CN116562508B - Optimal critical value acquisition and analysis system based on track traffic station reservation engineering - Google Patents
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Abstract
The application belongs to the field of subway reserved engineering, relates to a data analysis technology, and is used for solving the problem that the conventional railway traffic station reserved engineering cannot analyze historical data to obtain an optimal critical value of the station reserved engineering, in particular to an optimal critical value acquisition and analysis system based on the railway traffic station reserved engineering, which comprises an acquisition and analysis platform, wherein the acquisition and analysis platform is in communication connection with a body quantity analysis module, a period management module, a critical analysis module and a storage module; the method comprises the steps of sending a volume coefficient TL of a detection object to an acquisition and analysis platform, and sending the volume coefficient TL to a period management module after the acquisition and analysis platform receives the volume coefficient TL; the method can detect and analyze the bearing capacity of the rail transit station, obtain the body quantity coefficient by carrying out predictive analysis on the load state of the rail transit station after being put into operation, and provide data support for screening matched objects by the period management module through the body quantity coefficient.
Description
Technical Field
The application belongs to the field of subway reservation engineering, relates to a data analysis technology, and particularly relates to an optimal critical value acquisition and analysis system based on rail transit station reservation engineering.
Background
In the comprehensive project of comprehensive hub and underground space integrated design, the long-term subway engineering needs to be considered according to reservation, so that the tight and reasonable transfer connection relation is ensured, the adverse effect on the existing engineering caused by subway engineering implementation after the implementation of the comprehensive hub and underground space integrated project is avoided, the reasonable reservation of the reserved engineering of the subway station is ensured, the optimal early-stage implementation engineering quantity and investment are ensured, the long-term implementation path is reasonable, and the engineering quantity is minimum, so that the technical problem to be solved in the field is urgent.
The existing track traffic station reservation engineering can only carry out planning analysis on reserved items, but historical data cannot be analyzed to obtain an optimal critical value of the station reservation engineering, so that effective monitoring on construction progress cannot be carried out, and scientific management on engineering progress cannot be carried out.
The application provides a solution to the technical problem.
Disclosure of Invention
The application aims to provide an optimal critical value acquisition and analysis system based on a track traffic station reservation project, which is used for solving the problem that the conventional track traffic station reservation project cannot analyze historical data to obtain an optimal critical value of the station reservation project;
the technical problems to be solved by the application are as follows: how to provide an optimal critical value acquisition and analysis system based on the track traffic station reservation project, which can analyze historical data to obtain the optimal critical value of the station reservation project.
The aim of the application can be achieved by the following technical scheme:
the optimal critical value acquisition and analysis system based on the track traffic station reservation engineering comprises an acquisition and analysis platform, wherein the acquisition and analysis platform is in communication connection with a body quantity analysis module, a period management module, a critical analysis module and a storage module;
the body quantity analysis module is used for detecting and analyzing the bearing capacity of the rail transit station, obtaining a body quantity coefficient TL of a detection object, sending the body quantity coefficient TL of the detection object to the acquisition and analysis platform, and sending the body quantity coefficient TL to the period management module after the acquisition and analysis platform receives the body quantity coefficient TL;
the period management module is used for carrying out management analysis on the construction period of the reserved engineering of the detection object and obtaining a matched object; the method comprises the steps that a matching object is sent to an acquisition and analysis platform, and the acquisition and analysis platform receives the matching object and then sends the matching object to a critical analysis module;
the critical analysis module is used for analyzing and processing the optimal critical value of the detection object after receiving the historical construction data, obtaining an optimal critical data set, sending the optimal critical data set to the acquisition and analysis platform, and sending the optimal critical data set to the mobile phone terminal of the manager after the acquisition and analysis platform receives the optimal critical data set.
As a preferred embodiment of the present application, the specific process of the body quantity analysis module for detecting and analyzing the bearing body quantity of the rail transit station includes: marking a rail transit station to be subjected to carrier quantity detection as a detection object, and acquiring line data XL, coverage data FD and transfer data HC of the detection object; the volume coefficient TL of the detection target is obtained by performing numerical calculation on the line data XL, the coverage data FD, and the transfer data HC.
