CN117591502A - Channel control network data management method and system - Google Patents
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Abstract
The application relates to a channel control network data management method and a system, wherein the channel control network data management method comprises the following steps: channel control point information is acquired and is input into a preset control point information database; collecting control point monitoring data corresponding to a channel control point, and inputting the control point monitoring data into a preset control point monitoring database; calling channel control point information data in the control point information database, and inputting the channel control point information data into a preset channel calculation model; the channel calculation module is used for generating channel control network data based on big data analysis and inputting the channel control network data into a preset channel control network database.
Description
Technical Field
The present disclosure relates to the field of channel management, and in particular, to a method and system for managing channel control network data.
Background
Most of established control point achievement management systems in the prior art can only operate the control point data managed on an internal network of an archive, and the control point data can not operate on a government external network, and because of the limitation of an internal network, the data volume is small, and the data processing and analysis are generally carried out manually.
With the concept of "digital Guangdong", control point information of multiple databases needs to be integrated, and then as the data volume increases, errors may be introduced in the data processing process by manual operation and conventional data processing tools. For example, errors may occur during data entry, calculation errors may exist during data conversion, and accuracy of channel information calculation is reduced, so improvement is needed.
Disclosure of Invention
In order to improve the accuracy of channel information calculation, the application provides a channel control network data management method and system.
In a first aspect, the above object of the present application is achieved by the following technical solutions:
a channel control network data management method comprises the following steps:
channel control point information is acquired and is input into a preset control point information database;
collecting control point monitoring data corresponding to a channel control point, and inputting the control point monitoring data into a preset control point monitoring database;
calling channel control point information data in the control point information database, and inputting the channel control point information data into a preset channel calculation model;
and the channel calculation module is used for generating channel control network data based on big data analysis and inputting the channel control network data into a preset channel control network database.
By adopting the technical scheme, the data accuracy is improved: the automatic data acquisition and standardization can reduce human errors and data loss, and improve the accuracy and the integrity of the data; and improving data consistency: through data standardization and consistency measures, channel control network data from different sources can be processed and compared according to a unified standard, so that the consistency and comparability of the data are improved; optimizing data quality control: the data quality control method is introduced, such as abnormal value detection and correction, so that abnormal data in channel control network data can be identified and corrected, and the data quality and reliability are improved; data integration and sharing are realized: through data integration and sharing, different departments and stakeholders can share channel control network data, so that information circulation and collaborative operation are promoted, and channel management efficiency and coordination are improved; support data analysis and application: the data analysis and application module can perform data exploration, association analysis, prediction, optimization and the like, help discover channel characteristics and trends, and support channel management decision and planning; technical support and training are provided: by providing technical support and training, channel management personnel can master data management and analysis skills, and can better utilize channel control network data to manage and make decisions; maintaining system updates and maintenance: and the channel control network data management system is updated and maintained regularly, so that the stability and functional completeness of the system are ensured, and the system is suitable for the continuously-changing channel management requirements.
The present application may be further configured in a preferred example to: acquiring control point monitoring data corresponding to a channel control point, and inputting the control point monitoring data into a preset control point monitoring database, wherein the control point monitoring data comprises geographical position information of the channel, water depth data corresponding to the control point, the positions of channel marks and markers, curves and turning points of the channel and channel barriers in a control point monitoring range; the equipment for collecting data comprises GPS measurement, a laser range finder and a channel measurement ship.
By adopting the technical scheme, various monitoring devices and multidimensional acquisition control points monitor data, and the diversity of the monitoring data is improved.
The present application may be further configured in a preferred example to: after the step of collecting control point monitoring data corresponding to the channel control points and inputting the control point monitoring data into a preset control point monitoring database, the method comprises the following steps:
detecting and identifying abnormal values of the control point monitoring database;
transmitting an error report of the abnormal value of the control point monitoring database;
correcting or eliminating the abnormal value of the control point monitoring database, and generating an error correction report according to the correcting or eliminating action.
