Efficient collection method and device for pipeline thickness data, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data collection, and discloses an efficient collection method and device for pipeline thickness data, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of pieces of pipeline thickness data, and dividing the pipeline thickness data into a plurality of pipeline thickness data links based on a generation time period; marking and calibrating the pipeline thickness data chain according to the pipeline thickness data and preset pipeline thickness data, and calculating a data acquisition initial coefficient; extracting a historical collection record of the pipeline wall thickness data on the pipeline thickness data chain, and calculating a historical collection fluctuation factor; a comprehensive data acquisition value is calculated based on a data acquisition initial coefficient and a historical acquisition fluctuation factor, and a data acquisition strategy of a pipeline thickness data chain is set according to the comprehensive data acquisition value, so that intelligent acquisition of pipeline thickness data can be realized, the precision and efficiency of pipeline thickness data acquisition are improved, the data acquisition priority is dynamically adjusted, and the data acquisition efficiency is improved. And the safety and the reliability of the pipeline operation process are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to an efficient acquisition method, device, electronic equipment and storage medium for pipeline thickness data. Background Art

[0002] In the modern industrial field, the health of the pipeline system is crucial to ensure production safety and environmental protection. Among them, the accurate collection and transmission of pipeline thickness data is one of the key parameters for evaluating pipeline integrity and predicting its remaining life. In order to ensure the normal operation of the pipeline, it is necessary to divide the pipeline into multiple wall thickness data collection segments, and generate a large amount of pipeline thickness data at the same time.

[0003] At present, when collecting pipeline thickness data, operation and maintenance personnel are required to log in to the equipment one by one to collect data on each wall thickness data collection segment. And the current data collection method usually adopts a sequential scanning method to collect the thickness data of each pipeline in turn. This method is simple to implement, but it has significant shortcomings when facing large-scale and high-frequency data collection needs. First of all, the sequential scanning method cannot distinguish and process the data according to its importance and urgency, which easily causes delayed collection and transmission of important data, aggravating the problem of delayed collection of pipeline thickness data, and making the important data collection cycle very long. Summary of the invention

[0004] In view of this, the present invention proposes an efficient collection method, device, electronic device and storage medium for pipeline thickness data. The present invention can realize intelligent collection of pipeline thickness data, improve the accuracy and efficiency of pipeline thickness data collection, dynamically adjust data collection priority, and ensure safety and reliability during pipeline operation.

[0005] The present invention proposes an efficient method for collecting pipeline thickness data, comprising:

[0006] Acquire multiple generated pipeline thickness data, and divide all pipeline thickness data into multiple pipeline thickness data chains based on the generation time period;

[0007] Analyze the pipeline thickness data on each pipeline thickness data link, identify and calibrate the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculate the data collection starting coefficient of the pipeline thickness data link based on the calibration result;

[0008] Extracting historical collection records of pipeline wall thickness data on the pipeline thickness data chain, analyzing the historical collection records, and calculating the historical collection fluctuation factor of the pipeline thickness data chain based on the analysis results;

[0009] The comprehensive data collection value of the pipeline thickness data link is calculated based on the data collection starting coefficient and the historical collection fluctuation factor, and the data collection strategy of the pipeline thickness data link is set according to the comprehensive data collection value.

[0010] Further, when analyzing the pipeline thickness data on each pipeline thickness data link, and calibrating the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculating the data acquisition starting coefficient of the pipeline thickness data link based on the calibration result, it includes:

[0011] Generate a risk thickness data identifier for all pipeline thickness data on the pipeline thickness data chain that is less than the preset pipeline thickness data;

[0012] Generate a safety thickness data identifier for all pipeline thickness data on the pipeline thickness data chain that is greater than or equal to the preset pipeline thickness data;

[0013] Clustering the pipeline thickness data for generating risk thickness data identification, determining the corresponding risk cluster center, and determining the risk cluster distance of each pipeline thickness data;

[0014] Clustering the pipeline thickness data for generating the safety thickness data identifier, determining the corresponding safety cluster center, and determining the safety cluster distance of each pipeline thickness data;

[0015] Calculating an average risk distance of all risk cluster distances, and calculating a sum of risk cluster distances of all risk cluster distances greater than or equal to the average risk distance;

[0016] Calculate the average safety distance of all safety clustering distances, and calculate the safety clustering distance and value of all safety clustering distances greater than or equal to the average safety distance;

[0017] The calculated ratio of the risk cluster distance and value to the safety cluster distance and value is calculated, and the data collection starting coefficient of the pipeline thickness data link is calculated according to the risk cluster distance and value, the safety cluster distance and value and the calculated ratio.

