A data integrity monitoring system for integrated energy monitoring and control of rail transit

Abstract

The present invention discloses a data integrity monitoring system for integrated energy monitoring and control of rail transit, characterized in that it comprises: a defect statistics configuration module, which is used to configure a defect statistics configuration file and adopt different defect statistics methods according to different situations; a data statistics algorithm module, which is used to construct a statistical algorithm for statistical data defects; a data defect statistics module, which is used to judge the corresponding defect statistics method according to the defect statistics configuration module during the data collection process, and to count the data defects according to the statistical algorithm in the data statistics algorithm module.

Description

Technical Field

[0001] The present invention relates to the field of integrated monitoring and control of rail transit, and in particular to a data integrity monitoring system for integrated energy monitoring and control of rail transit. Background Art

[0002] The subway integrated monitoring system is an important tool to ensure the safe and efficient operation of the subway. It integrates the operating data of multiple subsystems including signal, communication, power, environmental control, etc. It mainly completes the data fusion and analysis functions of various disciplines and realizes cross-discipline equipment control. With the increase of integrated professional data categories and the continuous increase of total data, the information integration of the integrated monitoring system is getting higher and higher.

[0003] The significance of rail transit monitoring data integrity is far-reaching and significant. Data integrity is not only about the accuracy of data, but also about the consistency, completeness and timeliness of data. It is not only about the daily operation safety and efficiency of urban rail transit systems, but also the basis for the normal operation of big data analysis and decision support systems. In the field of rail transit, whether it is the actual energy consumption of stations, the environmental conditions of stations or the flow of passengers, high-quality data is needed for real-time monitoring and predictive analysis. If data is omitted, delayed or erroneous during collection, transmission or processing, it may lead to misjudgment of operating conditions, thereby affecting the timeliness and effectiveness of energy scheduling, maintenance plans and emergency response. Therefore, it is necessary to monitor the missing data points during the data collection process to facilitate data supplementation. Summary of the invention

[0004] To achieve the above-mentioned purpose and other related purposes, the present invention discloses a data integrity monitoring system for integrated energy monitoring and control of rail transit, comprising:

[0005] The defect statistics configuration module is used to configure the defect statistics configuration file and adopt different defect statistics methods according to different situations;

[0006] Data statistical algorithm module, used to construct statistical algorithms for statistical data shortcomings;

[0007] The data defect statistics module is used to determine the corresponding defect statistics method according to the defect statistics configuration module during the data collection process, and to count the data defects according to the statistical algorithm in the data statistical algorithm module.

[0008] Furthermore, the different methods of counting defects according to different situations include:

[0009] When the data collection time is less than the first time threshold, the defect data is counted in the process of collecting data;

[0010] When the data collection time is greater than the first time threshold and less than the second time threshold, the defect data is counted by using the local statistical cache file, and the defect data is counted by reading the cache file;

[0011] When the data collection time is greater than the second time threshold and less than the third time threshold, the defect data is counted by using the local statistical data file, and the defect data is counted by converting the cache file into a persistent file and reading the persistent file;

[0012] When the data collection time is greater than the third time threshold, the defect data is counted by loading data from the database, writing the persistent file into the database, and reading data from the database to count the defect data.

[0013] Furthermore, the defect statistics configuration module also includes:

[0014] Configure whether to count the completeness rate based on the data collection situation;

[0015] Set the file format for filtering incoming data;

[0016] Configure whether to send the defect statistics data to the relevant supplementary recruitment process according to the data collection situation;

[0017] Count the start time and end time of the shortcomings;

[0018] Statistics collection completeness rate period.

[0019] Furthermore, the steps of the data defect statistics module when working include:

[0020] Get the meter reading task template from the database. The template stores the meter reading task ID, the collection device object ID set, the data type set, and the collection time interval.

