CN118364431B - Diagnostic system and method based on rail transit network energy consumption - Google Patents
Diagnostic system and method based on rail transit network energy consumption Download PDFInfo
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Abstract
The invention discloses a diagnosis system and a method based on rail transit network energy, which relate to the technical field of energy diagnosis and comprise a data acquisition module: the method is used for collecting the energy data and the environment data of the rail transit network of each monitoring point, the method comprises the steps of including network energy consumption parameters, equipment state parameters and environment state parameters; a diagnostic dimension establishing module: the system comprises a diagnosis dimension for establishing energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of a rail transit network; and a data analysis module: based on the collected line network energy consumption parameters, equipment operation parameters and environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, the abnormality is counted into an analysis result. The invention can realize the monitoring and diagnosis of the energy consumption condition of the rail transit network.
Description
Technical Field
The invention relates to the technical field of network energy diagnosis and provides a diagnosis system and method based on rail transit network energy.
Background
Urban rail transit has the characteristics of long line, large scale, multiple facilities, scattered energy utilization equipment layout and the like, and brings certain difficulty to energy data acquisition and management. At present, energy management systems are arranged in partial urban construction, basically, the systems are composed of electric energy meters, station-level communication management machines, energy management centers and communication channels which are arranged in high, medium and low voltage power distribution cabinets, and the systems are mainly used for replacing the traditional manual meter reading, increasing the data acquisition amount and reducing the workload of personnel. However, diagnosis and management on the dynamic energy utilization condition of a specific equipment system cannot be realized at present.
Disclosure of Invention
In order to solve at least one technical problem mentioned in the background art, the invention aims to provide a diagnosis system and a diagnosis method based on the energy consumption of a rail transit network, which can monitor and diagnose the energy consumption of the rail transit network.
In order to achieve the above object, the present invention provides the following technical solutions, including:
and a data acquisition module: the method is used for collecting the energy data and the environment data of the rail transit network of each monitoring point, the method comprises the steps of including network energy consumption parameters, equipment state parameters and environment state parameters;
A diagnostic dimension establishing module: the system comprises a diagnosis dimension for establishing energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of a rail transit network;
And a data analysis module: based on the collected line network energy consumption parameters, equipment operation parameters and environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, counting the abnormality into an analysis result;
a report generation module: and the analysis result is summarized into a diagnosis report corresponding to the diagnosis dimension, and the diagnosis report is uploaded to the system cloud platform for display, wherein the diagnosis report comprises a diagnosis object, a diagnosis range, the number of diagnosis items and the number of anomalies.
Further, the preset analysis flow in the data analysis module includes:
Presetting a household energy consumption diagnosis unit: the method comprises the steps that the actual energy consumption of a user in the network energy consumption parameters is greater than the planned energy consumption, is considered as abnormal, and is counted into an analysis result;
presetting a photovoltaic energy consumption diagnosis unit: the method is used for identifying abnormality when the fluctuation of the light Fu Yue power generation quantity in the network energy consumption parameter and the fluctuation of the power generation quantity in the same month of the last year are larger than 5%, and counting analysis results;
presetting an electric energy consumption diagnosis unit for traction: the method is used for identifying the traction power consumption in the network energy consumption parameters as abnormal when the traction power consumption is greater than the contemporaneous maximum value or less than the contemporaneous minimum value, and counting analysis results;
Presetting an energy-saving energy consumption diagnosis unit: the method is used for recognizing that the energy consumption after energy conservation is greater than the energy consumption before energy conservation in the network energy consumption parameters is abnormal and counting analysis results.
Further, the preset analysis flow in the data analysis module further includes:
presetting a photovoltaic energy efficiency diagnosis unit: the method is used for identifying abnormality when the light Fu Yue generated energy in the network energy consumption parameters is smaller than the same-period month generated energy, and counting analysis results;
A preset device energy efficiency diagnosis unit: the method is used for judging whether the month average COP of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is smaller than the month average COP of the same period on the basis of the operation parameters of the equipment, if so, the ground source heat pump set, the ventilation air conditioning set and the water chilling unit are considered to be abnormal, and analysis results are counted;
Presetting a wire network energy efficiency diagnosis unit: the method is used for identifying the wire network personnel energy consumption, wire network unit operation mileage traction electricity consumption, wire network unit passenger transport turnover traction electricity consumption, wire network unit building area movable illumination electricity consumption and wire network unit passenger transport water consumption in the wire network energy consumption parameters as abnormal when the wire network personnel energy consumption, the wire network unit operation mileage traction electricity consumption, the wire network unit passenger transport turnover traction electricity consumption and the wire network unit building area movable illumination electricity consumption are larger than index values, and counting analysis results.
Further, the preset analysis flow in the data analysis module further includes:
A preset bus voltage diagnosis unit: the method is used for identifying abnormality when the 110kV bus voltage deviation value in the network energy consumption parameter is more than 10% or less than-10%, and counting analysis results;
A preset traction voltage diagnosis unit: the method is used for identifying abnormality when the traction voltage deviation value in the net energy consumption parameter is more than 7% or less than-7%, and accounting the analysis result;
Presetting an illumination voltage diagnosis unit: the method is used for identifying abnormality when the dynamic illumination voltage deviation value in the line network energy consumption parameter is more than 7% or less than-10%, and accounting the analysis result;
A preset busbar frequency deviation diagnosis unit: the method is used for identifying abnormality when the frequency deviation value of the 110kV bus in the network energy consumption parameters is larger than 0.2Hz or smaller than-0.2 Hz, and counting analysis results;
Presetting a bus three-phase diagnosis unit: the method is used for identifying abnormality when the unbalance rate of the high-voltage side three-phase voltage of the 110kV bus in the network energy consumption parameters is more than 2%, and counting analysis results;
Presetting a busbar frequency power factor diagnosis unit: a110 kV busbar frequency power factor less than 0.85 used in the wire network energy consumption parameters is considered abnormal, and analysis results are counted.