[0018] Further, when calculating the data acquisition starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value and the calculation ratio, it includes:

[0019] Calculate the safety data and value corresponding to the safety thickness data identifier according to the following formula;

[0020]

[0021] Among them, w1 is the safety data and value corresponding to the safety thickness data identifier, n1 is the number of pipeline thickness data corresponding to the safety thickness data identifier, a is the safety clustering distance and value, b i is the safety clustering distance corresponding to the i-th safety thickness data identifier, k i is the weight corresponding to the i-th safe clustering distance;

[0022] Calculate the risk data and value corresponding to the risk thickness data identifier according to the following formula;

[0023]

[0024] Among them, w2 is the risk data and value corresponding to the risk thickness data identifier, n2 is the number of pipeline thickness data corresponding to the risk thickness data identifier, h is the risk clustering distance and value, e j is the risk clustering distance corresponding to the j-th risk thickness data identifier, c j is the weight corresponding to the j-th risk clustering distance.

[0025] Further, when calculating the data acquisition starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value and the calculation ratio, it also includes:

[0026] calculating a second calculated ratio between the safety data and value and the risk data and value;

[0027] Calculating a third calculated ratio of the safety cluster distance sum value to the safety data sum value, and calculating a fourth calculated ratio of the risk cluster distance sum value to the risk data sum value;

[0028] calculating a fifth calculated ratio of the calculated ratio and the second calculated ratio;

[0029] A data collection starting coefficient of the pipeline thickness data link is calculated based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio.

[0030] Further, when calculating the data acquisition starting coefficient of the pipeline thickness data link based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio, it includes:

[0031] The data acquisition starting coefficient of the pipeline thickness data link is calculated according to the following formula:

[0032]

[0033] Among them, p is the data collection starting coefficient of the pipeline thickness data link, m1 is the third calculation ratio, m2 is the fourth calculation ratio, and m is the fifth calculation ratio.

[0034] Furthermore, when extracting the historical collection records of the pipeline wall thickness data on the pipeline thickness data chain, analyzing the historical collection records, and calculating the historical collection fluctuation factor of the pipeline thickness data chain based on the analysis results, it includes:

[0035] Analyze the historical collection records and extract the corresponding historical collection duration;

[0036] Extract historical collection records whose historical collection duration is longer than the standard collection duration, construct a first sub-data chain, and count the corresponding first quantity;

[0037] Extract historical collection records whose historical collection duration is equal to the standard collection duration, construct a second sub-data chain, and count the corresponding second quantity;

[0038] Extract historical collection records whose historical collection duration is less than the standard collection duration, construct the third sub-data chain, and count the corresponding third quantity;

[0039] Calculating the duration difference between each historical collection duration and the standard collection duration on the first sub-data chain, and determining a duration difference set;

[0040] The historical acquisition fluctuation factor of the pipeline thickness data link is calculated according to the time difference value set, the first number, the second number and the third number.

[0041] Further, when calculating the historical acquisition fluctuation factor of the pipeline thickness data link according to the time difference set, the first number, the second number and the third number, it includes:

[0042] The historical acquisition fluctuation factor of the pipeline thickness data link is calculated according to the following formula:

[0043]

[0044] Among them, q is the historical collection fluctuation factor of the pipeline thickness data link, t1 is the first quantity, t2 is the second quantity, r is the number of time difference values ​​in the time difference value set, and y s is the sth duration difference in the duration difference set, ymax is the maximum duration difference in the duration difference set, and u is the adjustment coefficient of the historical collection fluctuation factor.

[0045] Furthermore, the adjustment coefficient u of the historical collection fluctuation factor is determined according to the following method:

[0046] Presetting a first preset quantity ratio coefficient and a second preset quantity ratio coefficient;

[0047] Presetting a first preset adjustment coefficient u1, a second preset adjustment coefficient u2 and a third preset adjustment coefficient u3;

[0048] When the first preset condition is identified, the first preset adjustment coefficient u1 is used as the adjustment coefficient of the historical collection fluctuation factor;

[0049] When the second preset condition is identified, the second preset adjustment coefficient u2 is used as the adjustment coefficient of the historical collection fluctuation factor;

[0050] When the third preset condition is identified, the third preset adjustment coefficient u3 is used as the adjustment coefficient of the historical collection fluctuation factor; wherein, the first preset condition is 1<t3 / t1+t2≤first preset quantity ratio coefficient, the second preset condition is the first preset quantity ratio coefficient<t3 / t1+t2<second preset quantity ratio coefficient, and the third preset condition is the second preset quantity ratio coefficient≤t3 / t1+t2.