[0021] Create and initialize meter reading task collection objects. Each data object contains meter reading task id, device id set, data type set, collection time, and counter Count1. The device id set stores the id numbers of all devices involved in this meter reading task. The data type set is a set of id numbers of data types that need to be collected in this meter reading task. Multiple data types that need to be collected can be queried through the data type set. Counter Count1 is used to store the accumulated value of collected data in the meter reading task collection object.

[0022] Initialize the data collection statistics table. The data collection statistics table is an object array. Each object contains the meter reading task id, the modulus value, and the counter Count2. It represents the value left after the device ID is modulo-operated:

[0023] ;

[0024] Where id is the device ID number, and the device ID number is modulo operation based on n, where n is an adjustable parameter. The device ID set is classified according to the characteristics of the serial number. is the value of each category, and counter Count2 is used to store the accumulated value of collected data in the collected data statistical information table;

[0025] Initialize the detailed statistical table of collected information. The detailed statistical table of collected information is a hash table structure. Each data object includes device ID, collection time, counter Count3. Counter Count3 is used to store the accumulated value of collected data in the detailed statistical table of collected information.

[0026] Determine the defect statistics method according to the defect statistics configuration module;

[0027] Different steps are used to perform defect statistics according to different defect statistics methods.

[0028] Furthermore, different steps are used to perform defect statistics according to different defect statistics methods, including:

[0029] When collecting data, the defect data should include:

[0030] Get the collected data message;

[0031] Analyze the collected messages;

[0032] Get the collected data and the corresponding device ID and data type;

[0033] Complete the statistics of collected data defects information according to the data statistics algorithm process;

[0034] Write the parsed data into a cache file whose name consists of the meter reading task id and time.

[0035] Furthermore, different steps are used to perform defect statistics according to different defect statistics methods, including:

[0036] When the local cache file is used to count the defect data, the defect data is counted by reading the cache file, including:

[0037] According to the meter reading task template, search for the corresponding cache file in the cache folder by meter reading task ID and time;

[0038] Read all data from the cache file in sequence;

[0039] Get all devices and data types and values;

[0040] Complete the statistics of collected data defects information according to the data statistics algorithm process;

[0041] Move the cache files to other folders for conversion into storage data files by other processes.

[0042] Furthermore, different steps are used to perform defect statistics according to different defect statistics methods, including:

[0043] When the local statistical data file is used to count the defect data, the defect data is counted by converting the cache file into a persistent file and reading the persistent file, including:

[0044] According to the meter reading task template, open the corresponding data file from the storage folder;

[0045] Read all data from the input data file in sequence and generate memory data objects;

[0046] Get the corresponding device, data type and value;

[0047] Complete the statistics of collected data defects information according to the data statistics algorithm process;

[0048] Move data files into a backup folder and delete them periodically.

[0049] Furthermore, different steps are used to perform defect statistics according to different defect statistics methods, including:

[0050] When counting defect data by loading data from a database, writing a persistent file to the database, and reading data from the database to count defect data, it includes:

[0051] According to the meter reading task template, obtain the data type and device information that need to be collected within the cycle;

[0052] Query database SQL;

[0053] Query the database;

[0054] Generate the storage data file based on the returned data results;

[0055] Read all data from the input data file in sequence and generate memory data objects;

[0056] Get the corresponding device, data type and value;

[0057] Complete the statistics of collected data defects information according to the data statistics algorithm process;

[0058] Move data files into a backup folder and delete them periodically.

[0059] Furthermore, the statistical algorithm of the statistical data shortcomings includes:

[0060] S1: Get the data to be processed: When it is a real-time collection mode, the data to be processed is the data of all data types of all devices in the data message collected this time; when it is a cache file mode, the data to be processed is the corresponding data that has been saved in the content;

[0061] S2: Get a data object from the current address pointer;

[0062] S3: Determine whether the data is abnormal. If abnormal, discard the data;

[0063] S4: If normal, update the counter Count1 in the meter reading task collection object by accumulating 1 according to the meter reading task id corresponding to the data;

[0064] S5: according to the corresponding meter reading task id and the device id, the meter reading task id corresponding to the modulo result data is updated by accumulating 1 in the counter Count2 in the collected data statistical information table;

[0065] S6: Update the counter Count3 in the detailed statistics table of the collected information by accumulating 1 according to the corresponding device ID;

[0066] S7: Determine whether the data statistics are complete. If not, point to the next data object and re-execute S3-S7. Otherwise, execute S8.