Further, the preset analysis flow in the data analysis module further includes:
House weak current temperature diagnosis unit for preset equipment: the method comprises the steps that the method is used for identifying abnormality when the weak current temperature of equipment rooms in the environment parameters is higher than 27 ℃ and the frequency is higher than the set frequency, and analyzing results are counted;
strong room temperature diagnosis unit for preset equipment: the method comprises the steps that the equipment is used for recognizing abnormality when the strong electricity temperature of equipment per month in environmental parameters is larger than 36 ℃ and the number of the strong electricity temperature is larger than the set number of the strong electricity temperature, and analyzing results are counted;
a preset environmental temperature diagnosis unit: the system is used for identifying abnormality when the temperature of a station hall, a platform, a carriage, an interval, an outdoor room and a management room in the environment parameters is higher than 29 ℃ and the frequency is higher than the set frequency, and counting analysis results;
A preset environmental humidity diagnosis unit: the system is used for identifying abnormal conditions in the environment parameters when the monthly station hall, carriage, section, outdoor, management room, equipment room weak current and equipment room strong current humidity is more than 60% or less than 40% and the frequency is more than the set frequency, and counting analysis results;
a preset environmental carbon dioxide concentration diagnosis unit: the method comprises the steps of determining that the car is abnormal when the concentration of carbon dioxide in the car is larger than 1000mg/m in the environmental parameter and the number of times is larger than the set number of times, and counting analysis results;
A preset environment VOC concentration diagnostic unit: the method comprises the steps of identifying abnormality when the VOC concentration of a carriage in each month in environmental parameters is more than 450ppm and the number of the VOC concentration is more than a set number of the VOC concentration, and counting analysis results;
preset environmental TVOC concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the TVOC of a carriage in each month in environmental parameters is larger than 0.6mg/m and the number of the TVOC is larger than the set number of the TVOC, and counting analysis results;
Preset environment PM2.5 concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the PM2.5 in a carriage per month in the environmental parameter is more than 35 mu g/m of the solution and the number of times is more than a set number of times, and counting analysis results;
preset environment PM10 concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the compartment PM10 in the environment parameter is greater than 100 mug/m (m) and the number of times is greater than the set number of times, and counting analysis results;
a preset environmental formaldehyde concentration diagnosis unit: and the analysis result is counted when the concentration of the formaldehyde in the carriage per month in the environmental parameter is greater than 0.1mg/m of the solution and the number of times is greater than the set number of times.
A diagnosis method based on rail transit network energy comprises the following steps:
s1: collecting energy data and environment data of a rail transit network of each monitoring point, wherein the energy data and the environment data comprise network energy consumption parameters, equipment state parameters and environment state parameters;
s2: establishing diagnosis dimensions of energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of the rail transit network;
S3: based on the collected line network energy consumption parameters, the equipment running state and the environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, counting the abnormality into an analysis result;
s4: and summarizing the analysis result into a diagnosis report corresponding to the diagnosis dimension, and uploading the diagnosis report to a system cloud platform for display, wherein the diagnosis report comprises a diagnosis object, a diagnosis range, the number of diagnosis items and the number of anomalies.
Further, the step S3 includes the steps of:
s301: when the actual energy consumption of the individual users is greater than the planned energy consumption, the individual users are considered as abnormal, and analysis results are counted;
s302: when the fluctuation of the generated energy of the light Fu Yue and the fluctuation of the generated energy of the same month in the last year are larger than 5%, the fluctuation is considered as abnormal, and the analysis result is counted;
s303: when the traction electricity consumption is greater than the contemporaneous maximum value or less than the contemporaneous minimum value, the traction electricity consumption is determined to be abnormal, and analysis results are counted;
s304: and the energy consumption after energy conservation is greater than the energy consumption before energy conservation, which is regarded as abnormality, and the analysis result is counted.
Further, the step S3 further includes the steps of:
S305: when the generated energy of the light Fu Yue is smaller than the generated energy of the same month, the generated energy is determined to be abnormal, and the analysis result is counted;
s306: judging whether the month average COP of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is smaller than the month average COP of the same period on the basis of the operation parameters of the equipment, if so, determining that the equipment is abnormal, and counting the analysis result;
S307: and when the line network personnel energy consumption, the line network unit operation mileage traction power consumption, the line network unit passenger transport turnover traction power consumption, the line network unit building area movable illumination power consumption and the line network unit passenger transport power consumption are larger than the index value, determining that the analysis result is abnormal, and counting the analysis result.
Further, the step S3 further includes the steps of:
S308: when the voltage deviation value of the 110kV bus is more than 10% or less than-10%, the bus is judged to be abnormal, and the analysis result is counted;
s309: when the traction voltage deviation value is more than 7% or less than-7%, the traction voltage deviation value is judged to be abnormal, and analysis results are counted;
s310: when the variation value of the illumination voltage is more than 7% or less than-10%, the illumination voltage is judged to be abnormal, and the analysis result is counted;
s311: when the frequency deviation value of the 110kV bus is greater than 0.2Hz or less than-0.2 Hz, the bus is identified as abnormal, and the analysis result is counted;
S312: when the unbalance rate of the three-phase voltage at the high-voltage side of the 110kV bus is more than 2%, the three-phase voltage is determined to be abnormal, and the analysis result is counted;
s313: power factors less than 0.85 at 110kV bus frequency are considered abnormal and the analysis results are counted.