[0051] Further, when the data collection strategy of the pipeline thickness data link is set according to the comprehensive data collection value, it includes:

[0052] Sorting all the comprehensive data collection values ​​by numerical value, and using the sorting result as a data collection priority strategy for the pipeline thickness data;

[0053] Presetting a plurality of preset comprehensive data collection values, and setting a data collection speed strategy for the pipeline thickness data according to a relationship between the comprehensive data collection value and the plurality of preset comprehensive data collection values;

[0054] The data acquisition strategy of the pipeline thickness data link is set according to the data acquisition priority strategy and the data acquisition speed strategy.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention discloses an efficient collection method, device, electronic device and storage medium for pipeline thickness data, wherein the method comprises: acquiring a plurality of pipeline thickness data, dividing the pipeline thickness data into a plurality of pipeline thickness data chains based on a generation time period; marking and calibrating the pipeline thickness data chain according to the pipeline thickness data and preset pipeline thickness data, and calculating a data collection starting coefficient; extracting a historical collection record of pipeline wall thickness data on the pipeline thickness data chain, and calculating a historical collection fluctuation factor; calculating a comprehensive data collection value based on the data collection starting coefficient and the historical collection fluctuation factor, and setting a data collection strategy for the pipeline thickness data chain according to the comprehensive data collection value, thereby realizing intelligent collection of pipeline thickness data, improving the accuracy and efficiency of pipeline thickness data collection, dynamically adjusting the data collection priority, and ensuring the safety and reliability of the pipeline operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0058] Figure 1 A schematic diagram of a flow chart of an efficient method for collecting pipeline thickness data provided by an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of a flow chart of calculating a data acquisition starting coefficient of a pipeline thickness data link provided by an embodiment of the present invention;

[0060] Figure 3 It is a structural schematic diagram of an efficient device for collecting pipeline thickness data provided by an embodiment of the present invention;

[0061] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides an efficient method for collecting pipeline thickness data, including:

[0064] S110: Acquire multiple generated pipeline thickness data, and divide all pipeline thickness data into multiple pipeline thickness data chains based on the generation time period;

[0065] In this embodiment, in order to ensure the safe operation of the pipeline, when actually performing pipeline thickness data detection, the pipeline will be divided into multiple pipeline thickness data detection sections, each section corresponding to a pipeline thickness data, to ensure the comprehensiveness of pipeline thickness data detection.

[0066] In this embodiment, the generation time period may be from the 1st second to the 5th second, from the 6th second to the 10th second, etc.

[0067] S120: analyzing the pipeline thickness data on each pipeline thickness data link, and calibrating the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculating the data collection starting coefficient of the pipeline thickness data link based on the calibration result;

[0068] In some embodiments of the present application, when analyzing the pipeline thickness data on each pipeline thickness data link, and calibrating the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculating the data acquisition starting coefficient of the pipeline thickness data link based on the calibration result, it includes:

[0069] S121: generating a risk thickness data identifier for all pipeline thickness data on the pipeline thickness data chain that is less than the preset pipeline thickness data;

[0070] S122: generating a safety thickness data identifier for all pipeline thickness data on the pipeline thickness data chain that is greater than or equal to the preset pipeline thickness data;

[0071] S123: clustering the pipeline thickness data for generating risk thickness data identifiers, determining corresponding risk cluster centers, and determining risk cluster distances for each pipeline thickness data;

[0072] S124: clustering the pipeline thickness data for generating the safety thickness data identifier, determining the corresponding safety cluster center, and determining the safety cluster distance of each pipeline thickness data;

[0073] S125: Calculate the average risk distance of all risk cluster distances, and calculate the sum of risk cluster distances of all risk cluster distances greater than or equal to the average risk distance;

[0074] S126: Calculate the average safety distance of all safety clustering distances, and calculate the sum of safety clustering distances of all safety clustering distances greater than or equal to the average safety distance;

[0075] S127: Calculate the calculated ratio of the risk cluster distance and value to the safety cluster distance and value, and calculate the data collection starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value, and the calculated ratio.

[0076] In this embodiment, the preset pipeline thickness data is set according to the data conditions of the pipeline and is the optimal value to ensure the safe operation of the pipeline, thereby ensuring the safe operation of the pipeline.

[0077] In this embodiment, the risk cluster distance sum value refers to a value obtained by summing all risk cluster distances that are greater than or equal to the average risk distance.