[0067] S8: Filter whether the value of the counter Count1 in the meter reading task set object is consistent with the total number of expected collected data items Total1(l), if they are consistent, the counting ends;

[0068] in:

[0069] ;

[0070] in For meter reading tasks The total number of all devices in The total number of data types of a device in the meter reading task;

[0071] S9: If the counter Count1 of a certain item is inconsistent with Total1(l), filter the meter reading task id in the data object corresponding to the item, and find the item whose Count2 in the collected data statistical information table is inconsistent with the expected total number of collected data items Total2(p);

[0072] in:

[0073] ;

[0074] in The number of subsets divided by the modulo operation for the device id;

[0075] S10: Obtain a device ID subset for device ID modulo operation based on the items filtered out in S9, perform a direct query based on each ID in the device ID subset, and filter out items that are inconsistent with the expected total number of collected data items Total3(h);

[0076] in:

[0077] ;

[0078] S11: Save the information of the items screened out in S10 in a defect statistics information table.

[0079] Further, the determining whether the data is abnormal includes:

[0080] Collect the moving average of the historical data of the same period for a preset time period to form a historical data set;

[0081] Get the average value of a historical data set and variance , and obtain the discriminant value Z;

[0082] in, ;

[0083] Determine whether Z is within the preset range. If not, it is considered as data abnormality.

[0084] By adopting the above technical solution, during the data collection process, the corresponding defect statistics method is determined according to the defect statistics configuration module, and the data defects are counted according to the statistical algorithm in the data statistical algorithm module, so as to monitor the data integrity and facilitate the subsequent data supplementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0086] Figure 1 is a system block diagram of the present invention;

[0087] Figure 2 It is the statistical process of system data defects of the present invention;

[0088] Figure 3 This is the detailed process of the data statistics algorithm of the present invention. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of the present invention.

[0090] Reference Figure 1 The embodiment of the present invention provides a data integrity monitoring system for rail transit integrated energy monitoring and control, comprising:

[0091] The defect statistics configuration module is used to configure the defect statistics configuration file and adopt different defect statistics methods according to different situations.

[0092] Data statistical algorithm module, used to construct statistical algorithms for statistical data shortcomings.

[0093] The data defect statistics module is used to determine the corresponding defect statistics method according to the defect statistics configuration module during the data collection process, and to count the data defects according to the statistical algorithm in the data statistical algorithm module.

[0094] Specifically, the different methods of counting defects according to different situations include:

[0095] When the data collection time is less than the first time threshold, the defect data is counted in the process of collecting data;

[0096] When the data collection time is greater than the first time threshold and less than the second time threshold, the defect data is counted by using the local statistical cache file, and the defect data is counted by reading the cache file;

[0097] When the data collection time is greater than the second time threshold and less than the third time threshold, the defect data is counted by using the local statistical data file, and the defect data is counted by converting the cache file into a persistent file and reading the persistent file;

[0098] When the data collection time is greater than the third time threshold, the defect data is counted by loading data from the database, writing the persistent file into the database, and reading data from the database to count the defect data.

[0099] Among them, the first time threshold, the second time threshold and the third time threshold are set by those skilled in the art according to actual needs.

[0100] Statistics during collection: While using the program to collect data, real-time statistics of fault information are collected. Generally, it is suitable for collecting data fault statistics in a relatively short period of time or when the server load rate is low. Long-term collection will affect the efficiency of system data collection.

[0101] Local statistical cache file: After the system collects data, it is generally not saved to the database immediately, but generates various types of intermediate files. After the system parses the data, it first saves the parsed data into a cache file. The cache file is a file that quickly saves data, which is convenient for program reading, but the data cannot be too large or too much. It is generally suitable for statistics of data defects collected in a short period of time.