Further, the step S3 further includes the steps of:
s314: when the weak current temperature of the equipment room per month is higher than 27 ℃ and the frequency is higher than the set frequency, the equipment room is identified as abnormal, and analysis results are counted;
s315: when the strong electricity temperature of the equipment room per month is more than 36 ℃ and the frequency is more than the set frequency, the equipment room is identified as abnormal, and analysis results are counted;
S316: the method comprises the steps of identifying abnormality when the temperature of a station hall, a platform, a carriage, an interval, an outdoor and a management room is higher than 29 ℃ and the number of times is higher than a set number of times, and counting analysis results;
s317: the method comprises the steps of identifying abnormality when the humidity of a station hall, a carriage, an interval, outdoors, a management room, a weak current of a device room and a strong current of the device room is more than 60% or less than 40% and the frequency is more than a set frequency, and counting analysis results;
S318: when the concentration of carbon dioxide in the carriage per month is more than 1000mg/m (W/W) and the number of times is more than the set number of times, determining that the carriage is abnormal, and counting the analysis result;
S319: the method comprises the steps that when the VOC concentration of a carriage per month is more than 450ppm and the number of times is more than a set number of times, the carriage is judged to be abnormal, and analysis results are counted;
s320: when the concentration of the TVOC in the carriage is larger than 0.6mg/m and the frequency is larger than the set frequency, the carriage is judged to be abnormal, and the analysis result is counted;
s321: when the concentration of PM2.5 in the carriage per month is more than 35 mug/m and the number of times is more than the set number of times, the carriage PM2.5 is judged to be abnormal, and the analysis result is counted;
S322: the method comprises the steps that when the concentration of PM10 in a carriage per month is greater than 100 mug/m and the number of times is greater than the set number of times, the carriage is judged to be abnormal, and analysis results are counted;
S323: and when the concentration of the formaldehyde in the carriage per month is greater than 0.1mg/m and the number of times is greater than the set number of times, the carriage is judged to be abnormal, and the analysis result is counted.
Compared with the prior art, the invention has the beneficial effects that:
The invention can monitor and diagnose in real time, and can monitor the energy consumption condition and the environment state of the rail transit network in real time through the data acquisition module, thereby ensuring the operation safety and the operation efficiency. The system can evaluate the rail transit network from multiple angles, and is more comprehensive and accurate. The data analysis module can timely discover abnormal conditions, display abnormal information in a report form through the report generation module, provide decision support for operators, and avoid or reduce potential safety risks and economic losses. Through the system cloud platform, operators can conveniently check and analyze the diagnosis report, and centralized management and efficient utilization of information are realized. The invention provides a comprehensive and efficient scheme for the energy utilization and environment monitoring of the rail transit network, and improves the operation efficiency and safety of rail transit.
Drawings
FIG. 1 is a block diagram of a diagnostic system based on rail transit network energy according to an embodiment of the present invention;
FIG. 2 is a block diagram of a diagnostic system based on rail transit network energy according to a first embodiment of the present invention;
FIG. 3 is a flowchart of a diagnostic method based on rail transit network energy provided in a second embodiment of the present invention;
Fig. 4 is a flowchart of analysis of energy consumption diagnosis dimensions in a diagnosis method based on rail transit network energy according to a second embodiment of the present invention;
FIG. 5 is a flow chart of analysis of energy efficiency diagnostic dimensions in a diagnostic method based on rail transit network energy provided in a second embodiment of the present invention;
FIG. 6 is a flow chart of analysis of the energy quality diagnostic dimension in the method for diagnosing energy consumption based on the rail transit network according to the second embodiment of the present invention;
Fig. 7 is a flowchart of an analysis of environmental quality diagnosis dimensions in a diagnosis method based on rail transit network energy according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the present embodiment provides a diagnostic system based on rail transit network energy, including:
and a data acquisition module: the method is used for collecting the energy data and the environment data of the rail transit network of each monitoring point, the method comprises the steps of including network energy consumption parameters, equipment state parameters and environment state parameters;
a diagnostic dimension establishing module: the system is used for establishing diagnosis dimensions of energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of the rail transit network;
And a data analysis module: based on the collected line network energy consumption parameters, equipment operation parameters and environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, counting the abnormality into an analysis result;
A report generation module: and the analysis result is summarized into a diagnosis report corresponding to the diagnosis dimension, and the diagnosis report is uploaded to the system cloud platform for display, wherein the diagnosis report comprises a diagnosis object, a diagnosis range, the number of diagnosis items and the number of anomalies.
Specifically, the data acquisition module: the main task of the module is to collect energy data and environment data of the rail transit network from each monitoring point. Such data includes, but is not limited to, net energy consumption parameters, equipment status parameters, and environmental status parameters. By real-time or periodic data acquisition, a sufficient source of information can be provided for subsequent data analysis.
Specifically, the diagnostic dimension creation module: the module is responsible for establishing the diagnostic dimension of the energy and environment of the rail transit network. These dimensions include energy consumption diagnostics, energy efficiency diagnostics, energy quality diagnostics, and environmental quality diagnostics. These diagnostic dimensions provide a well-defined direction and framework for data analysis.
Specifically, the data analysis module: based on the collected line network energy consumption parameters, the equipment operation parameters and the environment parameters, the module can analyze the diagnosis dimension item by item according to a preset analysis flow. If the abnormal condition in the preset analysis flow is met, the abnormality is counted into the analysis result. Thus, the problems in the aspects of energy consumption and environment of the rail transit network can be timely found and solved.
Specifically, the report generation module: the module gathers the analysis results into a diagnosis report corresponding to the diagnosis dimension, and uploads the diagnosis report to the system cloud platform for display. These reports include key information such as diagnostic subjects, diagnostic scope, number of diagnostic items, and number of anomalies. In this way, the manager or related personnel can intuitively understand the status of the track traffic network and the environment, as well as possible problems.
Specifically, through data acquisition and real-time analysis, the energy consumption condition and the environment state of the rail transit network can be monitored in real time, and early warning is timely sent out when abnormality is found, so that a manager is helped to respond quickly.
Specifically, energy efficiency optimization: through energy consumption diagnosis and energy efficiency diagnosis, a manager can be helped to know the energy consumption efficiency and potential energy-saving space, so that the energy consumption strategy is optimized, and the operation cost is reduced.
Specifically, the environmental quality promotes: through the environment quality diagnosis, the environment problems in the rail transit network can be timely found and improved, and the comfort and satisfaction of passengers are improved.
Specifically, decision support: the generated diagnostic report provides decision support for managers, helps the managers to better understand network energy consumption and environmental conditions, and makes more scientific and reasonable operation and management strategies.