[0078] In this embodiment, the safety cluster distance sum value refers to a value obtained by summing up all safety cluster distances that are greater than or equal to the average safety distance.

[0079] In this embodiment, the determination of the cluster center and the determination of the cluster distance are complicated and mature, and will not be introduced or limited in detail here.

[0080] The beneficial effect of the above technical solution is that the present invention calculates the calculated ratio of the risk cluster distance and value and the safety cluster distance and value, and calculates the data collection starting coefficient of the pipeline thickness data chain according to the risk cluster distance and value, the safety cluster distance and value and the calculated ratio, thereby ensuring the calculation accuracy of the data collection starting coefficient and providing a basis for the collection and processing of pipeline thickness data.

[0081] In some embodiments of the present application, when calculating the data acquisition starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value and the calculation ratio, it includes:

[0082] Calculate the safety data and value corresponding to the safety thickness data identifier according to the following formula;

[0083]

[0084] Among them, w1 is the safety data and value corresponding to the safety thickness data identifier, n1 is the number of pipeline thickness data corresponding to the safety thickness data identifier, a is the safety clustering distance and value, b i is the safety clustering distance corresponding to the i-th safety thickness data identifier, k i is the weight corresponding to the i-th safe clustering distance;

[0085] Calculate the risk data and value corresponding to the risk thickness data identifier according to the following formula;

[0086]

[0087] Among them, w2 is the risk data and value corresponding to the risk thickness data identifier, n2 is the number of pipeline thickness data corresponding to the risk thickness data identifier, h is the risk clustering distance and value, e j is the risk clustering distance corresponding to the j-th risk thickness data identifier, c j is the weight corresponding to the j-th risk clustering distance.

[0088] The beneficial effect of the above technical solution is that the present invention can further lay a foundation for calculating the data collection starting coefficient by calculating the safety data and value and the risk data and value.

[0089] In some embodiments of the present application, when calculating the data acquisition starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value and the calculation ratio, it also includes:

[0090] calculating a second calculated ratio between the safety data and value and the risk data and value;

[0091] Calculating a third calculated ratio of the safety cluster distance sum value to the safety data sum value, and calculating a fourth calculated ratio of the risk cluster distance sum value to the risk data sum value;

[0092] calculating a fifth calculated ratio of the calculated ratio and the second calculated ratio;

[0093] A data collection starting coefficient of the pipeline thickness data link is calculated based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio.

[0094] The beneficial effect of the above technical solution is that the present invention calculates the data acquisition starting coefficient of the pipeline thickness data chain based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio, further ensuring the calculation accuracy of the data acquisition starting coefficient and avoiding calculation errors.

[0095] In some embodiments of the present application, when calculating the data acquisition start coefficient of the pipeline thickness data link based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio, it includes:

[0096] The data acquisition starting coefficient of the pipeline thickness data link is calculated according to the following formula:

[0097]

[0098] Among them, p is the data collection starting coefficient of the pipeline thickness data link, m1 is the third calculation ratio, m2 is the fourth calculation ratio, and m is the fifth calculation ratio.

[0099] S130: extracting historical collection records of pipeline wall thickness data on the pipeline thickness data chain, analyzing the historical collection records, and calculating a historical collection fluctuation factor of the pipeline thickness data chain based on the analysis results;

[0100] In some embodiments of the present application, when extracting the historical collection records of the pipeline wall thickness data on the pipeline thickness data link, analyzing the historical collection records, and calculating the historical collection fluctuation factor of the pipeline thickness data link based on the analysis results, it includes:

[0101] Analyze the historical collection records and extract the corresponding historical collection duration;

[0102] Extract historical collection records whose historical collection duration is longer than the standard collection duration, construct a first sub-data chain, and count the corresponding first quantity;

[0103] Extract historical collection records whose historical collection duration is equal to the standard collection duration, construct a second sub-data chain, and count the corresponding second quantity;

[0104] Extract historical collection records whose historical collection duration is less than the standard collection duration, construct the third sub-data chain, and count the corresponding third quantity;

[0105] Calculating the duration difference between each historical collection duration and the standard collection duration on the first sub-data chain, and determining a duration difference set;

[0106] The historical acquisition fluctuation factor of the pipeline thickness data link is calculated according to the time difference value set, the first number, the second number and the third number.

[0107] In this embodiment, each historical collection record corresponds to a historical collection duration.

[0108] In this embodiment, the standard collection time is associated with the data collector, and is preferably 5 minutes.