As a preferred embodiment of the present application, the line data XL of the detection object is a track line number value passing through the detection object; the acquisition process of the coverage data FD of the detection object includes: drawing a circle by taking a detection object as a circle center and r1 as a radius, marking the obtained circular area as a coverage area, and marking the number of rail transit stations in the coverage area as coverage data FD; the transfer data HC of the detection object is a sum of the number of stairs and the number of stairs from the hall to the platform of the detection object.
As a preferred embodiment of the present application, the specific process of the cycle management module for performing management analysis on the construction cycle of the reserved engineering of the detection object includes: the method comprises the steps that a historical volume coefficient of all rail transit stations is obtained through a storage module, a volume range is formed by a maximum value of the historical volume coefficient and a minimum value of the historical volume coefficient, the volume range is divided into a plurality of volume intervals, the volume intervals matched with a volume coefficient TL of a detection object are marked as matching intervals, periodic data ZQ and sum data JE of the detection object are obtained, the periodic data ZQ is the total duration of reserved engineering construction periods of the detection object, and the sum data JE of the detection object is the total budget value of reserved engineering construction of the detection object; obtaining period thresholds ZQmax and ZQmin of a detection object through formulas ZQmax=t1×ZQ and ZQmin=t2×ZQ, and obtaining sum thresholds JEMax and JEMin of the detection object through formulas JEMin=t1×JE and JEMin=t2×JE, wherein t1 and t2 are proportionality coefficients, and t1 is more than or equal to 1.15 and less than or equal to 1.25,0.75 and t2 is less than or equal to 0.85; the period threshold ZQmax and ZQmin form a period range, the sum threshold JEMin and JEMax form a sum range, and the track traffic station with the history volume coefficient in the matching section, the sum data in the sum range and the period data in the period range is marked as a matching object.
As a preferred embodiment of the present application, the specific process of analyzing the optimal critical value of the detection object by the critical analysis module includes: carrying out construction period segmentation on the construction period of the matched object to obtain a plurality of construction nodes, carrying out numerical conversion on the construction progress in percentage by the construction node division of the construction period, obtaining a progress elevation value and a progress depression value of the construction nodes, obtaining the construction progress value of the matched object on the construction nodes, judging whether the construction progress value is positioned between the progress elevation value and the progress depression value, and if so, marking the corresponding construction nodes as conforming nodes; if not, marking the corresponding construction node as a deviation node; obtaining coincidence data FH, deviation data PL and stable data WD of a matching object, and performing numerical calculation to obtain a close coefficient TJ of the matching object; and marking the matched object with the largest proximate coefficient TJ value as a selected object, and forming an optimal critical data set by the progress critical value and the progress critical value of each construction node in the selected object.
As a preferred embodiment of the present application, the process of acquiring the coincidence data FH of the matching object includes: marking an average value of a progress temporary high value and a progress temporary low value of a construction node as a progress intermediate value, marking an absolute value of a difference value between the construction progress value and the progress intermediate value of the construction node as a coincidence value of the construction node, summing all coincidence values of the coincidence nodes, and taking the average value to obtain coincidence data FH; the acquisition process of the offset data PL of the matching object includes: judging whether the construction progress value of the deviated node is smaller than the progress critical low value, if so, marking the difference value between the progress critical low value and the construction progress value as the deviated value of the deviated node; if not, marking the difference value between the construction progress value and the progress temporary value as a deviation value of a deviation node; summing the deviation values of all the deviation nodes and averaging to obtain deviation data PL; the process for acquiring the stable data WD of the matching object includes: and establishing a stable set of the coincidence values of all the construction nodes, and performing variance calculation on the stable set to obtain stable data WD of the matched object.