Referring to fig. 2, the preset analysis flow in the data analysis module includes:
Presetting a household energy consumption diagnosis unit: the method comprises the steps that the actual energy consumption of a user in the network energy consumption parameters is greater than the planned energy consumption, is considered as abnormal, and is counted into an analysis result;
presetting a photovoltaic energy consumption diagnosis unit: the method is used for identifying abnormality when the fluctuation of the light Fu Yue power generation quantity in the network energy consumption parameter and the fluctuation of the power generation quantity in the same month of the last year are larger than 5%, and counting analysis results;
presetting an electric energy consumption diagnosis unit for traction: the method is used for identifying the traction power consumption in the network energy consumption parameters as abnormal when the traction power consumption is greater than the contemporaneous maximum value or less than the contemporaneous minimum value, and counting analysis results;
Presetting an energy-saving energy consumption diagnosis unit: the method is used for recognizing that the energy consumption after energy conservation is greater than the energy consumption before energy conservation in the network energy consumption parameters is abnormal and counting analysis results.
Specifically, the wire-grid energy consumption is analyzed in the energy consumption diagnosis dimension:
And (3) diagnosing household energy consumption: the actual energy consumption of each user is monitored and compared to the planned energy consumption. If the actual energy consumption exceeds the planned energy consumption, it is considered to be abnormal.
Photovoltaic energy consumption diagnosis: and analyzing the fluctuation of the monthly power generation capacity of the photovoltaic system and comparing the fluctuation with the data of the previous year. If the fluctuation deviation exceeds 5%, it is regarded as abnormal.
And (3) traction electricity consumption diagnosis: real-time data of the traction power is monitored and compared with historical contemporaneous data. Traction electricity is considered abnormal if it exceeds a contemporaneous maximum or is less than a minimum.
And (3) energy conservation and energy consumption diagnosis: and comparing the energy consumption data before and after the implementation of the energy-saving measures. If the energy consumption after energy saving is increased instead, the energy consumption is regarded as abnormal.
The preset analysis flow in the data analysis module further comprises:
presetting a photovoltaic energy efficiency diagnosis unit: the method is used for identifying abnormality when the light Fu Yue generated energy in the network energy consumption parameters is smaller than the same-period month generated energy, and counting analysis results;
A preset device energy efficiency diagnosis unit: the method is used for judging whether the month average COP of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is smaller than the month average COP of the same period on the basis of the operation parameters of the equipment, if so, the ground source heat pump set, the ventilation air conditioning set and the water chilling unit are considered to be abnormal, and analysis results are counted;
Presetting a wire network energy efficiency diagnosis unit: the method is used for identifying the wire network personnel energy consumption, wire network unit operation mileage traction electricity consumption, wire network unit passenger transport turnover traction electricity consumption, wire network unit building area movable illumination electricity consumption and wire network unit passenger transport water consumption in the wire network energy consumption parameters as abnormal when the wire network personnel energy consumption, the wire network unit operation mileage traction electricity consumption, the wire network unit passenger transport turnover traction electricity consumption and the wire network unit building area movable illumination electricity consumption are larger than index values, and counting analysis results.
Specifically, the net and device energy efficiency are analyzed in the energy efficiency diagnosis dimension:
photovoltaic energy efficiency diagnosis: and (5) monitoring the month generating capacity of the photovoltaic system and comparing the month generating capacity with the contemporaneous month generating capacity. If the monthly power generation amount decreases, it is regarded as abnormal.
Device energy efficiency diagnosis: based on the equipment operation parameters, the energy efficiency (COP) of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is evaluated. If the average COP decreases, it is considered abnormal.
Diagnosing the net energy efficiency: and various energy consumption indexes of the wire network are analyzed, such as personnel energy consumption, traction electricity consumption per unit operation mileage and the like. If these indicators exceed a preset standard value, they are considered abnormal.
The preset analysis flow in the data analysis module further comprises:
A preset bus voltage diagnosis unit: the method is used for identifying abnormality when the 110kV bus voltage deviation value in the network energy consumption parameter is more than 10% or less than-10%, and counting analysis results;
A preset traction voltage diagnosis unit: the method is used for identifying abnormality when the traction voltage deviation value in the net energy consumption parameter is more than 7% or less than-7%, and accounting the analysis result;
Presetting an illumination voltage diagnosis unit: the method is used for identifying abnormality when the dynamic illumination voltage deviation value in the line network energy consumption parameter is more than 7% or less than-10%, and accounting the analysis result;
A preset busbar frequency deviation diagnosis unit: the method is used for identifying abnormality when the frequency deviation value of the 110kV bus in the network energy consumption parameters is larger than 0.2Hz or smaller than-0.2 Hz, and counting analysis results;
Presetting a bus three-phase diagnosis unit: the method is used for identifying abnormality when the unbalance rate of the high-voltage side three-phase voltage of the 110kV bus in the network energy consumption parameters is more than 2%, and counting analysis results;
Presetting a busbar frequency power factor diagnosis unit: a110 kV busbar frequency power factor less than 0.85 used in the wire network energy consumption parameters is considered abnormal, and analysis results are counted.
Specifically, the electrical parameters are analyzed in the quality of use diagnostic dimension:
Bus voltage diagnosis: the deviation value of the 110kV bus voltage is monitored. If the deviation exceeds + -10%, it is considered abnormal.
Traction voltage diagnosis: the deviation value of the traction voltage is analyzed. If the deviation exceeds + -7%, it is considered abnormal.
And (3) performing illumination voltage diagnosis: the deviation value of the illumination voltage is monitored. If the deviation exceeds + -10%, it is considered abnormal.
Bus frequency deviation diagnosis: and analyzing the deviation value of the 110kV bus frequency. An anomaly is considered if the deviation exceeds + -0.2 Hz.
Bus three-phase diagnosis: and (5) evaluating the unbalance rate of the three-phase voltage at the high-voltage side of the 110kV bus. If the unbalance rate exceeds 2%, it is regarded as abnormal.
