A system for measuring energy consumption of urban rail transit

Abstract

The invention discloses an urban rail transit energy consumption measurement system, which relates to the technical field of energy consumption measurement. A complete verification module, a data verification module and a trend verification module are used to perform complete verification, data verification and trend verification on electric variable measurement data in real time, so as to solve the problems of data missing, data redundancy (repetition) and data error in a targeted manner. Meanwhile, a quality correction module is provided to predict data and supplement data, so as to ensure the formation of complete and correct time series data, which is convenient for the use of the urban rail transit energy consumption measurement system, and further solves the problems of data missing, data redundancy (repetition) and data error, improves the accuracy and punctuality of data, and is convenient for the system to accurately monitor vehicle energy consumption, scientifically formulate energy-saving strategies and optimize the guidance of operation management.

Description

Technical Field

[0001] The invention relates to the technical field of energy consumption measurement, and in particular to an urban rail transit energy consumption measurement system. Background Art

[0002] The urban rail transit energy consumption measurement system has multiple functional advantages in the measurement of electric variables of new energy vehicles, including high-precision measurement, all-round monitoring and flexible adaptation, intelligent data processing and in-depth analysis, high system integration and interoperability, efficient energy management and operation optimization. These functional advantages play an important role in the field of new energy vehicles, and have effectively promoted energy conservation and emission reduction, operating cost reduction and energy efficiency improvement.

[0003] With the rapid development of information technology and the continuous innovation of data acquisition technology, the number of new energy vehicles in urban traffic continues to grow, and the vehicle electrical variable measurement data that the energy consumption measurement system needs to process has also expanded dramatically. Faced with such a huge amount of data, the energy consumption measurement system faces severe challenges in data processing and analysis capabilities. In addition, the frequent occurrence of data missing, data redundancy (duplicate) and data errors has further aggravated the complexity and difficulty of data processing, posing a serious threat to the efficiency and accuracy of data processing.

[0004] These problems cause significant delays in the urban rail transit energy consumption measurement system when processing electrical variable measurement data, and even produce erroneous results, making it impossible to accurately reflect the actual changes in electrical variables in real time. This lag and error will seriously limit the effective application of energy consumption measurement for new energy vehicles, affecting its accurate monitoring of vehicle energy consumption, scientific formulation of energy-saving strategies, and optimization guidance for operation management. Summary of the invention

[0005] In view of the above situation, the present invention can detect the electrical variable measurement data in real time, and through the complete verification, data verification and trend verification of the electrical variable measurement data, carry out targeted data quality correction to solve the problems of data missing, data redundancy (duplication) and data error, and improve the accuracy and timeliness of the data.

[0006] The technical solution includes a data receiving module, a complete verification module, a data verification module, a trend verification module, a quality correction module, and a database. The specific process is as follows:

[0007] The data receiving module receives the electric variable measurement data XKw in real time, and then pre-processes the data, formats and standardizes the data to make it consistent with the data standard stored in the database;

[0008] The complete verification module extracts the timestamp X1, data length X2kB, and IP address X3 of the electrical variable measurement data, first checks the timing according to the timestamp X1 to see if there is a time jump or omission, and if so, sends a first warning signal to the quality correction module, then extracts the threshold data length X21kB in the database, and calculates the length error rate X22. , X22 greater than 0.2 indicates data redundancy or loss, and a second warning signal is sent to the quality correction module;

[0009] The data verification module extracts the voltage value X4V and the current value X5A of the electrical variable measurement data, and extracts the corresponding reference voltage value X41V and the reference current value X51A in the database according to the IP address X3, and then calculates the data integrity check value M. , M is less than 0.3, indicating that the data is normal, otherwise it indicates that the data is abnormal, and the verification value M is sent to the quality correction module;

[0010] The trend verification module calculates the smoothing value XtKw of the data XKw according to the prediction formula, Xt=αX+(1-α)Xt-1, where XtKw is the smoothing value of the tth period, XKw is the actual value of the tth period, that is, the electrical variable measurement data XKw, Xt-1Kw is the smoothing value of the t-1th period, α is the smoothing coefficient, where 0<α<1, and calculates the trend error rate N, , N is less than 0.3, indicating that the trend is normal, otherwise it indicates that the trend is abnormal, and the trend result is sent to the quality correction module;

[0011] The quality correction module performs corresponding processing according to the received analysis results, so that the electrical variable measurement data XKw can be used directly.