[0109] The beneficial effect of the above technical solution is that the present invention calculates the historical collection fluctuation factor of the pipeline thickness data chain according to the time difference set, the first quantity, the second quantity and the third quantity. The present invention not only ensures the accurate calculation of the historical collection fluctuation factor, but also provides another basis for the collection and processing of pipeline thickness data.

[0110] In some embodiments of the present application, when calculating the historical acquisition fluctuation factor of the pipeline thickness data link according to the time difference value set, the first number, the second number and the third number, it includes:

[0111] The historical acquisition fluctuation factor of the pipeline thickness data link is calculated according to the following formula:

[0112]

[0113] Among them, q is the historical collection fluctuation factor of the pipeline thickness data link, t1 is the first quantity, t2 is the second quantity, r is the number of time difference values ​​in the time difference value set, and y s is the sth duration difference in the duration difference set, ymax is the maximum duration difference in the duration difference set, and u is the adjustment coefficient of the historical collection fluctuation factor.

[0114] In some embodiments of the present application, the adjustment coefficient u of the historical acquisition fluctuation factor is determined according to the following method:

[0115] Presetting a first preset quantity ratio coefficient and a second preset quantity ratio coefficient;

[0116] Presetting a first preset adjustment coefficient u1, a second preset adjustment coefficient u2 and a third preset adjustment coefficient u3;

[0117] When the first preset condition is identified, the first preset adjustment coefficient u1 is used as the adjustment coefficient of the historical collection fluctuation factor;

[0118] When the second preset condition is identified, the second preset adjustment coefficient u2 is used as the adjustment coefficient of the historical collection fluctuation factor;

[0119] When the third preset condition is identified, the third preset adjustment coefficient u3 is used as the adjustment coefficient of the historical collection fluctuation factor; wherein, the first preset condition is 1<t3 / t1+t2≤first preset quantity ratio coefficient, the second preset condition is the first preset quantity ratio coefficient<t3 / t1+t2<second preset quantity ratio coefficient, and the third preset condition is the second preset quantity ratio coefficient≤t3 / t1+t2.

[0120] In this embodiment, the first preset quantity ratio coefficient is preferably 1.2, and the second preset quantity ratio coefficient is preferably 1.4.

[0121] In this embodiment, the first preset adjustment coefficient u1=1.15, the second preset adjustment coefficient u2=1.25, and the third preset adjustment coefficient u3=1.35.

[0122] The beneficial effect of the above technical solution is that the present invention adjusts the historical collection fluctuation factor by selecting the corresponding adjustment coefficient, thereby realizing the dynamic adjustment of the historical collection fluctuation factor, and further ensuring the calculation comprehensiveness and calculation accuracy of the historical collection fluctuation factor.

[0123] S140: Calculating a comprehensive data collection value of the pipeline thickness data link based on the data collection starting coefficient and the historical collection fluctuation factor, and setting a data collection strategy for the pipeline thickness data link according to the comprehensive data collection value.

[0124] In this embodiment, the first calculation coefficient of the data collection starting coefficient is determined, and the second calculation coefficient of the historical collection fluctuation factor is determined. The comprehensive data collection value = data collection starting coefficient * first calculation coefficient + historical collection fluctuation factor * second calculation coefficient, wherein the first calculation coefficient is preferably 0.7 and the second calculation coefficient is preferably 0.3.

[0125] In some embodiments of the present application, when the data acquisition strategy of the pipeline thickness data link is set according to the comprehensive data acquisition value, it includes:

[0126] Sorting all the comprehensive data collection values ​​by numerical value, and using the sorting result as a data collection priority strategy for the pipeline thickness data;

[0127] Presetting a plurality of preset comprehensive data collection values, and setting a data collection speed strategy for the pipeline thickness data according to a relationship between the comprehensive data collection value and the plurality of preset comprehensive data collection values;

[0128] The data acquisition strategy of the pipeline thickness data link is set according to the data acquisition priority strategy and the data acquisition speed strategy.

[0129] In this embodiment, the greater the comprehensive data collection value is, the greater the priority is.