Bus frequency power factor diagnosis: the power factor of the 110kV bus frequency was monitored. If the power factor is less than 0.85, it is considered abnormal.
The preset analysis flow in the data analysis module further comprises:
House weak current temperature diagnosis unit for preset equipment: the method comprises the steps that the method is used for identifying abnormality when the weak current temperature of equipment rooms in the environment parameters is higher than 27 ℃ and the frequency is higher than the set frequency, and analyzing results are counted;
strong room temperature diagnosis unit for preset equipment: the method comprises the steps that the equipment is used for recognizing abnormality when the strong electricity temperature of equipment per month in environmental parameters is larger than 36 ℃ and the number of the strong electricity temperature is larger than the set number of the strong electricity temperature, and analyzing results are counted;
a preset environmental temperature diagnosis unit: the system is used for identifying abnormality when the temperature of a station hall, a platform, a carriage, an interval, an outdoor room and a management room in the environment parameters is higher than 29 ℃ and the frequency is higher than the set frequency, and counting analysis results;
A preset environmental humidity diagnosis unit: the system is used for identifying abnormal conditions in the environment parameters when the monthly station hall, carriage, section, outdoor, management room, equipment room weak current and equipment room strong current humidity is more than 60% or less than 40% and the frequency is more than the set frequency, and counting analysis results;
a preset environmental carbon dioxide concentration diagnosis unit: the method comprises the steps of determining that the car is abnormal when the concentration of carbon dioxide in the car is larger than 1000mg/m in the environmental parameter and the number of times is larger than the set number of times, and counting analysis results;
A preset environment VOC concentration diagnostic unit: the method comprises the steps of identifying abnormality when the VOC concentration of a carriage in each month in environmental parameters is more than 450ppm and the number of the VOC concentration is more than a set number of the VOC concentration, and counting analysis results;
preset environmental TVOC concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the TVOC of a carriage in each month in environmental parameters is larger than 0.6mg/m and the number of the TVOC is larger than the set number of the TVOC, and counting analysis results;
Preset environment PM2.5 concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the PM2.5 in a carriage per month in the environmental parameter is more than 35 mu g/m of the solution and the number of times is more than a set number of times, and counting analysis results;
preset environment PM10 concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the compartment PM10 in the environment parameter is greater than 100 mug/m (m) and the number of times is greater than the set number of times, and counting analysis results;
a preset environmental formaldehyde concentration diagnosis unit: and the analysis result is counted when the concentration of the formaldehyde in the carriage per month in the environmental parameter is greater than 0.1mg/m of the solution and the number of times is greater than the set number of times.
Specifically, environmental parameters are analyzed in an environmental quality diagnostic dimension:
room temperature diagnostic for equipment: the weak and strong electrical temperatures of the utility room are monitored. If the temperature exceeds a preset threshold and the number exceeds a set number, then the abnormality is considered.
And (3) diagnosing the ambient temperature: environmental temperatures in different areas such as a station hall, a platform, a carriage and the like are monitored. If the temperature exceeds 29 ℃ and the number of times exceeds the set number of times, it is regarded as abnormal.
And (3) diagnosing the environmental humidity: the humidity of each zone was evaluated. If the humidity exceeds 60% or is lower than 40% and the number of times exceeds the set number of times, it is regarded as an abnormality.
Air quality diagnosis: carbon dioxide, VOC, TVOC, PM 2.5.2, PM10 and formaldehyde concentrations in the vehicle cabin were monitored. If the concentration exceeds a preset threshold and the number exceeds a set number, then it is considered abnormal.
Specifically, abnormal conditions of the network energy consumption and the environment parameters can be monitored and diagnosed in real time through a plurality of preset diagnosis units. These anomalies may mean problems with energy inefficiency, equipment failure, or insufficient environmental comfort. Energy efficiency optimization: by identifying and analyzing the abnormal energy consumption, the scheme can help the rail transit system to optimize the energy consumption strategy, reduce the energy consumption and improve the energy efficiency, thereby saving the cost. And (3) improving the environmental quality: through monitoring environmental parameters, the scheme can timely find and solve environmental problems such as overhigh temperature, overlarge humidity, poor air quality and the like, thereby improving the comfort level of passengers and staff. Decision support: the generated diagnostic report provides rich data and information, provides decision support for the manager, and helps them better understand and improve the performance and environmental aspects of the net.
The system performs one-key management diagnosis function based on energy consumption diagnosis, and realizes a plurality of diagnosis dimensions including energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis. According to the preset analysis flow and the diagnosed data, the problems are automatically found out, the reasons are analyzed, the problems can be canceled in the diagnosis process, the detailed content analysis can be checked for the diagnosed content items, and the PDF file generation of the diagnosis report is supported. Diagnostic report includes a subject of diagnosis, a diagnostic scope, the number of diagnostic items, and the number of abnormalities.
The system can monitor and diagnose in real time, and can monitor the energy consumption condition and the environment state of the rail transit network in real time through the data acquisition module, thereby ensuring the operation safety and the operation efficiency. The system can evaluate the rail transit network from multiple angles, and is more comprehensive and accurate. The data analysis module can timely discover abnormal conditions, display abnormal information in a report form through the report generation module, provide decision support for operators, and avoid or reduce potential safety risks and economic losses. Through the system cloud platform, operators can conveniently check and analyze the diagnosis report, and centralized management and efficient utilization of information are realized. The invention provides a comprehensive and efficient scheme for the energy utilization and environment monitoring of the rail transit network, and improves the operation efficiency and safety of rail transit.
Example two
Referring to fig. 3, the embodiment provides a diagnosis method based on rail transit network energy, which includes the steps of:
s1: collecting energy data and environment data of a rail transit network of each monitoring point, wherein the energy data and the environment data comprise network energy consumption parameters, equipment state parameters and environment state parameters;
S2: establishing diagnosis dimensions of energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of the rail transit network;
S3: based on the collected line network energy consumption parameters, the equipment running state and the environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, counting the abnormality into an analysis result;
S4: and summarizing the analysis result into a diagnosis report corresponding to the diagnosis dimension, and uploading the diagnosis report to a system cloud platform for display, wherein the diagnosis report comprises a diagnosis object, a diagnosis range, the number of diagnosis items and the number of anomalies.