[0102] Local statistical data file: The system further converts the cache file into a persistent file, generally a sqlite type file, which is convenient for other processes to process the file and write data into the database. It is a file that quickly saves data and is easy for programs to read. It is generally suitable for collecting data defects in a medium time period.

[0103] Load data statistics from the database. The collected data is stored in the database, and the historical data information of the specified equipment in the specified time period is obtained from the database. It is generally used for statistics of data defects collected over a long period of time.

[0104] The disadvantage statistics configuration module also includes:

[0105] Configure whether to count the completeness rate according to the data collection situation to determine whether to take effect. In order to adapt to low-configuration collection equipment or situations where data requirements are not high, the collection completeness rate method is generally required.

[0106] Set the file format for filtering incoming data; used to set the file type for statistics of existing data files, which can further filter different types of data files to speed up data statistics. Supports multiple types of data such as CSV, TSV, xml, Excel, txt, sqlite, json, etc.

[0107] Configure whether to send the defect statistics data to the relevant supplementary recruitment process according to the data collection situation.

[0108] The start time and end time of counting defects are both set to the current time by default.

[0109] Statistical collection completeness rate cycle. Since the statistical collection completeness rate time is much faster than the data collection time, it is not necessary to count the defect information once in one collection cycle. The defect information can be counted once at intervals of multiple collection cycles.

[0110] After that, you need to set a timer. The system generates a defect statistics task periodically by counting the complete rate of collection. The system loads the meter reading task table from the database, obtains the meter reading data type and time interval, and sets the defect statistics task timer according to the time interval. When the timer expires, it starts to count the data collection within the time interval.

[0111] The steps of the data defect statistics module include:

[0112] Reference Figure 2 , get the meter reading task template from the database, which stores the meter reading task id, the collection device object id set, the data type set, and the collection time interval;

[0113] Create and initialize the meter reading task collection object. The meter reading task collection object MeterMap is a Map structure. Each data object MeterObj contains the meter reading task id, device id set, data type set, collection time, and counter Count1. The device id set stores the id numbers of all devices involved in this meter reading task. The data type set refers to the set of data type id numbers that need to be collected in this meter reading task. Through the data type set, you can query multiple data types that need to be collected, such as the id corresponding to the current is "1001", the id corresponding to the voltage is "1002" and other data type numbers. The counter Count1 is used to store the accumulated value of the collected data in the meter reading task collection object;

[0114] Initialize the data collection statistics table. The data collection statistics table InfoTable is an object array. Each object InfoRecord contains the meter reading task id, the modulus value, and the counter Count2. It represents the value left after the device ID is modulo-operated:

[0115] ;

[0116] Where id is the device ID number, and the device ID number is subjected to a modulo operation based on n, where n is an adjustable parameter and is preferably 5 in this embodiment. The device ID set is classified according to the characteristics of the serial number. is the value of each category, and counter Count2 is used to store the accumulated value of collected data in the collected data statistical information table;

[0117] Initialize the detailed statistical table of collected information. The detailed statistical table of collected information StatTable is a hash table structure. Each data object StatRecord includes the device id, collection time, and counter Count3. The hash table structure is used to facilitate the search for collected statistical data based on the device id. Counter Count3 is used to store the accumulated value of collected data in the detailed statistical table of collected information; the detailed statistical table of collected information StatTable is used to count the accumulated values ​​of various data collected by each device in each data collection cycle, and put them in counter Count3. The data collection status of the device can be judged through this table;

[0118] Determine the defect statistics method according to the defect statistics configuration module;

[0119] Different steps are used to count defects according to different defect counting methods. Figure 3 , including:

[0120] When collecting data, the defect data should include:

[0121] Get the collected data message;

[0122] Analyze the collected messages;

[0123] Get the collected data and the corresponding device ID and data type;

[0124] Complete the statistics of the shortcomings of the collected data according to the data statistics algorithm process;

[0125] Write the parsed data into a cache file whose name consists of the meter reading task id and time.