By adopting the technical scheme, the abnormal value is identified and corrected, the reliability and the accuracy of the data are improved, and the interference of the abnormal value on an analysis result is avoided.
The present application may be further configured in a preferred example to: after the channel calculation module generates channel control network data based on big data analysis and inputs the channel control network data into a preset channel control network database, the method comprises the following steps:
calling channel control network data of the channel control network database, and inputting the channel control network data into a preset data visualization model;
the data visualization model generates a channel image based on the channel control network data.
By adopting the technical scheme, the channel control network data is visually displayed, so that a user is helped to intuitively know the topological structure and the spatial distribution of the channel, and the channel management decision and the planning are facilitated.
The present application may be further configured in a preferred example to: the method comprises the steps of collecting control point monitoring data corresponding to a channel control point and inputting the control point monitoring data into a preset control point monitoring database, and comprises the following steps:
and grouping the control point monitoring data in the control point monitoring database based on a clustering algorithm, and classifying similar data.
By adopting the technical scheme, the control point monitoring data are grouped by using a clustering algorithm, and similar data are classified together to form a data set, so that subsequent data calling and optimization are facilitated.
The present application may be further configured in a preferred example to: after the channel calculation module generates channel control network data based on big data analysis and inputs the channel control network data into a preset channel control network database, the method comprises the following steps:
analyzing historical data and trends in a channel control network database based on a regression algorithm, and predicting the change of the channel length;
optimizing the channel control network data based on an optimization algorithm and an analog simulation method, and generating an optimization report.
By adopting the technical scheme, the change of the channel length is predicted, planning of channel extension and maintenance plans is facilitated, and safety and sustainable development of the channel are ensured.
In a second aspect, the above object of the present application is achieved by the following technical solutions:
the channel control network information management system comprises an acquisition equipment module, a control point information database module, a control point monitoring database module, a channel calculation model module and a channel control network information database module, wherein the acquisition equipment module is used for acquiring channel control point information and inputting the channel control point information into a preset control point information database module;
the control point information database module is used for presetting a control point information database;
the control point monitoring database module is used for presetting a control point monitoring database;
the channel calculation model module is used for presetting a channel calculation model;
the channel control network information base module is used for presetting a channel control network information base.
By adopting the technical scheme, the data accuracy is improved: the automatic data acquisition and standardization can reduce human errors and data loss, and improve the accuracy and the integrity of the data; and improving data consistency: through data standardization and consistency measures, channel control network data from different sources can be processed and compared according to a unified standard, so that the consistency and comparability of the data are improved; optimizing data quality control: the data quality control method is introduced, such as abnormal value detection and correction, so that abnormal data in channel control network data can be identified and corrected, and the data quality and reliability are improved; data integration and sharing are realized: through data integration and sharing, different departments and stakeholders can share channel control network data, so that information circulation and collaborative operation are promoted, and channel management efficiency and coordination are improved; support data analysis and application: the data analysis and application module can perform data exploration, association analysis, prediction, optimization and the like, help discover channel characteristics and trends, and support channel management decision and planning; technical support and training are provided: by providing technical support and training, channel management personnel can master data management and analysis skills, and can better utilize channel control network data to manage and make decisions; maintaining system updates and maintenance: and the channel control network data management system is updated and maintained regularly, so that the stability and functional completeness of the system are ensured, and the system is suitable for the continuously-changing channel management requirements.
Optionally, the system also comprises a control point monitoring data correction module and a data visualization model module,
the control point monitoring data correction module is used for correcting or eliminating abnormal values of the control point monitoring database and generating an error correction report according to the correction or elimination actions;
the data visualization model module is used for generating a channel image based on the channel control network data.
By adopting the technical scheme, the abnormal values are identified and corrected, the reliability and the accuracy of the data are improved, the interference of the abnormal values on the analysis result is avoided, the channel control network data are visually displayed, users are helped to intuitively know the topological structure and the spatial distribution of the channel, and the channel management decision and the planning are facilitated.