[0130] In this embodiment, when a plurality of preset comprehensive data acquisition values ​​are preset, and the data acquisition speed strategy of the pipeline thickness data is set according to the relationship between the comprehensive data acquisition value and the plurality of preset comprehensive data acquisition values, the first preset comprehensive data acquisition value is preset, and preferably 8, the second preset comprehensive data acquisition value is preset, and preferably 12, the first preset data acquisition speed is preset, and preferably 180KS / s, the second preset data acquisition speed is preset, and preferably 220KS / s, and the third preset data acquisition speed is preset, and preferably 260KS / s, where "KS" refers to 200,000 sampling points per second, and "s" means seconds, 180KS / s can also be recorded as 180KS, and similarly 220KS / s can also be recorded as 220KS, and 260KS / s can be recorded as 260KS. Specifically, data acquisition instruments such as data acquisition instruments, sensors, and automated test systems can be used, and selected according to actual needs.

[0131] In this embodiment, when the comprehensive data acquisition value is less than the first preset comprehensive data acquisition value, the first preset data acquisition speed is used as the data acquisition speed strategy for the pipeline thickness data; when the comprehensive data acquisition value is greater than or equal to the first preset comprehensive data acquisition value and less than the second preset comprehensive data acquisition value, the second preset data acquisition speed is used as the data acquisition speed strategy for the pipeline thickness data; when the comprehensive data acquisition value is greater than or equal to the second preset comprehensive data acquisition value, the third preset data acquisition speed is used as the data acquisition speed strategy for the pipeline thickness data.

[0132] The beneficial effect of the above technical solution is: the present invention sorts all the comprehensive data collection values ​​by numerical size, and uses the sorting result as the data collection priority strategy for the pipeline thickness data, dynamically adjusts the data collection priority, ensures the preferred collection and processing of important data, and sets the data collection speed strategy for the pipeline thickness data according to the relationship between the comprehensive data collection value and multiple preset comprehensive data collection values, which can not only ensure the phenomenon of data delayed collection, but also avoid the phenomenon of unstable data collection, thereby ensuring the real-time and reliability of data collection.

[0133] Figure 3 The schematic diagram of the structure of an efficient pipeline thickness data acquisition device provided in an embodiment of the present invention is a schematic diagram of the structure of an efficient pipeline thickness data acquisition device provided in an embodiment of the present invention. The efficient pipeline thickness data acquisition device provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal and / or a server to implement the efficient pipeline thickness data acquisition method in an embodiment of the present invention. The device may specifically include: a data link division module 310, a data acquisition start coefficient calculation module 320, a historical acquisition fluctuation factor calculation module 330, and a data acquisition strategy setting module 340.

[0134] Among them, the data link division module 310 is used to obtain multiple generated pipeline thickness data, and divide all the pipeline thickness data into multiple pipeline thickness data links based on the generation time period; the data acquisition starting coefficient calculation module 320 is used to analyze the pipeline thickness data on each pipeline thickness data link, and identify and calibrate the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculate the data acquisition starting coefficient of the pipeline thickness data link based on the calibration result; the historical acquisition fluctuation factor calculation module 330 is used to extract the historical acquisition records of the pipeline wall thickness data on the pipeline thickness data link, analyze the historical acquisition records, and calculate the historical acquisition fluctuation factor of the pipeline thickness data link based on the analysis result; the data acquisition strategy setting module 340 is used to calculate the comprehensive data acquisition value of the pipeline thickness data link based on the data acquisition starting coefficient and the historical acquisition fluctuation factor, and set the data acquisition strategy of the pipeline thickness data link according to the comprehensive data acquisition value.

[0135] The technical solution of this embodiment is to obtain multiple generated pipeline thickness data, divide the pipeline thickness data into multiple pipeline thickness data chains based on the generation time period; identify and calibrate the pipeline thickness data chain according to the pipeline thickness data and the preset pipeline thickness data, and calculate the data collection starting coefficient of the pipeline thickness data chain; extract the historical collection record of the pipeline wall thickness data on the pipeline thickness data chain, and calculate the historical collection fluctuation factor of the pipeline thickness data chain; calculate the comprehensive data collection value of the pipeline thickness data chain based on the data collection starting coefficient and the historical collection fluctuation factor, and set the data collection strategy of the pipeline thickness data chain according to the comprehensive data collection value, so as to realize the intelligent collection of pipeline thickness data, improve the accuracy and efficiency of pipeline thickness data collection, dynamically adjust the data collection priority, and ensure the safety and reliability of the pipeline operation process.

[0136] The above-mentioned data storage device can execute the data storage method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the data storage method.

[0137] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, the electronic device includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of processors 410 in the device can be one or more. Figure 4 A processor 410 is taken as an example; the processor 410, memory 420, input device 430 and output device 440 in the device can be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0138] The memory 420 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to an efficient method for collecting pipeline thickness data in an embodiment of the present invention. The processor 410 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 420.