[0126] When the local cache file is used to count the defect data, the defect data is counted by reading the cache file, including:

[0127] According to the meter reading task template, search for the corresponding cache file in the cache folder by meter reading task ID and time;

[0128] Read all data from the cache file in sequence;

[0129] Get all devices and data types and values;

[0130] Complete the statistics of the shortcomings of the collected data according to the data statistics algorithm process;

[0131] Move the cache files to other folders for conversion into storage data files by other processes.

[0132] When the local statistical data file is used to count the defect data, the defect data is counted by converting the cache file into a persistent file and reading the persistent file, including:

[0133] According to the meter reading task template, open the corresponding data file from the storage folder;

[0134] Read all data from the input data file in sequence and generate memory data objects;

[0135] Get the corresponding device, data type and value;

[0136] Complete the statistics of collected data defects information according to the data statistics algorithm process;

[0137] Move data files into a backup folder and delete them periodically.

[0138] When counting defect data by loading data from a database, writing a persistent file to the database, and reading data from the database to count defect data, it includes:

[0139] According to the meter reading task template, obtain the data type and device information that need to be collected within the cycle;

[0140] Query database SQL;

[0141] Query the database;

[0142] Generate the storage data file based on the returned data results;

[0143] Read all data from the input data file in sequence and generate memory data objects;

[0144] Get the corresponding device, data type and value;

[0145] Complete the statistics of collected data defects information according to the data statistics algorithm process;

[0146] Move data files into a backup folder and delete them periodically.

[0147] In the data defect statistics module, refer to Figure 3 , the statistical algorithms for the statistical data shortcomings include:

[0148] S1: Get the data to be processed: When it is a real-time collection mode, the data to be processed is the data of all data types of all devices in the data message collected this time; when it is a cache file mode, the data to be processed is the corresponding data that has been saved in the content;

[0149] S2: Get a data object from the current address pointer;

[0150] S3: Determine whether the data is abnormal. If abnormal, discard the data;

[0151] S4: If normal, update the counter Count1 in the meter reading task collection object by accumulating 1 according to the meter reading task id corresponding to the data;

[0152] S5: according to the corresponding meter reading task id and the device id, the meter reading task id corresponding to the modulo result data is updated by accumulating 1 in the counter Count2 in the collected data statistical information table;

[0153] S6: Update the counter Count3 in the detailed statistics table of the collected information by accumulating 1 according to the corresponding device ID;

[0154] S7: Determine whether the data statistics are complete. If not, point to the next data object and re-execute S3-S7. Otherwise, execute S8.

[0155] S8: Filter whether the value of the counter Count1 in the meter reading task set object is consistent with the total number of expected collected data items Total1(l), if they are consistent, the counting ends;

[0156] in:

[0157] ;

[0158] in For meter reading tasks The total number of all devices in The total number of data types of a device in the meter reading task;

[0159] S9: If the counter Count1 of a certain item is inconsistent with Total1(l), filter the meter reading task id in the data object corresponding to the item, and find the item whose Count2 in the collected data statistical information table is inconsistent with the expected total number of collected data items Total2(p);

[0160] in:

[0161] ;

[0162] in The number of subsets divided by the modulo operation for the device id;

[0163] S10: Obtain a device ID subset for device ID modulo operation based on the items filtered out in S9, perform a direct query based on each ID in the device ID subset, and filter out items that are inconsistent with the expected total number of collected data items Total3(h);

[0164] in:

[0165] ;

[0166] S11: Save the information of the items screened out in S10 in a defect statistics information table.

[0167] Specifically, determining whether the data is abnormal includes:

[0168] Collect the moving average of the historical data of the same period for a preset time period to form a historical data set;

[0169] Get the average value of a historical data set and variance , and obtain the discriminant value Z;

[0170] in, ;

[0171] Determine whether Z is within the preset range. If not, it is considered as data abnormality.

