may be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 202 may include both an internal storage unit and an external storage device. The memory 202 may be used to store an operating system, application programs, a boot loader, data, and other programs. The memory 202 may also be used to temporarily store data that has been output or is about to be output.

[0072] In one possible implementation, memory 202 may exist independently of processor 201 and may be connected to processor 201 via bus 204 for storing instructions or program code. When processor 201 calls and executes the instructions or program code stored in memory 202, the boosting station operating status assessment method provided in the following embodiments of this application can be implemented.

[0073] In another possible implementation, the memory 202 may also be integrated with the processor 201 .

[0074] The communication interface 203 is used to connect the boosting station operation status assessment device to other devices via a communication network, which can be Ethernet, wireless access network, wireless local area network (WLAN), etc. The communication interface 203 can include a receiving unit for receiving data and a sending unit for sending data.

[0075] The bus 204 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0076] It should be pointed out that Figure 2 The structure shown in the figure does not constitute a limitation on the booster station operation status evaluation device. Figure 2 In addition to the components shown, the boosting station operating status assessment device may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0077] The following is a detailed introduction to the booster station operating status assessment method provided in the embodiment of the present application with reference to the accompanying drawings.

[0078] The booster station operating status assessment method provided in the embodiment of the present application is applied to Figure 1 The booster station operation status evaluation device 101 in the booster station operation status evaluation system shown in FIG. Figure 3 As shown, a method for evaluating the operating status of a booster station provided in an embodiment of the present application includes:

[0079] S301. The boosting station operation status assessment device determines a pipeline status monitoring point set in the boosting station, and sets a pipeline status monitoring area according to the pipeline status monitoring point set.

[0080] Specifically, in order to accurately locate the operating status of key pipeline status monitoring points within the pipeline and flexibly configure pipeline status monitoring areas based on demand, thereby improving the pertinence and accuracy of monitoring, it is necessary to determine the pipeline status monitoring point set and set the pipeline status monitoring areas based on the pipeline status monitoring point set.

[0081] Optionally, the pipeline status monitoring point can be monitored by setting a sensor or a sensor group consisting of multiple sensors.

[0082] For example, the sensor may be an acoustic sensor, a pressure sensor, a flow rate sensor, or the like.

[0083] Specifically, the method for determining the pipeline status monitoring point set is described in detail in the following embodiments and will not be elaborated here.

[0084] Specifically, each pipeline status monitoring point corresponds to a region, and each pipeline status monitoring point is used to divide the region.

[0085] S302. The booster station operation status assessment device obtains the real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area, and generates a status comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information.

[0086] Specifically, in order to combine the real-time monitoring operation information and the historical calibration standard information, comprehensively consider the characteristics of both, and thus form a more accurate evaluation standard, it is necessary to generate a state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information, and use the state comparison fusion matrix as the comprehensive evaluation value.

[0087] Specifically, the historical calibration standard information is theoretical information, which is obtained by fitting the working condition information of the booster station under normal operation and historical monitoring operation information (i.e., monitoring operation information at historical time). Real-time monitoring operation information is obtained by monitoring the pipeline status monitoring points.

[0088] Alternatively, historical calibration standard information can also be generated by numerical simulation of the fourth-order coupled acoustic-fluid dynamics equations.

[0089] Specifically, the specific method of generating the state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information is described in detail in the embodiments below and will not be repeated here.

[0090] It is understandable that if the booster station is in a normal state, the real-time monitoring operation information should be very close to the historical calibration standard information. Therefore, by using the preset historical calibration standard information for subsequent comparison with the real-time monitoring operation information, an accurate assessment result of the booster station operation status can be obtained.

[0091] S303: The booster station operation status assessment device generates a pipeline status assessment value based on the status comparison fusion matrix.

[0092] Specifically, in order to comprehensively evaluate the pipeline and thus determine the evaluation results of the booster station operation status, it is necessary to generate the pipeline status evaluation value based on the status comparison fusion matrix.

[0093] Optionally, a pipeline condition assessment value can be generated by coupling the deviation benchmark model.

[0094] Specifically, the specific method of generating the pipeline state evaluation value according to the state comparison fusion matrix is ​​described in detail in the following embodiments and will not be repeated here.

[0095] S304: The booster station operation status assessment device determines a booster station operation status assessment result according to the pipeline status assessment value.

[0096] Specifically, in order to be able to warn of potential failures and make effective predictions before they occur, it is necessary to determine the booster station operation status assessment results based on the pipeline status assessment values.

[0097] Specifically, the specific method for determining the boosting station operation status evaluation result according to the pipeline status evaluation value is described in detail in the embodiments below and will not be repeated here.

[0098] In some embodiments, in the above S302, the historical calibration standard information includes historical maintenance records of the booster station; generating a state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information specifically includes:

[0099] The booster station operation status assessment device generates a historical operation parameter vector based on historical calibration standard information, and generates a multi-dimensional operation parameter vector based on real-time monitoring operation information.