[0139] The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 420 may further include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0140] The input device 430 may be used to receive input digital or character information and generate signal input related to user settings and function control of the device. The output device 440 may include a display device such as a display screen.

[0141] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0142] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0143] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An efficient method for collecting pipeline thickness data, characterized in that: include: Acquire multiple generated pipeline thickness data, and divide all pipeline thickness data into multiple pipeline thickness data chains based on the generation time period; Analyze the pipeline thickness data on each pipeline thickness data link, identify and calibrate the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculate the data collection starting coefficient of the pipeline thickness data link based on the calibration result; Extracting historical collection records of pipeline wall thickness data on the pipeline thickness data chain, analyzing the historical collection records, and calculating the historical collection fluctuation factor of the pipeline thickness data chain based on the analysis results; The comprehensive data collection value of the pipeline thickness data link is calculated based on the data collection starting coefficient and the historical collection fluctuation factor, and the data collection strategy of the pipeline thickness data link is set according to the comprehensive data collection value.

2. The efficient method for collecting pipeline thickness data according to claim 1 is characterized in that: When analyzing the pipeline thickness data on each pipeline thickness data link, and calibrating the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculating the data collection starting coefficient of the pipeline thickness data link based on the calibration result, it includes: Generate a risk thickness data identifier for all pipeline thickness data on the pipeline thickness data chain that is less than the preset pipeline thickness data; Generate a safety thickness data identifier for all pipeline thickness data on the pipeline thickness data chain that is greater than or equal to the preset pipeline thickness data; Clustering the pipeline thickness data for generating risk thickness data identification, determining the corresponding risk cluster center, and determining the risk cluster distance of each pipeline thickness data; Clustering the pipeline thickness data for generating the safety thickness data identifier, determining the corresponding safety cluster center, and determining the safety cluster distance of each pipeline thickness data; Calculating an average risk distance of all risk cluster distances, and calculating a sum of risk cluster distances of all risk cluster distances greater than or equal to the average risk distance; Calculate the average safety distance of all safety clustering distances, and calculate the safety clustering distance and value of all safety clustering distances greater than or equal to the average safety distance; The calculated ratio of the risk cluster distance and value to the safety cluster distance and value is calculated, and the data collection starting coefficient of the pipeline thickness data link is calculated according to the risk cluster distance and value, the safety cluster distance and value and the calculated ratio.

3. The efficient method for collecting pipeline thickness data according to claim 2 is characterized in that: When calculating the data acquisition starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value and the calculation ratio, it includes: Calculate the safety data and value corresponding to the safety thickness data identifier according to the following formula; Among them, w1 is the safety data and value corresponding to the safety thickness data identifier, n1 is the number of pipeline thickness data corresponding to the safety thickness data identifier, a is the safety clustering distance and value, b i is the safety clustering distance corresponding to the i-th safety thickness data identifier, k i is the weight corresponding to the i-th safe clustering distance; Calculate the risk data and value corresponding to the risk thickness data identifier according to the following formula; Among them, w2 is the risk data and value corresponding to the risk thickness data identifier, n2 is the number of pipeline thickness data corresponding to the risk thickness data identifier, h is the risk clustering distance and value, e j is the risk clustering distance corresponding to the j-th risk thickness data identifier, c j is the weight corresponding to the j-th risk clustering distance.

4. The efficient method for collecting pipeline thickness data according to claim 3 is characterized in that: When calculating the data acquisition starting coefficient of the pipeline thickness data link according to the risk cluster distance and value, the safety cluster distance and value and the calculation ratio, it also includes: calculating a second calculated ratio between the safety data and value and the risk data and value; Calculating a third calculated ratio of the safety cluster distance sum value to the safety data sum value, and calculating a fourth calculated ratio of the risk cluster distance sum value to the risk data sum value; calculating a fifth calculated ratio of the calculated ratio and the second calculated ratio; A data collection starting coefficient of the pipeline thickness data link is calculated based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio.

5. The efficient method for collecting pipeline thickness data according to claim 4 is characterized in that: When calculating the data acquisition starting coefficient of the pipeline thickness data link based on the third calculation ratio, the fourth calculation ratio and the fifth calculation ratio, it includes: The data acquisition starting coefficient of the pipeline thickness data link is calculated according to the following formula: Among them, p is the data collection starting coefficient of the pipeline thickness data link, m1 is the third calculation ratio, m2 is the fourth calculation ratio, and m is the fifth calculation ratio.