In a third aspect, the above object of the present application is achieved by the following technical solutions:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a channel control network data management method as described above when the computer program is executed.
In a fourth aspect, the above object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which when executed by a processor performs the steps of a channel control network data management method as described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. improving data accuracy: the automatic data acquisition and standardization can reduce human errors and data loss, and improve the accuracy and the integrity of the data; and improving data consistency: through data standardization and consistency measures, channel control network data from different sources can be processed and compared according to a unified standard, so that the consistency and comparability of the data are improved; optimizing data quality control: the data quality control method is introduced, such as abnormal value detection and correction, so that abnormal data in channel control network data can be identified and corrected, and the data quality and reliability are improved; data integration and sharing are realized: through data integration and sharing, different departments and stakeholders can share channel control network data, so that information circulation and collaborative operation are promoted, and channel management efficiency and coordination are improved; support data analysis and application: the data analysis and application module can perform data exploration, association analysis, prediction, optimization and the like, help discover channel characteristics and trends, and support channel management decision and planning; technical support and training are provided: by providing technical support and training, channel management personnel can master data management and analysis skills, and can better utilize channel control network data to manage and make decisions; maintaining system updates and maintenance: the channel control network data management system is updated and maintained regularly, so that the stability and functional completeness of the system are ensured, and the system is suitable for the continuously-changing channel management requirements;
2. grouping the control point monitoring data in the control point monitoring database based on a clustering algorithm, and classifying similar data;
3. the abnormal values are identified and corrected, the reliability and the accuracy of the data are improved, the interference of the abnormal values on the analysis result is avoided, the channel control network data are visually displayed, users are helped to intuitively know the topological structure and the spatial distribution of the channel, and channel management decision and planning are facilitated.
Drawings
Fig. 1 is a schematic flow chart of steps of a channel control network data management method according to an embodiment of the present application;
fig. 2 is a schematic flow chart in step S20 in a channel control network data management method according to an embodiment of the present application;
fig. 3 is a schematic flow chart after step 20 in a channel control network data management method according to an embodiment of the present application;
fig. 4 is a schematic flow chart after step S40 in a channel control network data management method according to an embodiment of the present application;
FIG. 5 is a schematic block diagram of a channel control network data management system according to an embodiment of the present application;
fig. 6 is a schematic diagram of an internal structure of a computer device according to an embodiment of the present application.
Reference numerals illustrate:
1. a collection device module; 2. a control point information database module; 3. the control point monitors the database module; 4. a channel calculation model module; 5. a channel control network information base module; 6. the control point monitoring data correction module; 7. and a data visualization model module.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
In an embodiment, as shown in fig. 1-5, the application discloses a channel control network data management method, which specifically includes the following steps:
a channel control network data management method comprises the following steps:
s10: channel control point information is acquired and is input into a preset control point information database;
specifically, in the embodiment of the application, the control point information is recorded according to a 1980 western security coordinate system and a 2000 national geodetic coordinate system in the range of Liguangdong province, and based on the Internet, each monitoring unit shares the control point information, compared with the existing most established control point result management system, the managed control point data can only run on the internal network of an archive, so that each monitoring unit has an information barrier.
S20, collecting control point monitoring data corresponding to a channel control point, and inputting the control point monitoring data into a preset control point monitoring database;
the equipment for collecting the monitoring data of the control points comprises GPS measurement, a laser range finder and a channel measurement ship; the control point monitoring data comprise geographical position information of the channel, water depth data corresponding to the control point, positions of channel marks and markers, curves and turning points of the channel and channel barriers in a monitoring range of the control point.