[0100] Specifically, in order to integrate multi-dimensional real-time monitoring operation information and historical calibration standard information and standardize the above information, it is necessary to generate a historical operation parameter vector based on the historical calibration standard information and generate a multi-dimensional operation parameter vector based on the real-time monitoring operation information.

[0101] Optionally, real-time monitoring operation information or historical calibration standard information may include acoustic information, flow rate information, temperature information and pressure information, etc., but real-time monitoring operation information and historical calibration standard information not only include the above information, but may also include monitoring information of more dimensions, which is not limited here.

[0102] Specifically, each dimension of the multi-dimensional operating parameter vector and the historical operating parameter vector contains the same information and is in one-to-one correspondence.

[0103] For example, a multidimensional operating parameter vector or a historical operating parameter vector can be [45 3 56 6], indicating an acoustic level of 45 decibels, a flow rate of 3 meters per second, a temperature of 56 degrees Celsius, and a pressure of 6 MPa. The above example data is not actual monitored data and is provided for reference only.

[0104] The booster station operation status assessment device generates a nonlinear mapping matrix according to the multi-dimensional operation parameter vector.

[0105] In some embodiments, the nonlinear mapping matrix satisfies the following formula:

[0106]

[0107] Among them, y r (t) is the multidimensional operating parameter vector corresponding to time t in the real-time monitoring period; y s (t) is the historical operating parameter vector corresponding to time t in the historical monitoring period; Ψ(y r (t),y s (t)) is the nonlinear mapping matrix; Kur(y r (t)) is the kurtosis of the multidimensional operating parameter vector corresponding to time t; Ske(y r (t)) is the skewness of the multidimensional operating parameter vector corresponding to time t; Kur(y s (t)) is the kurtosis of the historical operating parameter vector corresponding to time t; Ske(y s (t)) is the skewness of the historical operating parameter vector corresponding to time t; the length of the real-time monitoring period and the historical monitoring period are the same, and the monitoring frequency within the real-time monitoring period and the historical monitoring period are also the same; the multidimensional operating parameter vector corresponding to time t within the real-time monitoring period and the historical operating parameter vector corresponding to time t within the historical monitoring period are in a corresponding relationship.

[0108] Specifically, the kurtosis and skewness of the multidimensional operating parameter vector and the historical operating parameter vector are calculated, specifically by generating corresponding kurtosis and skewness for the information therein (such as acoustic information, pressure information, flow rate information, etc.).

[0109] Specifically, the historical monitoring period and the real-time monitoring period are the same, and the monitoring frequency within the monitoring period is also the same, thereby ensuring that the historical calibration standard information within the historical monitoring period and the real-time monitoring operation information within the real-time monitoring period are corresponding.

[0110] For example, the historical monitoring cycle is one day, and the data corresponding to different sampling moments (i.e., monitoring frequencies) in one day are historical calibration standard information. The real-time monitoring cycle is also one day, and the same monitoring frequency is maintained when obtaining real-time monitoring operation information.

[0111] Specifically, the nonlinear mapping matrix is ​​used to extract the nonlinear characteristics of kurtosis and skewness between real-time monitoring information and historical calibration standard information. By combining the differences between real-time monitoring information and historical calibration standard information into a single value, it is possible to capture complex nonlinear characteristics (such as nonlinear noise and system dynamics).

[0112] Optionally, the nonlinear mapping matrix can also be the following formula:

[0113] Ψ(y r (t),y s (t))=|y r (t)-y s (t)| η ;

[0114] Where η is a constant.

[0115] It can be understood that based on this form, the impact of the difference between real-time monitoring operation information and historical calibration standard information can be enhanced.

[0116] Optionally, the nonlinear mapping matrix can also be the following formula:

[0117]

[0118] It can be understood that based on this form, it is possible to avoid excessive errors caused by changes in the difference between real-time monitoring operation information and historical calibration standard information.

[0119] Optionally, the present application does not make any specific limitation on the nonlinear mapping matrix, and the actual selection is made by those skilled in the art according to actual needs.

[0120] The booster station operation status assessment device determines weighted status monitoring points from the pipeline status monitoring points according to historical maintenance records, and sets a diagonal weighted matrix according to the weighted status monitoring points.

[0121] In some embodiments, the diagonal weighting matrix satisfies the following formula:

[0122]

[0123] Where W1 is the diagonal weight matrix; κ 1,1 is the weight corresponding to the first weighted state monitoring point; κ 1,2 is the weight corresponding to the second weighted state monitoring point; κ 1,n is the weight corresponding to the nth weighted status monitoring point.

[0124] Specifically, the diagonal weighting matrix can adjust the influence of the weighted state monitoring points.