As a preferred embodiment of the present application, the working method of the optimal critical value acquisition and analysis system based on the track traffic station reservation engineering includes the following steps:
step one: detecting and analyzing the bearing capacity of the rail transit station: marking a rail transit station to be subjected to carrier quantity detection as a detection object, acquiring line data XL, coverage data FD and transfer data HC of the detection object, and performing numerical calculation to obtain a body quantity coefficient TL;
step two: management analysis is carried out on the construction period of the reserved engineering of the detection object: the method comprises the steps of obtaining a volume interval of a rail transit station, marking the volume interval matched with a volume coefficient TL of a detection object as a matching interval, obtaining an amount range and a period range of the detection object, and screening the rail transit station through the matching interval, the period range and the amount range to obtain the matching object;
step three: analyzing and processing the optimal critical value of the detection object: and obtaining coincidence data FH, deviation data PL and stable data WD of the matched objects, carrying out numerical calculation to obtain a close coefficient TJ, marking the matched object with the largest close coefficient TJ as a selected object, and forming an optimal critical data set by a progress critical value and a progress critical value of each construction node in the selected object.
The application has the following beneficial effects:
the body quantity analysis module can detect and analyze the bearing capacity of the rail transit station, the body quantity coefficient is obtained by carrying out predictive analysis on the load state of the transit station after being put into operation, and the period management module is used for screening matched objects through the body quantity coefficient to provide data support;
the construction period of the reserved engineering of the detection object can be managed and analyzed through the period management module, the historical volume coefficient of the rail transit station is analyzed to obtain a matching range, the period range and the amount range of the detection object are combined to screen to obtain a matching object, the matching object is similar to the detection object in the carrier volume, the construction period and the amount budget, and therefore accuracy of the optimal critical value obtained by carrying out critical value analysis on the matching object is improved;
the optimal critical value of the detection object can be analyzed and processed through the critical analysis module, the approach coefficient is obtained through data analysis of the progress completion quantity of the matching object at each construction node, the critical value setting reasonability of the matching object is fed back through the approach coefficient, the node critical value data set of the matching object with the highest attachment degree is marked, the marked data set is used as the optimal critical value of the detection object, and therefore construction progress constraint is conducted on the detection object through the optimal critical value.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present application;
fig. 2 is a flowchart of a method according to a second embodiment of the application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
As shown in fig. 1, the optimal critical value acquisition and analysis system based on the track traffic station reservation engineering comprises an acquisition and analysis platform, wherein the acquisition and analysis platform is in communication connection with a volume analysis module, a period management module, a critical analysis module and a storage module.
The body quantity analysis module is used for detecting and analyzing the bearing body quantity of the rail transit station: marking a rail transit station to be subjected to carrier quantity detection as a detection object, and acquiring line data XL, coverage data FD and transfer data HC of the detection object; the line data XL of the detection object is the track line quantity value passing through the detection object; the acquisition process of the coverage data FD of the detection object includes: drawing a circle by taking a detection object as a circle center and r1 as a radius, marking the obtained circular area as a coverage area, and marking the number of rail transit stations in the coverage area as coverage data FD; the transfer data HC of the detection object is the sum of the stair number value and the stair number value from the hall to the platform of the detection object; obtaining a body quantity coefficient TL of the detection object through a formula TL=α1xXL- α2xFD+α3xHC, wherein the body quantity coefficient is a numerical value reflecting the size of the carrier quantity after the operation of the detection object, and the larger the numerical value of the body quantity coefficient is, the larger the carrier quantity after the operation of the detection object is indicated; wherein, alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 > alpha 2 > alpha 3 > 1; the method comprises the steps of sending a volume coefficient TL of a detection object to an acquisition and analysis platform, and sending the volume coefficient TL to a period management module after the acquisition and analysis platform receives the volume coefficient TL; and detecting and analyzing the bearing capacity of the rail transit station, obtaining a body quantity coefficient by predicting and analyzing the load state of the transit rail station after being put into the station, and providing data support for screening the matched objects by the period management module through the body quantity coefficient.