[0012] Furthermore, when the quality correction module receives the first warning signal, it indicates that the data XKw has a time jump or missing. When the timestamp X1 is a jump, the timestamp X1 is corrected. When the timestamp X1 is missing, the correct timestamp in the time series is extracted to replace the timestamp X1, so that the timestamp X1 is formed into a complete time series.

[0013] When the quality correction module receives the second warning signal, if it is in a data redundancy state, it cleans the redundant and repeated data and deletes the redundant and repeated data to form complete data for subsequent processing. If it is in a data loss state, it re-measures the corresponding electrical variable measurement data according to the IP address X3 until the data is complete.

[0014] Furthermore, the quality correction module receives the check value M sent by the data verification module and the trend result sent by the trend verification module. When the trend error rate N is less than 0.3 and the trend is normal, and the check value M is greater than 0.3 and the data is abnormal, the corrected data value XNKw is calculated. , the corrected data value XNKw is used as the substitute data XKw;

[0015] When the trend error rate N is greater than 0.3, the trend is abnormal, and the check value M is greater than 0.3, the data is abnormal, indicating that data A is particularly abnormal. Starting from the timestamp X1, the five data of the complete time series XAKw, XBKw, XCKw, XDKw, and XEKw are extracted in sequence, and then the value XYKw is predicted based on the calculated data. , Indicates the error correction value, the data prediction value XYKw is used instead of the data XKw, and the particularly abnormal data XKw is deleted.

[0016] Furthermore, the quality correction module receives the check value M sent by the data verification module and the trend result sent by the trend verification module. When the trend error rate N is greater than 0.3, the trend is abnormal, and the check value M is less than 0.3, the data is normal, starting from the timestamp X1, the smoothed values ​​Xt-1Kw, Xt-2Kw, Xt-3Kw, Xt-4Kw, and Xt-5Kw of the 5 data of the complete time series are extracted in sequence, and then the smoothed prediction value of the calculated data A is calculated according to the Kw, , Represents the error calibration value, which will smooth the predicted value Kw replaces the smoothing value XtKw and deletes the smoothing value XtKw to smooth the predicted value Kw is stored in the database and used as the correct smoothing value, so that the database can update data in real time, while data A is used normally;

[0017] When the trend error rate N is less than 0.3, the trend is normal, and the check value M is less than 0.3, the data is normal, and the data XKw is used normally. At the same time, the data XKw and the smoothing value XtKw are stored in the database, and the database information is updated to form a complete and correct time series database.

[0018] Furthermore, it also includes a management center module receiving the corrected data sent by the quality correction module and directly using it as the correct electrical variable measurement data;

[0019] The management center module supports real-time display of urban rail transit energy consumption measurement data, so that users can instantly understand the operating status of the system. The display method uses a table format to clearly display the electrical variable measurement data at each time point, or uses charts and dashboards to intuitively display data, so that users can understand and analyze data more quickly;

[0020] The management center module also provides data visualization functions, showing the changing trends and distribution of electrical variable measurement data through charts and curves.

[0021] Furthermore, during the detection process, once the quality correction module finds that the data from the same IP address X3 shows the missing or data loss state of the timestamp X1 in two consecutive detections, it will immediately trigger a fault signal and quickly send this signal to the management center module;

[0022] After receiving this fault signal, the management center module will immediately activate its built-in visual alarm system, and remind users of this abnormal situation in an intuitive way by making the relevant indicator icons or information boxes flash continuously, so as to take maintenance measures as soon as possible to ensure the normal operation of the system and the integrity of the data.

[0023] Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art:

[0024] 1. Use the complete verification module, data verification module, and trend verification module to perform real-time complete verification, data verification, and trend verification of the electrical variable measurement data, and solve the problems of data missing, data redundancy (duplication), and data errors in a targeted manner. At the same time, a quality correction module is set up to predict data and supplement data to ensure the formation of complete and correct time series data, which is convenient for the use of the urban rail transit energy consumption measurement system, thereby solving the problems of data missing, data redundancy (duplication), and data errors, improving the accuracy and timeliness of the data, and facilitating the system to accurately monitor vehicle energy consumption, scientifically formulate energy-saving strategies, and optimize guidance for operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flow chart of an urban rail transit energy consumption measurement system. DETAILED DESCRIPTION

[0026] The above and other technical contents, features and effects of the present invention are described in detail below with reference to the attached Figure 1 The detailed description of the embodiments will clearly show that the structural contents mentioned in the following embodiments are all based on the drawings in the specification.