[0125] Optionally, the weighted condition monitoring points may be monitoring points that require special attention after maintenance.

[0126] For example, the diagonal weighting matrix may be a 3×3 matrix:

[0127]

[0128] The booster station operation status assessment device generates a state comparison fusion matrix based on a nonlinear mapping matrix, a diagonal weighted matrix, a multidimensional operation parameter vector and a historical operation parameter vector.

[0129] In some embodiments, the state contrast fusion matrix satisfies the following formula:

[0130] Y(t)=W1·(y r (t)-y s (t))+W2·Ψ(y r (t),y s (t));

[0131] Among them, Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; W2 is the mapping association matrix.

[0132] Specifically, in order to generate pipeline status evaluation values ​​and thus determine the operating status results of the booster station, it is necessary to generate a state comparison fusion matrix based on the nonlinear mapping matrix, the diagonal weighted matrix, the multidimensional operating parameter vector and the historical operating parameter vector.

[0133] Among them, the mapping correlation matrix is ​​used to adjust the influence of the results after nonlinear mapping on the coupling deviation benchmark model, and matches the dimension of the nonlinear mapping matrix.

[0134] It can be understood that by capturing nonlinear characteristics through the nonlinear mapping matrix and combining it with the mapping correlation matrix to further adjust the influence of different nonlinear characteristics on the state comparison fusion matrix, the problem of inaccurate operating status results of the booster station when monitoring complex and nonlinear problems is effectively solved.

[0135] That is to say, the process of the embodiment of the present application can be: generating a multidimensional operation parameter vector based on real-time monitoring operation information. Generating a historical operation parameter vector based on historical calibration standard information. Generating the kurtosis corresponding to the multidimensional operation parameter vector and the skewness corresponding to the multidimensional operation parameter vector based on real-time monitoring operation information. Generating a nonlinear mapping matrix based on the kurtosis corresponding to the multidimensional operation parameter vector and the skewness corresponding to the multidimensional operation parameter vector and the kurtosis corresponding to the historical operation parameter vector in the historical calibration standard information and the skewness corresponding to the historical operation parameter vector. Extracting historical maintenance records based on the historical calibration standard information, and setting weighted status monitoring points from the pipeline status monitoring points based on the historical maintenance records. Setting a diagonal weighting matrix based on the weighted status monitoring points. Generating a state comparison fusion matrix based on the nonlinear mapping matrix, the diagonal weighting matrix, the multidimensional operation parameter vector and the historical operation parameter vector.

[0136] In some embodiments, the pipeline status evaluation value satisfies the following formula:

[0137]

[0138] Among them, Φ(t) is the pipeline status evaluation value corresponding to time t in the real-time monitoring period; H is the weighted matrix operator; Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; Δt is the time window length; M is the mapping matrix operator; Y(τ) is the state comparison fusion matrix corresponding to time τ in the real-time monitoring period.

[0139] Specifically, the weighting matrix operator and the mapping matrix operator are preset by relevant staff members, so as to weight the information that needs to be focused on (ie, the real-time monitoring operation information and the historical calibration standard information).

[0140] It can be understood that by further linearly transforming Y(t) through the weighted matrix operator, the information of each pipeline status monitoring point corresponding to the current moment and the instantaneous intensity of the nonlinear characteristics can be reflected.

[0141] It can be understood that through the calculation of the second norm, the information of multiple pipeline status monitoring points can be integrated into a scalar, and it can be more sensitive to information with large changes, effectively improving the accuracy.

[0142] It can be understood that by calculating the second-order derivative, it is possible to effectively analyze the sudden high-frequency or large-scale changes in information associated with abnormal conditions such as pipeline leakage.

[0143] It is understandable that, through integral calculation, the continuous abnormality information and the short-term abnormality information can be accumulated, thereby accurately identifying the authenticity of the abnormality.

[0144] For example, if Y(t) is a P×P matrix, the weighting matrix operator is a Q×P matrix.

[0145] Optionally, the mapping matrix operator may also be a diagonal weighted matrix. The weighted matrix operator and the mapping matrix operator may be set in various forms according to actual conditions. This application is only an example and does not make specific limitations.

[0146] It can be understood that this application realizes the combination of real-time monitoring operation information and historical calibration standard information of pipeline status monitoring points with nonlinear characteristics through the setting of state comparison fusion matrix and coupling deviation benchmark model, and accumulates short-term anomalies to generate a comprehensive indicator (i.e. pipeline status evaluation value) that can capture short-term anomalies and measure current deviations (i.e. the difference between real-time monitoring operation information and historical calibration standard information), thereby more accurately and comprehensively determining the evaluation results of the booster station operation status.