Specifically, when the control point monitoring data acquisition device is used for acquiring the control point monitoring data, the technical basis is as follows: basic technical regulations for national geodetic survey-GB 22021-2008; global Positioning System (GPS) measurement specifications-GB/T18314-2009; global positioning system real-time kinematic (RTK) specifications-GH/T2009-2010; notification about the implementation of the printing-enabled 2000 national coordinate system (national survey national word [ 2008 ] No. 24) and its accessories, conversion of existing mapping achievements to the 2000 national geodetic coordinate system technical guide; "Infinite letters (test office letters [ 2013 ] 66 number) and annex 1 thereof" technical guidelines for popularization and use of 2000 national geodetic coordinate System "and" geodetic control Point coordinate conversion technical Specification ": technical guidelines for the popularization and use of the 2000 national geodetic coordinate system, annex 2: technical regulations of coordinate conversion of geodetic control points; mapping technical design rules (CH/T1004-2005); quality inspection and acceptance of mapping achievements-GB/T24356-2009.
S30: calling channel control point information data in the control point information database, and inputting the channel control point information data into a preset channel calculation model;
s40: the channel calculation module generates channel control network data based on big data analysis and inputs the channel control network data into a preset channel control network database;
improving data accuracy: the automatic data acquisition and standardization can reduce human errors and data loss, and improve the accuracy and the integrity of the data; and improving data consistency: through data standardization and consistency measures, channel control network data from different sources can be processed and compared according to a unified standard, so that the consistency and comparability of the data are improved; optimizing data quality control: the data quality control method is introduced, such as abnormal value detection and correction, so that abnormal data in channel control network data can be identified and corrected, and the data quality and reliability are improved; data integration and sharing are realized: through data integration and sharing, different departments and stakeholders can share channel control network data, so that information circulation and collaborative operation are promoted, and channel management efficiency and coordination are improved; support data analysis and application: the data analysis and application module can perform data exploration, association analysis, prediction, optimization and the like, help discover channel characteristics and trends, and support channel management decision and planning; technical support and training are provided: by providing technical support and training, channel management personnel can master data management and analysis skills, and can better utilize channel control network data to manage and make decisions; maintaining system updates and maintenance: and the channel control network data management system is updated and maintained regularly, so that the stability and functional completeness of the system are ensured, and the system is suitable for the continuously-changing channel management requirements.
At S20: the method comprises the steps of collecting control point monitoring data corresponding to a channel control point and inputting the control point monitoring data into a preset control point monitoring database, and comprises the following steps:
s21: grouping the control point monitoring data in the control point monitoring database based on a clustering algorithm, and classifying similar data;
specifically, the control point monitoring data are grouped by using a clustering algorithm, and similar data are classified together to form a data set, so that subsequent data calling and optimization are facilitated.
At S20: after the step of collecting control point monitoring data corresponding to the channel control points and inputting the control point monitoring data into a preset control point monitoring database, the method comprises the following steps:
s201: detecting and identifying abnormal values of the control point monitoring database;
specifically, a difference interval (threshold) between two sets of data is set, for example: it is assumed that the values of a certain feature in the dataset obey a normal distribution. According to the nature of normal distribution, a threshold is defined beyond which data exceeding the mean plus or minus 3 standard deviations can be considered outliers in embodiments of the present application. And comparing the absolute value with a set difference interval through difference operation and calculating the absolute value, judging that the data is abnormal if the absolute value is positioned outside the difference interval, filling the missing value by adopting methods such as interpolation, deletion or substitution, and the like, and ensuring the integrity and the accuracy of the data.
S202: transmitting an error report of the abnormal value of the control point monitoring database;
specifically, a corresponding error report is generated, wherein the error report comprises the corresponding control point, the control point monitoring data and the calculated absolute value.
S203: correcting or eliminating the abnormal value of the control point monitoring database, and generating an error correction report according to the correcting or eliminating action.
Specifically, in the embodiment of the present application, the correction method is to replace the outlier with the mean of the feature.