[0027] In order to solve the frequent problems of data missing, data redundancy (duplication) and data errors, the complexity and difficulty of data processing have been further aggravated, posing a serious threat to the efficiency and accuracy of data processing, making it impossible to reflect the actual changes of electrical variables in real time and accurately. It is necessary to consider that there are many factors affecting the measurement data of electrical variables, and the solutions to each factor are different. Therefore, it is necessary to first ensure the reasons for data lags and errors. Only by ensuring complete and accurate time series data can the lags and errors of data be fundamentally overcome.

[0028] Therefore, this solution includes a data receiving module, a complete verification module, a data verification module, a trend verification module, a quality correction module, and a database. The specific process is as follows;

[0029] The data receiving module receives the electric variable measurement data XKw in real time, and then pre-processes the data, formats and standardizes the data to make it consistent with the data standard stored in the database;

[0030] The complete verification module extracts the timestamp X1, data length X2kB, and IP address X3 of the electrical variable measurement data, first checks the timing according to the timestamp X1 to see if there is a time jump or omission, and if so, sends a first warning signal to the quality correction module, then extracts the threshold data length X21kB in the database, and calculates the length error rate X22. , X22 greater than 0.2 indicates data redundancy or loss, and a second warning signal is sent to the quality correction module;

[0031] The data verification module extracts the voltage value X4V and the current value X5A of the electrical variable measurement data, and extracts the corresponding reference voltage value X41V and the reference current value X51A in the database according to the IP address X3, and then calculates the data integrity check value M. , M is less than 0.3, indicating that the data is normal, otherwise it indicates that the data is abnormal, and the verification value M is sent to the quality correction module;

[0032] The trend verification module calculates the smoothed value XtKw of the data XKw according to the prediction formula, Xt=αX+(1-α)Xt-1, where Xt is the smoothed value of the tth period, X is the actual value of the tth period, that is, the electrical variable measurement data XKw, Xt-1 is the smoothed value of the t-1th period, α is the smoothing coefficient, where 0<α<1, and calculates the trend error rate N, , N is less than 0.3, indicating that the trend is normal, otherwise it indicates that the trend is abnormal, and the trend result is sent to the quality correction module;

[0033] The quality correction module performs corresponding processing according to the received analysis results, so that the electrical variable measurement data XKw can be used directly.

[0034] Furthermore, when the quality correction module receives the first warning signal, it indicates that there is a time jump or missing in the data X. When the timestamp X1 is a jump, the timestamp X1 is corrected. When the timestamp X1 is missing, the correct timestamp in the time series is extracted to replace the timestamp X1, so that a complete time series is formed.

[0035] When the quality correction module receives the second warning signal, if it is in a data redundancy state, it cleans the redundant and repeated data and deletes the redundant and repeated data to form complete data for subsequent processing. If it is in a data loss state, it re-measures the corresponding electrical variable measurement data according to the IP address X3 until the data is complete.

[0036] Furthermore, the quality correction module receives the check value M sent by the data verification module and the trend result sent by the trend verification module. When the trend error rate N is less than 0.3 and the trend is normal, and the check value M is greater than 0.3 and the data is abnormal, the corrected data value XN is calculated. , the corrected data value XN is used as the substitute data A;

[0037] When the trend error rate N is greater than 0.3, the trend is abnormal, and the check value M is greater than 0.3, the data is abnormal, indicating that data A is particularly abnormal. Starting from timestamp X1, the five data XA, XB, XC, XD, and XE of the complete time series are extracted in sequence, and then the value XY is predicted based on the calculated data. , Indicates the error correction value, uses the data prediction value XY instead of data A, and deletes the particularly abnormal data A.

[0038] Furthermore, the quality correction module receives the check value M sent by the data verification module and the trend result sent by the trend verification module. When the trend error rate N is greater than 0.3, the trend is abnormal, and the check value M is less than 0.3, the data is normal, starting from the timestamp X1, the smoothed values ​​Xt-1, Xt-2, Xt-3, Xt-4, and Xt-5 of the 5 data of the complete time series are extracted in sequence, and then the smoothed prediction value of the calculated data A is calculated according to the , , Represents the error calibration value, which will smooth the predicted value Replace the smoothed value Xt and delete the smoothed value Xt to smooth the predicted value The storage database is used as the correct smoothing value, so that the database can update the data in real time, and data A can be used normally;

[0039] When the trend error rate N is less than 0.3, the trend is normal, and the check value M is less than 0.3, the data is normal, and data A is used normally. At the same time, data A and smoothing value Xt are stored in the database, and the database information is updated to form a complete and correct time series database.