The period management module is used for carrying out management analysis on the construction period of the reserved engineering of the detection object: the method comprises the steps that a historical volume coefficient of all rail transit stations is obtained through a storage module, a volume range is formed by a maximum value of the historical volume coefficient and a minimum value of the historical volume coefficient, the volume range is divided into a plurality of volume intervals, the volume intervals matched with a volume coefficient TL of a detection object are marked as matching intervals, periodic data ZQ and sum data JE of the detection object are obtained, the periodic data ZQ is the total duration of reserved engineering construction periods of the detection object, and the sum data JE of the detection object is the total budget value of reserved engineering construction of the detection object; obtaining period thresholds ZQmax and ZQmin of a detection object through formulas ZQmax=t1×ZQ and ZQmin=t2×ZQ, and obtaining sum thresholds JEMax and JEMin of the detection object through formulas JEMin=t1×JE and JEMin=t2×JE, wherein t1 and t2 are proportionality coefficients, and t1 is more than or equal to 1.15 and less than or equal to 1.25,0.75 and t2 is less than or equal to 0.85; the method comprises the steps that a period range is formed by a period threshold ZQmax and ZQmin, an amount range is formed by an amount threshold JEMin and JEMax, and a track traffic station with a history volume coefficient in a matching interval, amount data in the amount range and period data in the period range is marked as a matching object; the method comprises the steps that a matching object is sent to an acquisition and analysis platform, and the acquisition and analysis platform receives the matching object and then sends the matching object to a critical analysis module; and carrying out management analysis on the construction period of the reserved engineering of the detection object, analyzing the historical volume coefficient of the rail transit station to obtain a matching range, screening the cycle range and the monetary range of the detection object to obtain a matching object, wherein the matching object is similar to the detection object in the carrier volume, the construction period and the monetary budget, so that the accuracy of carrying out critical value analysis on the matching object to obtain an optimal critical value is improved.
The critical analysis module is used for carrying out analysis processing on the optimal critical value of the detection object after receiving the historical construction data: carrying out construction period segmentation on the construction period of the matched object to obtain a plurality of construction nodes, carrying out numerical conversion on the construction progress in percentage by the construction node division of the construction period, obtaining a progress elevation value and a progress depression value of the construction nodes, obtaining the construction progress value of the matched object on the construction nodes, judging whether the construction progress value is positioned between the progress elevation value and the progress depression value, and if so, marking the corresponding construction nodes as conforming nodes; if not, marking the corresponding construction node as a deviation node; the acquiring process of the coincidence data FH of the matching object, the deviation data PL and the stable data WD includes: marking an average value of a progress temporary high value and a progress temporary low value of a construction node as a progress intermediate value, marking an absolute value of a difference value between the construction progress value and the progress intermediate value of the construction node as a coincidence value of the construction node, summing all coincidence values of the coincidence nodes, and taking the average value to obtain coincidence data FH; the acquisition process of the offset data PL of the matching object includes: judging whether the construction progress value of the deviated node is smaller than the progress critical low value, if so, marking the difference value between the progress critical low value and the construction progress value as the deviated value of the deviated node; if not, marking the difference value between the construction progress value and the progress temporary value as a deviation value of a deviation node; summing the deviation values of all the deviation nodes and averaging to obtain deviation data PL; the process for acquiring the stable data WD of the matching object includes: establishing a stable set of the coincidence values of all construction nodes, and performing variance calculation on the stable set to obtain stable data WD of a matching object; obtaining a close coefficient TJ of the matched object through a formula TJ=β1xFH- (β2xPL+β3xWD), wherein the close coefficient is a numerical value reflecting the reasonable setting degree of the critical value of the matched object, and the larger the numerical value of the close coefficient is, the higher the setting rationality of the critical value of the matched object is; wherein β1, β2 and β3 are proportionality coefficients, and β3 > β2 > β1 > 1; marking a matched object with the largest proximate coefficient TJ value as a selected object, forming an optimal critical data set by a progress temporary high value and a progress temporary low value of each construction node in the selected object, transmitting the optimal critical data set to an acquisition and analysis platform, and transmitting the optimal critical data set to a mobile phone terminal of a manager after the acquisition and analysis platform receives the optimal critical data set; and analyzing and processing the optimal critical value of the detection object, and obtaining a close coefficient by carrying out data analysis on the progress completion quantity of the matching object at each construction node, so that the critical value setting reasonability of the matching object is fed back through the close coefficient, the node critical value data set of the matching object with the highest fitting degree is marked, and the marked data set is used as the optimal critical value of the detection object, so that the construction progress constraint is carried out on the detection object by the optimal critical value.