6. The efficient method for collecting pipeline thickness data according to claim 1 is characterized in that: When extracting the historical collection records of the pipeline wall thickness data on the pipeline thickness data chain, analyzing the historical collection records, and calculating the historical collection fluctuation factor of the pipeline thickness data chain based on the analysis results, it includes: Analyze the historical collection records and extract the corresponding historical collection duration; Extract historical collection records whose historical collection duration is longer than the standard collection duration, construct a first sub-data chain, and count the corresponding first quantity; Extract historical collection records whose historical collection duration is equal to the standard collection duration, construct a second sub-data chain, and count the corresponding second quantity; Extract historical collection records whose historical collection duration is less than the standard collection duration, construct the third sub-data chain, and count the corresponding third quantity; Calculating the duration difference between each historical collection duration and the standard collection duration on the first sub-data chain, and determining a duration difference set; The historical acquisition fluctuation factor of the pipeline thickness data link is calculated according to the time difference value set, the first number, the second number and the third number.

7. The efficient method for collecting pipeline thickness data according to claim 6 is characterized in that: When calculating the historical acquisition fluctuation factor of the pipeline thickness data link according to the time difference value set, the first number, the second number and the third number, it includes: The historical acquisition fluctuation factor of the pipeline thickness data link is calculated according to the following formula: Among them, q is the historical collection fluctuation factor of the pipeline thickness data link, t1 is the first quantity, t2 is the second quantity, r is the number of time difference values ​​in the time difference value set, and y s is the sth duration difference in the duration difference set, ymax is the maximum duration difference in the duration difference set, and u is the adjustment coefficient of the historical collection fluctuation factor.

8. The efficient method for collecting pipeline thickness data according to claim 7 is characterized in that: The adjustment coefficient u of the historical acquisition fluctuation factor is determined according to the following method: Presetting a first preset quantity ratio coefficient and a second preset quantity ratio coefficient; Presetting a first preset adjustment coefficient u1, a second preset adjustment coefficient u2 and a third preset adjustment coefficient u3; When the first preset condition is identified, the first preset adjustment coefficient u1 is used as the adjustment coefficient of the historical collection fluctuation factor; When the second preset condition is identified, the second preset adjustment coefficient u2 is used as the adjustment coefficient of the historical collection fluctuation factor; When the third preset condition is identified, the third preset adjustment coefficient u3 is used as the adjustment coefficient of the historical collection fluctuation factor; wherein, the first preset condition is 1<t3 / t1+t2≤first preset quantity ratio coefficient, the second preset condition is the first preset quantity ratio coefficient<t3 / t1+t2<second preset quantity ratio coefficient, and the third preset condition is the second preset quantity ratio coefficient≤t3 / t1+t2.

9. The efficient method for collecting pipeline thickness data according to claim 1, characterized in that: When the data acquisition strategy of the pipeline thickness data link is set according to the comprehensive data acquisition value, it includes: Sorting all the comprehensive data collection values ​​by numerical value, and using the sorting result as a data collection priority strategy for the pipeline thickness data; Presetting a plurality of preset comprehensive data collection values, and setting a data collection speed strategy for the pipeline thickness data according to a relationship between the comprehensive data collection value and the plurality of preset comprehensive data collection values; The data acquisition strategy of the pipeline thickness data link is set according to the data acquisition priority strategy and the data acquisition speed strategy.

10. An efficient device for collecting pipeline thickness data, characterized in that: include: A data chain division module, used for acquiring a plurality of generated pipeline thickness data, and dividing all pipeline thickness data into a plurality of pipeline thickness data chains based on a generation time period; A data acquisition starting coefficient calculation module is used to analyze the pipeline thickness data on each pipeline thickness data link, identify and calibrate the pipeline thickness data link according to the relationship between the pipeline thickness data and the preset pipeline thickness data, and calculate the data acquisition starting coefficient of the pipeline thickness data link based on the calibration result; A historical acquisition fluctuation factor calculation module is used to extract the historical acquisition records of the pipeline wall thickness data on the pipeline thickness data chain, analyze the historical acquisition records, and calculate the historical acquisition fluctuation factor of the pipeline thickness data chain based on the analysis results; A data acquisition strategy setting module is used to calculate the comprehensive data acquisition value of the pipeline thickness data link based on the data acquisition starting coefficient and the historical acquisition fluctuation factor, and set the data acquisition strategy of the pipeline thickness data link according to the comprehensive data acquisition value.

11. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the efficient pipeline thickness data collection method as described in any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the efficient method for collecting pipeline thickness data as described in any one of claims 1 to 9 is implemented.

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