[0147] In some embodiments, in the above S304, determining the boosting station operation status assessment result according to the pipeline status assessment value specifically includes:

[0148] When the pipeline status evaluation value is greater than or equal to a preset pipeline status evaluation threshold, the booster station operation status evaluation device counts the actual duration during which the pipeline status evaluation value is greater than or equal to the pipeline status evaluation threshold.

[0149] Specifically, in order to improve the accuracy of judgment and avoid evaluation errors caused by interference, when it is judged that the pipeline status evaluation value is greater than or equal to the preset pipeline status evaluation threshold, the booster station status warning is not directly generated. Instead, the actual duration of the pipeline status evaluation value being greater than or equal to the preset pipeline status evaluation threshold is first counted.

[0150] Optionally, the preset pipeline status assessment threshold may be set by relevant staff based on experience, or may be set based on historical calibration standard information.

[0151] When the actual duration is greater than or equal to the preset duration threshold, the booster station operation status assessment device determines that the booster station operation status assessment result is abnormal and issues a booster station status warning.

[0152] Specifically, in order to determine whether to generate a boosting station status warning, it is necessary to determine whether the actual duration is greater than or equal to a preset duration threshold.

[0153] Optionally, the preset duration threshold may be set by relevant staff based on experience, or may be set based on historical calibration standard information.

[0154] Exemplarily, the preset duration threshold may be set to 5 minutes.

[0155] In other words, the process of the embodiment of the present application may be as follows: determining whether the pipeline status assessment value is greater than or equal to a preset pipeline status assessment threshold. If so, counting the actual duration of time the pipeline status assessment value is greater than or equal to the preset pipeline status assessment threshold. Determining whether the actual duration is greater than or equal to the preset duration threshold. If not, generating a normal status instruction. If so, generating a booster station status warning instruction.

[0156] In some embodiments, in the above S301, determining the pipeline status monitoring point set in the boosting station specifically includes:

[0157] The booster station operation status assessment device obtains the pipeline flow model, the maximum number of monitoring points and the minimum monitoring distance of the booster station.

[0158] Specifically, in order to set the potential monitoring point set and the key monitoring point set, and thus determine the pipeline status monitoring point set, it is necessary to first obtain the pipeline flow model of the booster station, the maximum number of monitoring points, and the minimum monitoring distance.

[0159] Optionally, the pipeline circulation model can be a three-dimensional model set up based on Building Information Modeling (BIM) technology, or a three-dimensional model drawn by relevant staff themselves, or a two-dimensional model.

[0160] It can be understood that the pipeline status monitoring points can be quickly analyzed through the pipeline circulation model.

[0161] Specifically, the maximum number of monitoring points and the minimum monitoring distance are intended to constrain the pipeline status monitoring point set.

[0162] Optionally, the maximum number of monitoring points may be set based on actual budget costs or actual equipment management capabilities.

[0163] For example, the load of device management cannot be 15 sensors.

[0164] Optionally, the minimum monitoring distance may be set by a distance between monitoring points that does not cause interference, or may be set to avoid setting multiple monitoring points in the same area.

[0165] The booster station operation status assessment equipment sets a set of potential monitoring points based on the pipeline flow model.

[0166] The potential monitoring point set includes multiple potential feasible monitoring points, and the potential feasible monitoring points correspond to a set of spatial coordinates.

[0167] Specifically, in order to set the pipeline status monitoring point set, it is necessary to first set the potential monitoring point set.

[0168] Optionally, when setting up a set of potential monitoring points, you can first select candidate potential feasible monitoring points at a certain distance along the pipeline route or according to the distribution of various equipment in the booster station, and then filter out candidate potential feasible monitoring points where sensors cannot be installed due to specific factors (such as insufficient safety distance, insufficient space, or interference between equipment, etc.). Finally, the set of remaining candidate potential feasible monitoring points is the potential monitoring point set.

[0169] The booster station operation status assessment equipment sets a set of key monitoring points based on the pipeline flow model.

[0170] The key monitoring point set includes a plurality of key pending monitoring points, and the key pending monitoring points correspond to a set of spatial coordinates.

[0171] Specifically, in order to set the pipeline status monitoring point set, it is necessary to first set the key monitoring point set.

[0172] Optionally, when setting up a key monitoring point set, you can list the parts that need to be monitored (such as valves, flanges, elbows, welds or shaft seal positions, etc.), and then set the parts that need to be monitored as key pending monitoring points to determine the key monitoring point set.

[0173] The booster station operation status assessment equipment sets the pipeline status monitoring point set based on the monitoring point coverage model according to the potential monitoring point set, key monitoring point set, maximum number of monitoring points and minimum monitoring distance.

[0174] Specifically, in order to accurately determine the pipeline status monitoring area and thus realize monitoring of the pipeline status monitoring area, it is necessary to set the pipeline status monitoring point set based on the monitoring point coverage model according to the potential monitoring point set, key monitoring point set, maximum number of monitoring points and minimum monitoring distance.