After receiving the optimal critical data set, the manager can further screen data in the optimal critical data set according to parameters of station construction methods, stage implementation contents (such as construction of civil engineering, construction of wall, decoration, installation of equipment, long-term implementation of equipment approach path, reconstruction engineering and the like), so that historical data closest to the detected object is obtained in the optimal critical data set, and an optimal reserved implementation scheme of the detected object is generated according to the closest historical data.
Example 2
As shown in fig. 2, the method for collecting and analyzing the optimal critical value based on the track traffic station reservation engineering comprises the following steps:
step one: detecting and analyzing the bearing capacity of the rail transit station: marking a rail transit station to be subjected to carrier quantity detection as a detection object, acquiring line data XL, coverage data FD and transfer data HC of the detection object, performing numerical calculation to obtain a volume coefficient TL, and screening a matched object by using the volume coefficient as a period management module to provide data support;
step two: management analysis is carried out on the construction period of the reserved engineering of the detection object: the method comprises the steps of obtaining a volume interval of a rail transit station, marking the volume interval matched with a volume coefficient TL of a detection object as a matching interval, obtaining an amount range and a period range of the detection object, screening the rail transit station through the matching interval, the period range and the amount range, obtaining the matching object, and improving the accuracy of obtaining an optimal critical value by carrying out critical value analysis on the matching object;
step three: analyzing and processing the optimal critical value of the detection object: obtaining coincidence data FH, deviation data PL and stable data WD of the matched objects, carrying out numerical calculation to obtain a close coefficient TJ, marking the matched object with the largest close coefficient TJ as a selected object, forming an optimal critical data set by a progress critical value and a progress critical value of each construction node in the selected object, feeding back the critical value setting rationality of the matched object through the close coefficient, and marking the node critical value data set of the matched object with the highest close degree.
The method comprises the steps that an optimal critical value acquisition and analysis system based on track traffic station reservation engineering marks a track traffic station to be subjected to carrier quantity detection as a detection object during operation, line data XL, coverage data FD and transfer data HC of the detection object are obtained, and a volume coefficient TL is obtained through numerical value calculation; the method comprises the steps of obtaining a volume interval of a rail transit station, marking the volume interval matched with a volume coefficient TL of a detection object as a matching interval, obtaining an amount range and a period range of the detection object, and screening the rail transit station through the matching interval, the period range and the amount range to obtain the matching object; and obtaining coincidence data FH, deviation data PL and stable data WD of the matched objects, carrying out numerical calculation to obtain a close coefficient TJ, marking the matched object with the largest close coefficient TJ as a selected object, and forming an optimal critical data set by a progress critical value and a progress critical value of each construction node in the selected object.
The foregoing is merely illustrative of the structures of this application and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the application or from the scope of the application as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula TL = α1 x xl+α2 x fd+α3 x cr; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding volume coefficient for each group of sample data; substituting the set volume coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are 3.74, 2.97 and 2.65 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding volume coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the volume coefficient is directly proportional to the value of the line data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (3)
1. The optimal critical value acquisition and analysis system based on the track traffic station reservation engineering is characterized by comprising an acquisition and analysis platform, wherein the acquisition and analysis platform is in communication connection with a body analysis module, a period management module, a critical analysis module and a storage module;
the body quantity analysis module is used for detecting and analyzing the bearing capacity of the rail transit station, obtaining a body quantity coefficient TL of a detection object, sending the body quantity coefficient TL of the detection object to the acquisition and analysis platform, and sending the body quantity coefficient TL to the period management module after the acquisition and analysis platform receives the body quantity coefficient TL;
the period management module is used for carrying out management analysis on the construction period of the reserved engineering of the detection object and obtaining a matched object; the method comprises the steps that a matching object is sent to an acquisition and analysis platform, and the acquisition and analysis platform receives the matching object and then sends the matching object to a critical analysis module;
the critical analysis module is used for analyzing and processing the optimal critical value of the detection object after receiving the historical construction data, obtaining an optimal critical data set, sending the optimal critical data set to the acquisition and analysis platform, and sending the optimal critical data set to a mobile phone terminal of a manager after the acquisition and analysis platform receives the optimal critical data set;