[0040] Furthermore, it also includes a management center module receiving the corrected data sent by the quality correction module and directly using it as the correct electrical variable measurement data;

[0041] The management center module supports real-time display of urban rail transit energy consumption measurement data, so that users can instantly understand the operating status of the system. The display method uses a table format to clearly display the electrical variable measurement data at each time point, or uses charts and dashboards to intuitively display data, so that users can understand and analyze data more quickly;

[0042] The management center module also provides data visualization functions, showing the changing trends and distribution of electrical variable measurement data through charts and curves.

[0043] Furthermore, during the detection process, once the quality correction module finds that the data from the same IP address X3 shows the missing or data loss state of the timestamp X1 in two consecutive detections, it will immediately trigger a fault signal and quickly send this signal to the management center module;

[0044] After receiving this fault signal, the management center module will immediately activate its built-in visual alarm system, and remind users of this abnormal situation in an intuitive way by making the relevant indicator icons or information boxes flash continuously, so as to take maintenance measures as soon as possible to ensure the normal operation of the system and the integrity of the data.

[0045] When the present invention is used specifically, on the basis of the prior art, the data receiving module receives the electrical variable measurement data XKw in real time, and then pre-processes the data, formats and standardizes the data to make it consistent with the data standard stored in the database;

[0046] The complete verification module extracts the timestamp X1, data length X2kB, and IP address X3 of the electrical variable measurement data, first checks the timing according to the timestamp X1 to see if there is a time jump or omission, and if so, sends a first warning signal to the quality correction module, then extracts the threshold data length X21kB in the database, and calculates the length error rate X22. , X22 greater than 0.2 indicates data redundancy or loss, and a second warning signal is sent to the quality correction module;

[0047] The data verification module extracts the voltage value X4V and the current value X5A of the electrical variable measurement data, and extracts the corresponding reference voltage value X41V and the reference current value X51A in the database according to the IP address X3, and then calculates the data integrity check value M. , M is less than 0.3, indicating that the data is normal, otherwise it indicates that the data is abnormal, and the verification value M is sent to the quality correction module;

[0048] The trend verification module calculates the smoothed value XtKw of the data XKw according to the prediction formula, Xt=αX+(1-α)Xt-1, where Xt is the smoothed value of the tth period, X is the actual value of the tth period, that is, the electrical variable measurement data XKw, Xt-1 is the smoothed value of the t-1th period, α is the smoothing coefficient, where 0<α<1, and calculates the trend error rate N, , N is less than 0.3, indicating that the trend is normal, otherwise it indicates that the trend is abnormal, and the trend result is sent to the quality correction module;

[0049] The quality correction module performs corresponding processing according to the received analysis results, so that the electrical variable measurement data XKw can be used directly.

[0050] The management center module receives the corrected data sent by the quality correction module and directly uses it as the correct electrical variable measurement data;

[0051] The management center module supports real-time display of urban rail transit energy consumption measurement data, so that users can instantly understand the operating status of the system. The display method uses a table format to clearly display the electrical variable measurement data at each time point, or uses charts and dashboards to intuitively display data, so that users can understand and analyze data more quickly;

[0052] The management center module also provides data visualization functions, showing the changing trends and distribution of electrical variable measurement data through charts and curves.

[0053] The above is a further detailed description of the present invention in combination with a specific implementation method, and it cannot be determined that the specific implementation of the present invention is limited to this; for technical personnel in the technical field to which the present invention belongs and related technical fields, based on the technical solution of the present invention, the expansion and replacement of operating methods and data should all fall within the protection scope of the present invention.