[0175] In some embodiments, the pipeline status monitoring point set is set based on the monitoring point coverage model according to the potential monitoring point set, the key monitoring point set, the maximum number of monitoring points, and the minimum monitoring distance, specifically including:

[0176] The monitoring point coverage model is as follows:

[0177]

[0178] Among them, A is the feasibility indicator set corresponding to the subset of potential monitoring points, A={a1,a2,…,a i ,…};a i is the feasibility indicator corresponding to the i-th potentially feasible monitoring point, a i The value is 0 or 1; C(A) is the monitoring point coverage assessment value corresponding to the subset of the potential monitoring point set; j is the jth key monitoring point to be determined; K is the number of key monitoring points to be determined; i:a i =1 is The index of a i The i-th potential feasible monitoring point when the value of is 1; ω j is the weight corresponding to the jth key monitoring point to be determined; α is the distance attenuation coefficient; X i is the spatial coordinate corresponding to the i-th potential feasible monitoring point; Y j is the spatial coordinate corresponding to the jth key undetermined monitoring point, d(X i ,Y j ) is the distance between the spatial coordinates corresponding to the i-th potential feasible monitoring point and the spatial coordinates corresponding to the j-th key pending monitoring point.

[0179] Specifically, the weights corresponding to the key pending monitoring points are used to distinguish the importance of different key pending monitoring points.

[0180] Specifically, the distance attenuation coefficient can be used to adjust the rate at which the coverage of potential feasible monitoring points to key pending monitoring points decays as the distance increases.

[0181] Optionally, the distance attenuation coefficient can be pre-set by relevant staff and adjusted in a timely manner according to actual conditions.

[0182] It is understandable that if the distance attenuation coefficient is too large, a slight increase in distance will lead to a significant decrease in monitoring accuracy and coverage assessment value; if the distance attenuation coefficient is too small, the coverage assessment value of the sensor will decay too slowly.

[0183] As you can understand, using an exponential function to model distance (i.e., the distance between the spatial coordinates of the i-th potential monitoring point and the spatial coordinates of the j-th key pending monitoring point) effectively reflects the signal attenuation characteristic with distance and improves the accuracy of the monitoring point coverage model.

[0184] The booster station operation status assessment device inputs feasibility indication sets corresponding to different subsets of potential monitoring point sets into a monitoring point coverage model to obtain monitoring point coverage assessment values ​​corresponding to the subsets of potential monitoring point sets.

[0185] The number of potential feasible monitoring points in the subset of the potential monitoring point set satisfies the maximum number of monitoring points, and the distance between the potential feasible monitoring points in the subset of the potential monitoring point set satisfies the minimum monitoring distance.

[0186] Specifically, in order to determine the pipeline status monitoring point set according to the monitoring point coverage evaluation value, it is necessary to input the feasibility indication set corresponding to the subset of the potential monitoring point set into the monitoring point coverage model, thereby obtaining the corresponding monitoring point coverage evaluation value.

[0187] Specifically, the feasibility indication set represents different subsets of the potential monitoring point set. When the feasibility indication corresponding to the potential feasible monitoring point is 0, it means that the potential feasible monitoring point is not in the subset of the potential monitoring point set; when the feasibility indication corresponding to the potential feasible monitoring point is 1, it means that the potential feasible monitoring point is in the subset of the potential monitoring point set.

[0188] Optionally, the feasibility indicator set can be set by a preset algorithm, which will automatically iterate.

[0189] The booster station operation status assessment device determines that the subset of the potential monitoring point set is the pipeline status monitoring point set when the monitoring point coverage assessment value corresponding to the subset of the potential monitoring point set is the largest.

[0190] Specifically, in order to determine the coverage effect of the subset of potential monitoring point set on the key monitoring point set, the optimal subset of potential monitoring point set is determined. Therefore, it is necessary to determine the pipeline status monitoring point set according to the monitoring point coverage evaluation value corresponding to the subset of potential monitoring point set.

[0191] It can be understood that by comprehensively considering the settings of different monitoring points, it is possible to better cover the key monitoring point set while satisfying the constraints (i.e., the maximum number of monitoring points and the minimum monitoring distance), providing accurate information for the subsequent calculation of the state comparison fusion matrix.

[0192] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily appreciate that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0193] In the embodiment of the present application, the function modules of the boosting station operation status evaluation device can be divided according to the above method example. For example, each function module can be divided according to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software function modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0194] Figure 4FIG. 1 shows a schematic diagram of a booster station operating status evaluation device provided in an embodiment of the present application. Figure 4 As shown, the boosting station operation status assessment device includes: a pipeline status monitoring area setting module 401, a status comparison fusion matrix generation module 402, a pipeline status assessment value generation module 403 and a boosting station operation status assessment result generation module 404;

[0195] The pipeline status monitoring area setting module 401 is used to determine the pipeline status monitoring point set in the boosting station and set the pipeline status monitoring area according to the pipeline status monitoring point set.