At S40: the channel calculation module generates channel control network data based on big data analysis and inputs the channel control network data into a preset channel control network database, and then comprises the following steps:
s401: analyzing historical data and trends in a channel control network database based on a regression algorithm, and predicting the change of the channel length;
wherein the regression algorithm is a linear regression model: a linear regression model is a common prediction method used to establish a linear relationship between independent and dependent variables. For channel length prediction, channel length in the historical data can be used as a dependent variable, and other related factors (such as channel use frequency, cargo traffic, ship type, etc.) can be used as independent variables. The formula of the linear regression model is as follows: predicted value = coefficient 1 x independent variable 1 + coefficient 2 x independent variable 2+ intercept; by fitting the historical data, coefficients and intercepts for each independent variable can be obtained, and then a model is used to predict future channel lengths.
A time series model is also provided: the time series model is a prediction method for time-related data, and can capture the trend and seasonal change of the data. For channel length prediction, ARIMA (autoregressive moving average) model or other time series model may be used. The ARIMA model is formulated as follows: xt=c+Φ1 x t-1+Φ2 x t-2+Φp x t-p+θ1 x t-1+θ2 x t-2+θq x t-q+εt, where Xt represents the channel length at time t, c is a constant, Φ1, Φ2, Φp is the coefficient of the autoregressive term, θ1, θ2, θq is the coefficient of the moving average term, and εt is the error term. Parameters of the ARIMA model can be obtained by fitting the historical data, and then the model is used for predicting the future channel length.
It should be noted that the selection of the appropriate predictive model and algorithm requires evaluation and comparison based on the characteristics of the data and the predicted goals. In practice, statistical software or programming languages (such as statsmode libraries in Python) may be used to implement different predictive models, and the best model may be selected based on how well the model fits and the accuracy of the predictions. In addition, machine learning algorithms (e.g., decision trees, random forests, neural networks, etc.) may also be used to predict channel lengths.
And S402, optimizing the channel control network data based on an optimization algorithm and an analog simulation method, and generating an optimization report.
In the embodiment of the application, the optimization algorithm and the simulation method are as follows:
multi-objective optimization: channel design and management involves a number of objectives such as maximizing vessel throughput, minimizing channel costs, maximizing channel safety, etc. The multi-objective optimization method may help find a set of optimal solutions so that a balance is achieved between the objectives. The multi-objective optimization method comprises genetic algorithm and particle swarm optimization.
Simulation and emulation: the performance of different channel design and management strategies can be evaluated using simulation and emulation techniques. By constructing a model and running simulation experiments, the running condition of the channel is simulated, the effects of different strategies are evaluated, and comparison and optimization are performed. Simulation is used to evaluate the influence of parameters such as different channel widths, depths, curve radii and the like on the ship passing capacity and channel safety.
The data driving method comprises the following steps: rules and modes can be mined from historical data by utilizing big data and data analysis technology, and guidance is provided for channel design and management. For example, channel usage data and ship transportation data may be analyzed, bottlenecks and shortcuts identified, and corresponding improvements made. The data driven approach may also be applied to optimization of channel management decisions, such as using real-time data and predictive models to adjust channel traffic plans and resource allocation.
S403, calling channel control network data of the channel control network database, and inputting the channel control network data into a preset data visualization model;
s404, the data visualization model generates a channel image based on the channel control network data.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
In an embodiment, a channel control network information management system is provided, where the channel control network information management system corresponds to one channel control network information management method in the above embodiment. As shown in fig. 5, the channel control network information management system comprises an acquisition device module 1, a control point information database module 2, a control point monitoring database module 3, a channel calculation model module 4, a channel control network information database module 5, a control point monitoring data correction module 6 and a data visualization model module 7,
the acquisition equipment module 1 is used for acquiring channel control point information and inputting the channel control point information into the preset control point information database module 2;
the control point information database module 2 is used for presetting a control point information database;
the control point monitoring database module 3 is used for presetting a control point monitoring database;
the channel calculation model module 4 is used for presetting a channel calculation model;
the channel control network information base module 5 is used for presetting a channel control network information base.
The control point monitoring data correction module 6 is used for correcting or eliminating abnormal values of the control point monitoring database and generating an error correction report according to the correction or elimination actions;
the data visualization model module 7 is used for generating a channel image based on the channel control network data.