Referring to fig. 4, S3 includes the steps of:
s301: when the actual energy consumption of the individual users is greater than the planned energy consumption, the individual users are considered as abnormal, and analysis results are counted;
s302: when the fluctuation of the generated energy of the light Fu Yue and the fluctuation of the generated energy of the same month in the last year are larger than 5%, the fluctuation is considered as abnormal, and the analysis result is counted;
s303: when the traction electricity consumption is greater than the contemporaneous maximum value or less than the contemporaneous minimum value, the traction electricity consumption is determined to be abnormal, and analysis results are counted;
s304: and the energy consumption after energy conservation is greater than the energy consumption before energy conservation, which is regarded as abnormality, and the analysis result is counted.
Referring to fig. 5, S3 further includes the steps of:
S305: when the generated energy of the light Fu Yue is smaller than the generated energy of the same month, the generated energy is determined to be abnormal, and the analysis result is counted;
s306: judging whether the month average COP of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is smaller than the month average COP of the same period on the basis of the operation parameters of the equipment, if so, determining that the equipment is abnormal, and counting the analysis result;
S307: and when the line network personnel energy consumption, the line network unit operation mileage traction power consumption, the line network unit passenger transport turnover traction power consumption, the line network unit building area movable illumination power consumption and the line network unit passenger transport power consumption are larger than the index value, determining that the analysis result is abnormal, and counting the analysis result.
Referring to fig. 6, S3 further includes the steps of:
S308: when the voltage deviation value of the 110kV bus is more than 10% or less than-10%, the bus is judged to be abnormal, and the analysis result is counted;
s309: when the traction voltage deviation value is more than 7% or less than-7%, the traction voltage deviation value is judged to be abnormal, and analysis results are counted;
s310: when the variation value of the illumination voltage is more than 7% or less than-10%, the illumination voltage is judged to be abnormal, and the analysis result is counted;
s311: when the frequency deviation value of the 110kV bus is greater than 0.2Hz or less than-0.2 Hz, the bus is identified as abnormal, and the analysis result is counted;
S312: when the unbalance rate of the three-phase voltage at the high-voltage side of the 110kV bus is more than 2%, the three-phase voltage is determined to be abnormal, and the analysis result is counted;
s313: power factors less than 0.85 at 110kV bus frequency are considered abnormal and the analysis results are counted.
Referring to fig. 7, S3 further includes the steps of:
s314: when the weak current temperature of the equipment room per month is higher than 27 ℃ and the frequency is higher than the set frequency, the equipment room is identified as abnormal, and analysis results are counted;
s315: when the strong electricity temperature of the equipment room per month is more than 36 ℃ and the frequency is more than the set frequency, the equipment room is identified as abnormal, and analysis results are counted;
S316: the method comprises the steps of identifying abnormality when the temperature of a station hall, a platform, a carriage, an interval, an outdoor and a management room is higher than 29 ℃ and the number of times is higher than a set number of times, and counting analysis results;
s317: the method comprises the steps of identifying abnormality when the humidity of a station hall, a carriage, an interval, outdoors, a management room, a weak current of a device room and a strong current of the device room is more than 60% or less than 40% and the frequency is more than a set frequency, and counting analysis results;
S318: when the concentration of carbon dioxide in the carriage per month is more than 1000mg/m (W/W) and the number of times is more than the set number of times, determining that the carriage is abnormal, and counting the analysis result;
S319: the method comprises the steps that when the VOC concentration of a carriage per month is more than 450ppm and the number of times is more than a set number of times, the carriage is judged to be abnormal, and analysis results are counted;
s320: when the concentration of the TVOC in the carriage is larger than 0.6mg/m and the frequency is larger than the set frequency, the carriage is judged to be abnormal, and the analysis result is counted;
s321: when the concentration of PM2.5 in the carriage per month is more than 35 mug/m and the number of times is more than the set number of times, the carriage PM2.5 is judged to be abnormal, and the analysis result is counted;
S322: the method comprises the steps that when the concentration of PM10 in a carriage per month is greater than 100 mug/m and the number of times is greater than the set number of times, the carriage is judged to be abnormal, and analysis results are counted;
S323: and when the concentration of the formaldehyde in the carriage per month is greater than 0.1mg/m and the number of times is greater than the set number of times, the carriage is judged to be abnormal, and the analysis result is counted.
The method can monitor and diagnose the energy consumption condition and the environment state of the rail transit network in real time, and ensure the operation safety and efficiency. The method can also analyze in multiple dimensions, provide comprehensive diagnosis dimensions, and evaluate the rail transit network from multiple angles, so that the rail transit network is more comprehensive and accurate. The abnormal situation is found in time, the abnormal information is displayed in a report form through the report generating module, decision support is provided for operators, and potential safety risks and economic losses are avoided or reduced. The diagnosis report can be conveniently checked and analyzed by cloud platform operators, so that the centralized management and the efficient utilization of information are realized. The invention provides a comprehensive and efficient scheme for the energy utilization and environment monitoring of the rail transit network, and improves the operation efficiency and safety of rail transit.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (8)
1. A diagnostic system based on rail transit network energy usage, comprising:
and a data acquisition module: the method is used for collecting the energy data and the environment data of the rail transit network of each monitoring point, the method comprises the steps of including network energy consumption parameters, equipment state parameters and environment state parameters;
A diagnostic dimension establishing module: the system comprises a diagnosis dimension for establishing energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of a rail transit network;
And a data analysis module: based on the collected line network energy consumption parameters, equipment operation parameters and environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, counting the abnormality into an analysis result;
a report generation module: the system cloud platform is used for summarizing analysis results into diagnosis reports corresponding to the diagnosis dimensions, and uploading the diagnosis reports to the system cloud platform for display, wherein the diagnosis reports comprise diagnosis objects, diagnosis ranges, the number of diagnosis items and the number of anomalies;
The data analysis module is provided with an analysis flow which comprises the following steps:
Presetting a household energy consumption diagnosis unit: the method comprises the steps that the actual energy consumption of a user in the network energy consumption parameters is greater than the planned energy consumption, is considered as abnormal, and is counted into an analysis result;
presetting a photovoltaic energy consumption diagnosis unit: the method is used for identifying abnormality when the fluctuation of the light Fu Yue power generation quantity in the network energy consumption parameter and the fluctuation of the power generation quantity in the same month of the last year are larger than 5%, and counting analysis results;
presetting an electric energy consumption diagnosis unit for traction: the method is used for identifying the traction power consumption in the network energy consumption parameters as abnormal when the traction power consumption is greater than the contemporaneous maximum value or less than the contemporaneous minimum value, and counting analysis results;
Presetting an energy-saving energy consumption diagnosis unit: the method is used for recognizing that the energy consumption after energy conservation is greater than the energy consumption before energy conservation in the network energy consumption parameters is abnormal and counting analysis results.