[0196] The state comparison fusion matrix generation module 402 is used to obtain real-time monitoring operation information and historical calibration standard information of the pipeline state monitoring area, and generate a state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information.

[0197] The pipeline state evaluation value generating module 403 is configured to generate a pipeline state evaluation value according to the state comparison fusion matrix.

[0198] The boosting station operation status evaluation result generating module 404 is configured to determine the boosting station operation status evaluation result according to the pipeline status evaluation value.

[0199] In some embodiments, the historical calibration standard information includes historical maintenance records of the boosting station; the state comparison fusion matrix generation module 402 is specifically used to:

[0200] A historical operating parameter vector is generated based on historical calibration standard information, and a multi-dimensional operating parameter vector is generated based on real-time monitoring operation information.

[0201] Generate a nonlinear mapping matrix based on a multidimensional operating parameter vector.

[0202] According to historical maintenance records, weighted status monitoring points are determined from the pipeline status monitoring points, and a diagonal weighted matrix is ​​set according to the weighted status monitoring points.

[0203] A state comparison fusion matrix is ​​generated according to the nonlinear mapping matrix, the diagonal weighting matrix, the multidimensional operation parameter vector and the historical operation parameter vector.

[0204] In some embodiments, the diagonal weighting matrix satisfies the following formula:

[0205]

[0206] Where W1 is the diagonal weight matrix; κ 1,1 is the weight corresponding to the first weighted state monitoring point; κ 1,2 is the weight corresponding to the second weighted state monitoring point; κ 1,n is the weight corresponding to the nth weighted status monitoring point.

[0207] The nonlinear mapping matrix satisfies the following formula:

[0208]

[0209] Among them, y r (t) is the multidimensional operating parameter vector corresponding to time t in the real-time monitoring period; y s (t) is the historical operating parameter vector corresponding to time t in the historical monitoring period; Ψ(y r (t),y s (t)) is the nonlinear mapping matrix; Kur(y r (t)) is the kurtosis of the multidimensional operating parameter vector corresponding to time t; Ske(y r (t)) is the skewness of the multidimensional operating parameter vector corresponding to time t; Kur(y s (t)) is the kurtosis of the historical operating parameter vector corresponding to time t; Ske(y s (t)) is the skewness of the historical operating parameter vector corresponding to time t; the length of the real-time monitoring period and the historical monitoring period are the same, and the monitoring frequency within the real-time monitoring period and the historical monitoring period are also the same; the multidimensional operating parameter vector corresponding to time t within the real-time monitoring period and the historical operating parameter vector corresponding to time t within the historical monitoring period are in a corresponding relationship.

[0210] The state contrast fusion matrix satisfies the following formula:

[0211] Y(t)=W1·(y r (t)-y s (t))+W2·Ψ(y r (t),y s (t));

[0212] Among them, Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; W2 is the mapping association matrix.

[0213] In some embodiments, the pipeline status evaluation value satisfies the following formula:

[0214]

[0215] Among them, Φ(t) is the pipeline status evaluation value corresponding to time t in the real-time monitoring period; H is the weighted matrix operator; Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; Δt is the time window length; M is the mapping matrix operator; Y(τ) is the state comparison fusion matrix corresponding to time τ in the real-time monitoring period.

[0216] In some embodiments, the boosting station operation status assessment result generation module 404 is specifically configured to:

[0217] When the pipeline status evaluation value is greater than or equal to a preset pipeline status evaluation threshold, actual duration of time during which the pipeline status evaluation value is greater than or equal to the pipeline status evaluation threshold is counted.

[0218] When the actual duration is greater than or equal to the preset duration threshold, the booster station operation status assessment result is determined to be abnormal, and a booster station status warning is issued.

[0219] In some embodiments, the pipeline status monitoring area setting module 401 is specifically configured to:

[0220] Obtain the pipeline flow model, maximum number of monitoring points, and minimum monitoring distance of the booster station.

[0221] A potential monitoring point set is set according to the pipeline flow model; the potential monitoring point set includes multiple potential feasible monitoring points; and the potential feasible monitoring points correspond to a set of spatial coordinates.

[0222] A key monitoring point set is set according to the pipeline flow model; the key monitoring point set includes a plurality of key pending monitoring points; the key pending monitoring points correspond to a set of spatial coordinates.

[0223] According to the potential monitoring point set, key monitoring point set, maximum number of monitoring points and minimum monitoring distance, the pipeline status monitoring point set is set based on the monitoring point coverage model.