[0172] As mentioned above, there are two types of data in the smart station integrated system. The first type is data that grows and accumulates over time, and the other type is data that fluctuates around a central value. For the first type of data, the data of each data collection cycle is subtracted from the data of the previous cycle to form new data. In this way, the first and second types of data have the same characteristics. This embodiment obtains data from the same period of data collected over the years, preferably data from the same period every day from the previous 1 day to the previous 15 days, and the moving average of 5 adjacent time points in the 10 collection cycles before today to form a data set.

[0173] The preset range is obtained by those skilled in the art based on historical data and will not be described in detail here.

[0174] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0175] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0176] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data integrity monitoring system for integrated energy monitoring and control of rail transit, characterized in that: include: The defect statistics configuration module is used to configure the defect statistics configuration file, and adopt different defect statistics methods according to different situations. The different defect statistics methods adopted according to different situations include: When the data collection time is less than the first time threshold, the defect data is counted in the process of collecting data; When the data collection time is greater than the first time threshold and less than the second time threshold, the defect data is counted by using the local statistical cache file, and the defect data is counted by reading the cache file; When the data collection time is greater than the second time threshold and less than the third time threshold, the defect data is counted by using the local statistical data file, and the defect data is counted by converting the cache file into a persistent file and reading the persistent file; When the data collection time is greater than the third time threshold, the defect data is counted by loading data from the database, the persistent file is written into the database, and the data in the database is read to count the defect data; Data statistical algorithm module, used to construct statistical algorithms for statistical data shortcomings; The data defect statistics module is used to determine the corresponding defect statistics method according to the defect statistics configuration module during the data collection process, and to count the data defects according to the statistical algorithm in the data statistical algorithm module, including: Obtain a meter reading task template from the database, where the meter reading task template stores the meter reading task ID, a collection device object ID set, a data type set, and a collection time interval; Create and initialize meter reading task collection objects. Each data object contains meter reading task id, device id set, data type set, collection time, and counter Count1. The device id set stores the id numbers of all devices involved in the meter reading task. The data type set is a set of data type id numbers that need to be collected in the meter reading task. The data type set can be used to query multiple data types that need to be collected. Counter Count1 is used to store the accumulated value of collected data in the meter reading task collection object. Initialize the data collection statistics table. The data collection statistics table is an object array. Each object contains the meter reading task id, the modulus value, and the counter Count2. It represents the value left after the device ID is modulo-operated: ； Where id is the device ID number, and the device ID number is modulo operation based on n, where n is an adjustable parameter. The device ID set is classified according to the characteristics of the serial number. is the value of each category, and counter Count2 is used to store the accumulated value of collected data in the collected data statistical information table; Initialize the detailed statistical table of collected information. The detailed statistical table of collected information is a hash table structure. Each data object includes device ID, collection time, counter Count3. Counter Count3 is used to store the accumulated value of collected data in the detailed statistical table of collected information. Determine the defect statistics method according to the defect statistics configuration module; According to different defect counting methods, defect counting is performed using steps corresponding to the defect counting methods.

2. A data integrity monitoring system for integrated energy monitoring and control of rail transit according to claim 1, characterized in that: The shortcomings statistics configuration module also includes: Configure whether to count the completeness rate based on the data collection situation; Set the file format for filtering incoming data; Configure whether to send the defect statistics data to the relevant supplementary recruitment process according to the data collection situation; Count the start time and end time of the shortcomings; Statistics collection completeness rate period.

3. According to claim 1, a data integrity monitoring system for integrated energy monitoring and control of rail transit is characterized in that: According to different defect statistics methods, the steps corresponding to the defect statistics methods are used to perform defect statistics, including: When collecting data, the defect data should include: Get the collected data message; Analyze the collected messages; Get the collected data and the corresponding device ID and data type; Complete the statistics of collected data defects information according to the data statistics algorithm process; Write the parsed data into a cache file whose name consists of the meter reading task id and time.