the specific process of the cycle management module for managing and analyzing the construction cycle of the reserved engineering of the detection object comprises the following steps: the method comprises the steps that a historical volume coefficient of all rail transit stations is obtained through a storage module, a volume range is formed by a maximum value of the historical volume coefficient and a minimum value of the historical volume coefficient, the volume range is divided into a plurality of volume intervals, the volume intervals matched with a volume coefficient TL of a detection object are marked as matching intervals, periodic data ZQ and sum data JE of the detection object are obtained, the periodic data ZQ is the total duration of reserved engineering construction periods of the detection object, and the sum data JE of the detection object is the total budget value of reserved engineering construction of the detection object; obtaining period thresholds ZQmax and ZQmin of a detection object through formulas ZQmax=t1×ZQ and ZQmin=t2×ZQ, and obtaining sum thresholds JEMax and JEMin of the detection object through formulas JEMin=t1×JE and JEMin=t2×JE, wherein t1 and t2 are proportionality coefficients, and t1 is more than or equal to 1.15 and less than or equal to 1.25,0.75 and t2 is less than or equal to 0.85; the method comprises the steps that a period range is formed by a period threshold ZQmax and ZQmin, an amount range is formed by an amount threshold JEMin and JEMax, and a track traffic station with a history volume coefficient in a matching interval, amount data in the amount range and period data in the period range is marked as a matching object;
the specific process of analyzing and processing the optimal critical value of the detection object by the critical analysis module comprises the following steps: carrying out construction period segmentation on the construction period of the matched object to obtain a plurality of construction nodes, carrying out numerical conversion on the construction progress in percentage by the construction node division of the construction period, obtaining a progress elevation value and a progress depression value of the construction nodes, obtaining the construction progress value of the matched object on the construction nodes, judging whether the construction progress value is positioned between the progress elevation value and the progress depression value, and if so, marking the corresponding construction nodes as conforming nodes; if not, marking the corresponding construction node as a deviation node; obtaining coincidence data FH, deviation data PL and stable data WD of a matching object, and performing numerical calculation to obtain a close coefficient TJ of the matching object; marking a matched object with the largest proximate coefficient TJ value as a selected object, and forming an optimal critical data set by a progress critical value and a progress critical value of each construction node in the selected object;
the process for acquiring the coincidence data FH of the matching object comprises the following steps: marking an average value of a progress temporary high value and a progress temporary low value of a construction node as a progress intermediate value, marking an absolute value of a difference value between the construction progress value and the progress intermediate value of the construction node as a coincidence value of the construction node, summing all coincidence values of the coincidence nodes, and taking the average value to obtain coincidence data FH; the acquisition process of the offset data PL of the matching object includes: judging whether the construction progress value of the deviated node is smaller than the progress critical low value, if so, marking the difference value between the progress critical low value and the construction progress value as the deviated value of the deviated node; if not, marking the difference value between the construction progress value and the progress temporary value as a deviation value of a deviation node; summing the deviation values of all the deviation nodes and averaging to obtain deviation data PL; the process for acquiring the stable data WD of the matching object includes: and establishing a stable set of the coincidence values of all the construction nodes, and performing variance calculation on the stable set to obtain stable data WD of the matched object.
2. The optimal critical value acquisition and analysis system based on the track traffic station reservation engineering according to claim 1, wherein the specific process of detecting and analyzing the carrier quantity of the track traffic station by the body quantity analysis module comprises the following steps: marking a rail transit station to be subjected to carrier quantity detection as a detection object, and acquiring line data XL, coverage data FD and transfer data HC of the detection object; the volume coefficient TL of the detection target is obtained by performing numerical calculation on the line data XL, the coverage data FD, and the transfer data HC.
3. The optimal threshold value acquisition and analysis system based on the rail transit station reservation project according to claim 2, wherein the line data XL of the detection object is a rail line number value passing through the detection object; the acquisition process of the coverage data FD of the detection object includes: drawing a circle by taking a detection object as a circle center and r1 as a radius, marking the obtained circular area as a coverage area, and marking the number of rail transit stations in the coverage area as coverage data FD; the transfer data HC of the detection object is a sum of the number of stairs and the number of stairs from the hall to the platform of the detection object.
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