Claims

1. An urban rail transit energy consumption measurement system, characterized in that: It includes data receiving module, complete verification module, data verification module, trend verification module, quality correction module and database. The specific process is as follows; The data receiving module receives the electric variable measurement data XKw in real time, and then pre-processes the data, formats and standardizes the data to make it consistent with the data standard stored in the database; The complete verification module extracts the timestamp X1, data length X2kB, and IP address X3 of the electrical variable measurement data, first checks the timing according to the timestamp X1 to see if there is a time jump or missing, and if so, sends a first warning signal to the quality correction module; then extracts the threshold data length X21kB in the database, calculates the length error rate X22, , X22 greater than 0.2 indicates data redundancy or loss, and a second warning signal is sent to the quality correction module; The data verification module extracts the voltage value X4V and the current value X5A of the electrical variable measurement data, and extracts the corresponding reference voltage value X41V and the reference current value X51A in the database according to the IP address X3, and then calculates the data integrity check value M. , send the verification value M to the quality correction module. If M is less than 0.3, it means the data is normal, otherwise it means the data is abnormal; The trend verification module calculates the smoothing value of the data XKw according to the prediction formula Kw, =αX+(1-α) ,in Kw is the smoothed value of the tth period, and XKw is the actual value of the tth period, that is, the electrical variable measurement data XKw. Kw is the smoothing value of the t-1 period, α is the smoothing coefficient, where 0<α<1, and the trend error rate N is calculated. , send the trend results to the quality correction module, N less than 0.3 indicates a normal trend, otherwise it indicates an abnormal trend; The quality correction module performs corresponding processing according to the received analysis results so that the electrical variable measurement data X can be used directly; The quality correction module receives the check value M sent by the data verification module and the trend result sent by the trend verification module. When the trend error rate N is less than 0.3, the trend is normal, and the check value M is greater than 0.3, the data is abnormal, the corrected data value XNKw is calculated. , the corrected data value XNKw is used as the substitute data XKw; When the trend error rate N is greater than 0.3, the trend is abnormal, and the check value M is greater than 0.3, the data is abnormal, indicating that the data XKw is particularly abnormal. Starting from the timestamp X1, the five data of the complete time series XAKw, XBKw, XCKw, XDKw, and XEKw are extracted in sequence, and then the value XYKw is predicted based on the calculated data. , Indicates the error calibration value, uses the data prediction value XYKw instead of the data XKw, and deletes the particularly abnormal data XKw; The quality correction module receives the check value M sent by the data verification module and the trend result sent by the trend verification module. When the trend error rate N is greater than 0.3, the trend is abnormal, and the check value M is less than 0.3, the data is normal, the smoothing values ​​of the 5 data of the complete time series are extracted in sequence with the timestamp X1 as the starting point. Kw, Kw, Kw, Kw, Kw, and then calculate the smoothed predicted value of the data XKw Kw, , Represents the error calibration value, which will smooth the predicted value Kw alternative smoothing value Kw is used and the smoothing value is removed Kw, smoothes the predicted value Kw is stored in the database and used as the correct smoothing value, so that the database can update data in real time, and the data XKw can be used normally; When the trend error rate N is less than 0.3, the trend is normal, and the check value M is less than 0.3, the data is normal, and the data XKw is used normally. At the same time, the data XKw and the smoothing value Kw stores the database and updates the database information to form a complete and correct time series database.

2. According to claim 1, an urban rail transit energy consumption measurement system is characterized in that: When the quality correction module receives the first warning signal, it indicates that the data X has a time jump or missing. When the timestamp X1 is a jump, the timestamp X1 is corrected. When the timestamp X1 is missing, the correct timestamp in the time series is extracted to replace the timestamp X1, so that the data X1 forms a complete time series. When the quality correction module receives the second warning signal, if it is in a data redundancy state, it cleans the redundant and repeated data and deletes the redundant and repeated data to form complete data for subsequent processing. If it is in a data loss state, it re-measures the corresponding electrical variable measurement data according to the IP address X3 until the data is complete.

3. According to claim 1, an urban rail transit energy consumption measurement system is characterized in that: It also includes a management center module that receives the corrected data sent by the quality correction module and directly uses it as the correct electrical variable measurement data; The management center module supports real-time display of urban rail transit energy consumption measurement data, so that users can instantly understand the operating status of the system. The display method uses a table format to clearly display the electrical variable measurement data at each time point, or uses charts and dashboards to intuitively display data, so that users can understand and analyze data more quickly; The management center module also provides data visualization functions, showing the changing trends and distribution of electrical variable measurement data through charts and curves.

4. According to claim 3, an urban rail transit energy consumption measurement system is characterized in that: During the detection process, once the quality correction module finds that the data from the same IP address X3 shows the missing or data loss state of the timestamp X1 in two consecutive detections, it will immediately trigger a fault signal and quickly send this signal to the management center module; After receiving this fault signal, the management center module will immediately activate its built-in visual alarm system, and remind users of this abnormal situation in an intuitive way by making the relevant indicator icons or information boxes flash continuously, so as to take maintenance measures as soon as possible to ensure the normal operation of the system and the integrity of the data.

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