2. The diagnostic system based on rail transit network energy of claim 1, wherein the preset analysis flow in the data analysis module further comprises:
presetting a photovoltaic energy efficiency diagnosis unit: the method is used for identifying abnormality when the light Fu Yue generated energy in the network energy consumption parameters is smaller than the same-period month generated energy, and counting analysis results;
A preset device energy efficiency diagnosis unit: the method is used for judging whether the month average COP of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is smaller than the month average COP of the same period on the basis of the operation parameters of the equipment, if so, the ground source heat pump set, the ventilation air conditioning set and the water chilling unit are considered to be abnormal, and analysis results are counted;
Presetting a wire network energy efficiency diagnosis unit: the method is used for identifying the wire network personnel energy consumption, wire network unit operation mileage traction electricity consumption, wire network unit passenger transport turnover traction electricity consumption, wire network unit building area movable illumination electricity consumption and wire network unit passenger transport water consumption in the wire network energy consumption parameters as abnormal when the wire network personnel energy consumption, the wire network unit operation mileage traction electricity consumption, the wire network unit passenger transport turnover traction electricity consumption and the wire network unit building area movable illumination electricity consumption are larger than index values, and counting analysis results.
3. The diagnostic system based on rail transit network energy of claim 1, wherein the preset analysis flow in the data analysis module further comprises:
A preset bus voltage diagnosis unit: the method is used for identifying abnormality when the 110kV bus voltage deviation value in the network energy consumption parameter is more than 10% or less than-10%, and counting analysis results;
A preset traction voltage diagnosis unit: the method is used for identifying abnormality when the traction voltage deviation value in the net energy consumption parameter is more than 7% or less than-7%, and accounting the analysis result;
Presetting an illumination voltage diagnosis unit: the method is used for identifying abnormality when the dynamic illumination voltage deviation value in the line network energy consumption parameter is more than 7% or less than-10%, and accounting the analysis result;
A preset busbar frequency deviation diagnosis unit: the method is used for identifying abnormality when the frequency deviation value of the 110kV bus in the network energy consumption parameters is larger than 0.2Hz or smaller than-0.2 Hz, and counting analysis results;
Presetting a bus three-phase diagnosis unit: the method is used for identifying abnormality when the unbalance rate of the high-voltage side three-phase voltage of the 110kV bus in the network energy consumption parameters is more than 2%, and counting analysis results;
Presetting a busbar frequency power factor diagnosis unit: a110 kV busbar frequency power factor less than 0.85 used in the wire network energy consumption parameters is considered abnormal, and analysis results are counted.
4. The diagnostic system based on rail transit network energy of claim 1, wherein the preset analysis flow in the data analysis module further comprises:
House weak current temperature diagnosis unit for preset equipment: the method comprises the steps that the method is used for identifying abnormality when the weak current temperature of equipment rooms in the environment parameters is higher than 27 ℃ and the frequency is higher than the set frequency, and analyzing results are counted;
strong room temperature diagnosis unit for preset equipment: the method comprises the steps that the equipment is used for recognizing abnormality when the strong electricity temperature of equipment per month in environmental parameters is larger than 36 ℃ and the number of the strong electricity temperature is larger than the set number of the strong electricity temperature, and analyzing results are counted;
a preset environmental temperature diagnosis unit: the system is used for identifying abnormality when the temperature of a station hall, a platform, a carriage, an interval, an outdoor room and a management room in the environment parameters is higher than 29 ℃ and the frequency is higher than the set frequency, and counting analysis results;
A preset environmental humidity diagnosis unit: the system is used for identifying abnormal conditions in the environment parameters when the monthly station hall, carriage, section, outdoor, management room, equipment room weak current and equipment room strong current humidity is more than 60% or less than 40% and the frequency is more than the set frequency, and counting analysis results;
a preset environmental carbon dioxide concentration diagnosis unit: the method comprises the steps of determining that the car is abnormal when the concentration of carbon dioxide in the car is larger than 1000mg/m in the environmental parameter and the number of times is larger than the set number of times, and counting analysis results;
A preset environment VOC concentration diagnostic unit: the method comprises the steps of identifying abnormality when the VOC concentration of a carriage in each month in environmental parameters is more than 450ppm and the number of the VOC concentration is more than a set number of the VOC concentration, and counting analysis results;
preset environmental TVOC concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the TVOC of a carriage in each month in environmental parameters is larger than 0.6mg/m and the number of the TVOC is larger than the set number of the TVOC, and counting analysis results;
Preset environment PM2.5 concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the PM2.5 in a carriage per month in the environmental parameter is more than 35 mu g/m of the solution and the number of times is more than a set number of times, and counting analysis results;
preset environment PM10 concentration diagnostic unit: the method comprises the steps of identifying abnormality when the concentration of the compartment PM10 in the environment parameter is greater than 100 mug/m (m) and the number of times is greater than the set number of times, and counting analysis results;
a preset environmental formaldehyde concentration diagnosis unit: and the analysis result is counted when the concentration of the formaldehyde in the carriage per month in the environmental parameter is greater than 0.1mg/m of the solution and the number of times is greater than the set number of times.