[0224] In some embodiments, the pipeline status monitoring area setting module 401 is specifically configured to:

[0225] The monitoring point coverage model is as follows:

[0226]

[0227] Among them, A is the feasibility indicator set corresponding to the subset of potential monitoring points, A={a1,a2,…,a i ,…};a i is the feasibility indicator corresponding to the i-th potentially feasible monitoring point, a i The value is 0 or 1; C(A) is the monitoring point coverage assessment value corresponding to the subset of the potential monitoring point set; j is the jth key monitoring point to be determined; K is the number of key monitoring points to be determined; i:a i =1 is The index of a i The i-th potential feasible monitoring point when the value of is 1; ω j is the weight corresponding to the jth key monitoring point to be determined; α is the distance attenuation coefficient; X i is the spatial coordinate corresponding to the i-th potential feasible monitoring point; Yj is the spatial coordinate corresponding to the jth key undetermined monitoring point, d(X i ,Y j ) is the distance between the spatial coordinates corresponding to the i-th potential feasible monitoring point and the spatial coordinates corresponding to the j-th key pending monitoring point.

[0228] The feasibility indication sets corresponding to different subsets of potential monitoring point sets are input into the monitoring point coverage model to obtain the monitoring point coverage evaluation values ​​corresponding to the subsets of the potential monitoring point set; the number of potential feasible monitoring points in the subset of the potential monitoring point set meets the maximum number of monitoring points; and the distance between the potential feasible monitoring points in the subset of the potential monitoring point set meets the minimum monitoring distance.

[0229] When the monitoring point coverage evaluation value corresponding to the subset of the potential monitoring point set is the largest, the subset of the potential monitoring point set is determined to be the pipeline state monitoring point set.

[0230] An embodiment of the present application further provides a computer-readable storage medium, which includes computer-executable instructions. When the computer-executable instructions are executed on a computer, the computer executes the method for evaluating the operating status of a boosting station provided in the above embodiment.

[0231] An embodiment of the present application also provides a computer program, which can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program can implement the method for evaluating the operating status of a boosting station provided in the above embodiment.

[0232] Those skilled in the art will appreciate that, in one or more of the examples above, the functions described herein can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer-readable storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0233] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0234] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place, or they may be distributed in multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0235] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or in other words, the part that contributes to the general technology or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for making a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0236] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the operating status of a booster station, characterized in that: include: Determining a pipeline status monitoring point set in the boosting station, and setting a pipeline status monitoring area according to the pipeline status monitoring point set; Acquiring real-time monitoring operation information and historical calibration standard information of the pipeline status monitoring area, and generating a status comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information; generating a pipeline state evaluation value according to the state comparison fusion matrix; An evaluation result of the booster station operation status is determined according to the pipeline status evaluation value.

2. The method according to claim 1, characterized in that The historical calibration standard information includes historical maintenance records of the booster station; and generating a state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information includes: Generating a historical operating parameter vector based on the historical calibration standard information, and generating a multi-dimensional operating parameter vector based on the real-time monitoring operating information; generating a nonlinear mapping matrix according to the multidimensional operating parameter vector; Determining weighted status monitoring points from the pipeline status monitoring point set according to the historical maintenance records, and setting a diagonal weighting matrix according to the weighted status monitoring points; The state comparison fusion matrix is ​​generated according to the nonlinear mapping matrix, the diagonal weighting matrix, the multidimensional operation parameter vector and the historical operation parameter vector.

3. The method according to claim 2, characterized in that The diagonal weighted matrix satisfies the following formula: Wherein, W1 is the diagonal weighted matrix; κ 1,1 is the weight corresponding to the first weighted state monitoring point; κ 1,2 is the weight corresponding to the second weighted state monitoring point; κ 1,n is the weight corresponding to the nth weighted status monitoring point; The nonlinear mapping matrix satisfies the following formula: Among them, y r (t) is the multidimensional operating parameter vector corresponding to time t in the real-time monitoring period; y s (t) is the historical operating parameter vector corresponding to time t in the historical monitoring period; Ψ(y r (t),y s (t)) is the nonlinear mapping matrix; Kur(y r (t)) is the kurtosis of the multidimensional operating parameter vector corresponding to the time t; Ske(y r (t)) is the skewness of the multidimensional operation parameter vector corresponding to the time t; Kur(y s (t)) is the kurtosis of the historical operating parameter vector corresponding to the time t; Ske(y s (t)) is the skewness corresponding to the historical operating parameter vector corresponding to the time t; the real-time monitoring period and the historical monitoring period have the same length, and the monitoring frequencies within the real-time monitoring period and the historical monitoring period are also the same; the multidimensional operating parameter vector corresponding to the time t within the real-time monitoring period and the historical operating parameter vector corresponding to the time t within the historical monitoring period are in a corresponding relationship; The state contrast fusion matrix satisfies the following formula: Y(t)=W1·(y r (t)-y s (t))+W2·Ψ(y r (t),y s (t)); Among them, Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; W2 is the mapping association matrix.