For a specific limitation of a channel control network information management system, reference may be made to the limitation of a channel control network information management method hereinabove, and the description thereof will not be repeated here. The modules in the channel control network information management system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing first data to be analyzed and second data to be analyzed of a channel control network information management system, a channel control network information management system detection result and a channel control network information management system operation report. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a channel control network information management method.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
in one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others. It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A channel control network data management method is characterized in that: the method comprises the following steps:
channel control point information is acquired and is input into a preset control point information database;
collecting control point monitoring data corresponding to a channel control point, and inputting the control point monitoring data into a preset control point monitoring database;
calling channel control point information data in the control point information database, and inputting the channel control point information data into a preset channel calculation model;
and the channel calculation module is used for generating channel control network data based on big data analysis and inputting the channel control network data into a preset channel control network database.
2. The method for managing channel control network data according to claim 1, wherein: acquiring control point monitoring data corresponding to a channel control point, and inputting the control point monitoring data into a preset control point monitoring database, wherein the control point monitoring data comprises geographical position information of the channel, water depth data corresponding to the control point, the positions of channel marks and markers, curves and turning points of the channel and channel barriers in a control point monitoring range; the equipment for collecting data comprises GPS measurement, a laser range finder and a channel measurement ship.
3. The method for managing channel control network data according to claim 1, wherein: after the step of collecting control point monitoring data corresponding to the channel control points and inputting the control point monitoring data into a preset control point monitoring database, the method comprises the following steps:
detecting and identifying abnormal values of the control point monitoring database;
transmitting an error report of the abnormal value of the control point monitoring database;
correcting or eliminating the abnormal value of the control point monitoring database, and generating an error correction report according to the correcting or eliminating action.
4. A channel control network data management method according to claim 3, characterized in that: after the channel calculation module generates channel control network data based on big data analysis and inputs the channel control network data into a preset channel control network database, the method comprises the following steps:
calling channel control network data of the channel control network database, and inputting the channel control network data into a preset data visualization model;
the data visualization model generates a channel image based on the channel control network data.
5. The method for managing channel control network data according to claim 4, wherein: the method comprises the steps of collecting control point monitoring data corresponding to a channel control point and inputting the control point monitoring data into a preset control point monitoring database, and comprises the following steps:
and grouping the control point monitoring data in the control point monitoring database based on a clustering algorithm, and classifying similar data.
6. The method for managing channel control network data according to claim 5, wherein: after the channel calculation module generates channel control network data based on big data analysis and inputs the channel control network data into a preset channel control network database, the method comprises the following steps:
analyzing historical data and trends in a channel control network database based on a regression algorithm, and predicting the change of the channel length;
optimizing the channel control network data based on an optimization algorithm and an analog simulation method, and generating an optimization report.
7. A channel control network data management system, applied to a channel control network data management method as claimed in any one of claims 1 to 6, characterized in that: the system comprises an acquisition equipment module (1), a control point information database module (2), a control point monitoring database module (3), a channel calculation model module (4) and a channel control network information database module (5), wherein the acquisition equipment module (1) is used for acquiring channel control point information and inputting the channel control point information into the preset control point information database module (2);
the control point information database module (2) is used for presetting a control point information database;
the control point monitoring database module (3) is used for presetting a control point monitoring database;
the channel calculation model module (4) is used for presetting a channel calculation model;
the channel control network information base module (5) is used for presetting a channel control network information base.
8. A channel control network data management system as claimed in claim 7, wherein: the system also comprises a control point monitoring data correction module (6) and a data visualization model module (7),
the control point monitoring data correction module (6) is used for correcting or eliminating abnormal values of the control point monitoring database and generating an error correction report according to the correction or elimination actions;
the data visualization model module (7) is used for generating a channel image based on the channel control network data.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of a channel control network data management method according to any one of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a channel control network data management method according to any one of claims 1 to 6.
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