5. The diagnosis method based on the rail transit network energy is characterized by comprising the following steps:
s1: collecting energy data and environment data of a rail transit network of each monitoring point, wherein the energy data and the environment data comprise network energy consumption parameters, equipment state parameters and environment state parameters;
s2: establishing diagnosis dimensions of energy consumption diagnosis, energy efficiency diagnosis, energy consumption quality diagnosis and environment quality diagnosis of the rail transit network;
S3: based on the collected line network energy consumption parameters, the equipment running state and the environment parameters, carrying out item-by-item analysis on the diagnosis dimension according to a preset analysis flow; if the abnormal condition in the preset analysis flow is met, counting the abnormality into an analysis result;
S4: summarizing the analysis result into a diagnosis report corresponding to the diagnosis dimension, and uploading the diagnosis report to a system cloud platform for display, wherein the diagnosis report comprises a diagnosis object, a diagnosis range, the number of diagnosis items and the number of anomalies;
the step S3 comprises the following steps:
s301: when the actual energy consumption of the individual users is greater than the planned energy consumption, the individual users are considered as abnormal, and analysis results are counted;
s302: when the fluctuation of the generated energy of the light Fu Yue and the fluctuation of the generated energy of the same month in the last year are larger than 5%, the fluctuation is considered as abnormal, and the analysis result is counted;
s303: when the traction electricity consumption is greater than the contemporaneous maximum value or less than the contemporaneous minimum value, the traction electricity consumption is determined to be abnormal, and analysis results are counted;
s304: and the energy consumption after energy conservation is greater than the energy consumption before energy conservation, which is regarded as abnormality, and the analysis result is counted.
6. The method for diagnosing energy consumption based on a rail transit network as recited in claim 5, wherein the step S3 further comprises the steps of:
S305: when the generated energy of the light Fu Yue is smaller than the generated energy of the same month, the generated energy is determined to be abnormal, and the analysis result is counted;
s306: judging whether the month average COP of the ground source heat pump set, the ventilation air conditioning set and the water chilling unit is smaller than the month average COP of the same period on the basis of the operation parameters of the equipment, if so, determining that the equipment is abnormal, and counting the analysis result;
S307: and when the line network personnel energy consumption, the line network unit operation mileage traction power consumption, the line network unit passenger transport turnover traction power consumption, the line network unit building area movable illumination power consumption and the line network unit passenger transport power consumption are larger than the index value, determining that the analysis result is abnormal, and counting the analysis result.
7. The method for diagnosing energy consumption based on a rail transit network as recited in claim 5, wherein the step S3 further comprises the steps of:
S308: when the voltage deviation value of the 110kV bus is more than 10% or less than-10%, the bus is judged to be abnormal, and the analysis result is counted;
s309: when the traction voltage deviation value is more than 7% or less than-7%, the traction voltage deviation value is judged to be abnormal, and analysis results are counted;
s310: when the variation value of the illumination voltage is more than 7% or less than-10%, the illumination voltage is judged to be abnormal, and the analysis result is counted;
s311: when the frequency deviation value of the 110kV bus is greater than 0.2Hz or less than-0.2 Hz, the bus is identified as abnormal, and the analysis result is counted;
S312: when the unbalance rate of the three-phase voltage at the high-voltage side of the 110kV bus is more than 2%, the three-phase voltage is determined to be abnormal, and the analysis result is counted;
s313: power factors less than 0.85 at 110kV bus frequency are considered abnormal and the analysis results are counted.
8. The method for diagnosing energy consumption based on a rail transit network as recited in claim 5, wherein the step S3 further comprises the steps of:
s314: when the weak current temperature of the equipment room per month is higher than 27 ℃ and the frequency is higher than the set frequency, the equipment room is identified as abnormal, and analysis results are counted;
s315: when the strong electricity temperature of the equipment room per month is more than 36 ℃ and the frequency is more than the set frequency, the equipment room is identified as abnormal, and analysis results are counted;
S316: the method comprises the steps of identifying abnormality when the temperature of a station hall, a platform, a carriage, an interval, an outdoor and a management room is higher than 29 ℃ and the number of times is higher than a set number of times, and counting analysis results;
s317: the method comprises the steps of identifying abnormality when the humidity of a station hall, a carriage, an interval, outdoors, a management room, a weak current of a device room and a strong current of the device room is more than 60% or less than 40% and the frequency is more than a set frequency, and counting analysis results;
S318: when the concentration of carbon dioxide in the carriage per month is more than 1000mg/m (W/W) and the number of times is more than the set number of times, determining that the carriage is abnormal, and counting the analysis result;
S319: the method comprises the steps that when the VOC concentration of a carriage per month is more than 450ppm and the number of times is more than a set number of times, the carriage is judged to be abnormal, and analysis results are counted;
s320: when the concentration of the TVOC in the carriage is larger than 0.6mg/m and the frequency is larger than the set frequency, the carriage is judged to be abnormal, and the analysis result is counted;
s321: when the concentration of PM2.5 in the carriage per month is more than 35 mug/m and the number of times is more than the set number of times, the carriage PM2.5 is judged to be abnormal, and the analysis result is counted;
S322: the method comprises the steps that when the concentration of PM10 in a carriage per month is greater than 100 mug/m and the number of times is greater than the set number of times, the carriage is judged to be abnormal, and analysis results are counted;
S323: and when the concentration of the formaldehyde in the carriage per month is greater than 0.1mg/m and the number of times is greater than the set number of times, the carriage is judged to be abnormal, and the analysis result is counted.
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| CN108418902A (en) * | 2017-11-20 | 2018-08-17 | 北京万相融通科技股份有限公司 | A kind of railway combined transport hub integrated control system of wisdom |
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