4. According to claim 1, a data integrity monitoring system for integrated energy monitoring and control of rail transit is characterized in that: According to different defect statistics methods, the steps corresponding to the defect statistics methods are used to perform defect statistics, including: When the local cache file is used to count the defect data, the defect data is counted by reading the cache file, including: According to the meter reading task template, search for the corresponding cache file in the cache folder by meter reading task ID and time; Read all data from the cache file in sequence; Get all devices and data types and values; Complete the statistics of collected data defects information according to the data statistics algorithm process; Move the cache files to other folders for conversion into storage data files by other processes.

5. The data integrity monitoring system for rail transit integrated energy monitoring and control according to claim 1 is characterized in that: According to different defect statistics methods, the steps corresponding to the defect statistics methods are used to perform defect statistics, including: When the local statistical data file is used to count the defect data, the defect data is counted by converting the cache file into a persistent file and reading the persistent file, including: According to the meter reading task template, open the corresponding data file from the storage folder; Read all data from the input data file in sequence and generate memory data objects; Get the corresponding device, data type and value; Complete the statistics of collected data defects information according to the data statistics algorithm process; Move data files into a backup folder and delete them periodically.

6. A data integrity monitoring system for integrated energy monitoring and control of rail transit according to claim 1, characterized in that: According to different defect statistics methods, the steps corresponding to the defect statistics methods are used to perform defect statistics, including: When counting defect data by loading data from a database, writing a persistent file to the database, and reading data from the database to count defect data, it includes: According to the meter reading task template, obtain the data type and device information that need to be collected within the cycle; Query database SQL; Query the database; Generate the storage data file based on the returned data results; Read all data from the input data file in sequence and generate memory data objects; Get the corresponding device, data type and value; Complete the statistics of collected data defects information according to the data statistics algorithm process; Move data files into a backup folder and delete them periodically.

7. The data integrity monitoring system for rail transit integrated energy monitoring and control according to claim 1 is characterized in that: The statistical algorithms for the statistical data shortcomings include: S1: Get the data to be processed: When it is a real-time collection mode, the data to be processed is the data of all data types of all devices in the data message collected this time; when it is a cache file mode, the data to be processed is the corresponding data that has been saved in the content; S2: Get a data object from the current address pointer; S3: Determine whether the data is abnormal. If abnormal, discard the data; S4: If normal, update the counter Count1 in the meter reading task collection object by accumulating 1 according to the meter reading task id corresponding to the data; S5: according to the corresponding meter reading task id and the device id, the meter reading task id corresponding to the modulo result data is updated by accumulating 1 in the counter Count2 in the collected data statistical information table; S6: Update the counter Count3 in the detailed statistics table of the collected information by accumulating 1 according to the corresponding device ID; S7: Determine whether the data statistics are complete. If not, point to the next data object and re-execute S3-S7. Otherwise, execute S8. S8: Filter whether the value of the counter Count1 in the meter reading task set object is consistent with the total number of expected collected data items Total1(l), if they are consistent, the counting ends; in: ； in For meter reading tasks The total number of all devices in The total number of data types of a device in the meter reading task; S9: If the counter Count1 of a certain item is inconsistent with Total1(l), filter the meter reading task id in the data object corresponding to the item, and find the item whose Count2 in the collected data statistical information table is inconsistent with the expected total number of collected data items Total2(p); in: ； in The number of subsets divided by the modulo operation for the device id; S10: Obtain a device ID subset for device ID modulo operation based on the items filtered out in S9, perform a direct query based on each ID in the device ID subset, and filter out items that are inconsistent with the expected total number of collected data items Total3(h); in: ； S11: Save the information of the items screened out in S10 in a defect statistics information table.

8. A data integrity monitoring system for rail transit integrated energy monitoring and control according to claim 7, characterized in that: The determining whether the data is abnormal includes: Collect the moving average of the historical data of the same period for a preset time period to form a historical data set; Get the average value of a historical data set and variance , and obtain the discriminant value Z; in, ; Determine whether Z is within the preset range. If not, it is considered as data abnormality.

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