4. The method according to claim 1, wherein The pipeline status evaluation value satisfies the following formula: Among them, Φ(t) is the pipeline state evaluation value corresponding to time t in the real-time monitoring period; H is the weighted matrix operator; Y(t) is the state comparison fusion matrix corresponding to time t in the real-time monitoring period; Δt is the time window length; M is the mapping matrix operator; Y(τ) is the state comparison fusion matrix corresponding to time τ in the real-time monitoring period.

5. The method according to claim 1, characterized in that Determining the booster station operation status assessment result according to the pipeline status assessment value includes: When the pipeline state evaluation value is greater than or equal to a preset pipeline state evaluation threshold, counting an actual duration in which the pipeline state evaluation value is greater than or equal to the pipeline state evaluation threshold; When the actual duration is greater than or equal to the preset duration threshold, it is determined that the evaluation result of the operating status of the boosting station is abnormal, and a boosting station status warning is issued.

6. The method according to claim 1, characterized in that Determining the pipeline status monitoring point set in the boosting station includes: Obtain the pipeline flow model, maximum number of monitoring points, and minimum monitoring distance of the booster station; A potential monitoring point set is set according to the pipeline flow model; the potential monitoring point set includes a plurality of potential feasible monitoring points; the potential feasible monitoring points correspond to a set of spatial coordinates; A key monitoring point set is set according to the pipeline flow model; the key monitoring point set includes a plurality of key monitoring points to be determined; the key monitoring points to be determined correspond to a set of spatial coordinates; According to the potential monitoring point set, the key monitoring point set, the maximum number of monitoring points and the minimum monitoring distance, a pipeline status monitoring point set is set based on a monitoring point coverage model.

7. The method according to claim 6, characterized in that The step of setting the pipeline status monitoring point set based on the monitoring point coverage model according to the potential monitoring point set, the key monitoring point set, the maximum number of monitoring points, and the minimum monitoring distance includes: The monitoring point coverage model is as follows: Where A is the feasibility indicator set corresponding to the subset of the potential monitoring point set, A={a1,a2,…,a i ,…};a i is the feasibility indicator corresponding to the i-th potentially feasible monitoring point, a i The value is 0 or 1; C(A) is the monitoring point coverage evaluation value corresponding to the subset of the potential monitoring point set; j is the jth key monitoring point to be determined; K is the number of key monitoring points to be determined; i:a i =1 is The index of the i-th potential feasible monitoring point when the value of ai is 1; ωj is the weight corresponding to the j-th key pending monitoring point; α is the distance attenuation coefficient; Xi is the spatial coordinate corresponding to the i-th potential feasible monitoring point; Y j is the spatial coordinate corresponding to the jth key undetermined monitoring point, d(X i ,Y j ) is the distance between the spatial coordinates corresponding to the i-th potential feasible monitoring point and the spatial coordinates corresponding to the j-th key undetermined monitoring point; Inputting feasibility indication sets corresponding to different subsets of potential monitoring point sets into the monitoring point coverage model to obtain monitoring point coverage evaluation values ​​corresponding to the subsets of potential monitoring point sets; the number of potential feasible monitoring points in the subsets of potential monitoring point sets meets the maximum number of monitoring points; and the distance between the potential feasible monitoring points in the subsets of potential monitoring point sets meets the minimum monitoring distance; When the monitoring point coverage evaluation value corresponding to the subset of the potential monitoring point set is the largest, the subset of the potential monitoring point set is determined to be the pipeline status monitoring point set.

8. A device for evaluating the operating status of a booster station, characterized in that: include: Pipeline status monitoring area setting module, status comparison fusion matrix generation module, pipeline status assessment value generation module and booster station operation status assessment result generation module; The pipeline status monitoring area setting module is used to determine the pipeline status monitoring point set in the boosting station and set the pipeline status monitoring area according to the pipeline status monitoring point set; The state comparison fusion matrix generation module is used to obtain the real-time monitoring operation information and historical calibration standard information of the pipeline state monitoring area, and generate a state comparison fusion matrix based on the real-time monitoring operation information and the historical calibration standard information; The pipeline state evaluation value generating module is used to generate a pipeline state evaluation value according to the state comparison fusion matrix; The boosting station operation status evaluation result generating module is used to determine the boosting station operation status evaluation result according to the pipeline status evaluation value.

9. A booster station operating status assessment device, characterized in that: include: processor and memory; The memory is used to store one or more programs, and the one or more programs include computer-executable instructions. When the boosting station operation status assessment device is running, the processor executes the computer-executable instructions stored in the memory to enable the boosting station operation status assessment device to perform the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of a boosting station operating state evaluation device, the boosting station operating state evaluation device can perform the method according to any one of claims 1 to 7.

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