Spacecraft control center architecture method for spaceflight TT&C ground station network dual data bus

Abstract

The application provides a task center architecture method for a spaceflight TT&C ground station network double data bus, belongs to the field of spaceflight remote sensing application, and solves the problem that traditional spaceflight business data organization and task architecture cannot meet the development requirement of a TT&C information system; after business data is classified and defined, the double data bus with high throughput and high reliability is used for transmission, a data driving engine is used to produce business data into data products, a storage mode is selected for storage, the storage mode includes file archiving, database storage, cache storage, full-text index storage and big data storage, the stored data products are acquired to obtain corresponding business data, and corresponding business operation is performed; the application reorganizes business data of a spaceflight TT&C information system, can meet the processing of massive data, can support the requirement of high reliability and high performance retrieval of core data, and thus guarantees the business capacity of the spaceflight TT&C information system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of space remote sensing application, and particularly relates to a task center architecture method for a space TT&C ground station network double data bus. BACKGROUND

[0002] In a traditional space TT&C ground station network, the business data transmission mode of a measurement operation control information system generally adopts a TCP / IP protocol and uses a database for storage. After receiving external business data, the software responsible for the transmission and reception of business data in the measurement operation control system stores the business data in a relational database for archiving. When other business software in the measurement operation control system performs business processing, the business data in the database is read to complete relevant business operations.

[0003] In recent years, with the rapid development of a space measurement operation control information system, the management range of the system has significantly increased, including the types, range and quantity of equipment; meanwhile, the business processing capacity and task scheduling capacity of the system have also increased, including the quantity, complexity and performance requirements of tasks. Changes and development of the business of the space measurement operation control information system have caused great changes in the business data to be processed by the information system of the system, and the information system gradually has the following characteristics: the scale of business data is large, and often faces the business requirements of 7x24 hours processing and reporting of massive business data; the reliability and real-time performance of key business data are high, such as demand and plan business, and therefore the safety of business data and the ability of business access to a large amount of business data in a short time need to be ensured.

[0004] In the traditional business data organization and task architecture method, the upgrade of the hardware of the system and the expansion of the database cannot meet the requirements of the order of magnitude and use performance of business data processing, and therefore seeking a new architecture method for business data processing, transmission and use to meet the requirements of the business of the space measurement operation control system has become a problem to be solved in the technical field of space remote sensing application. SUMMARY

[0005] The application proposes an organization and use architecture method for business data according to the characteristics of business data of a measurement operation control information system in a space TT&C ground station network. Through the method, the business data of the space measurement operation control information system is reorganized, which can not only meet the rapid processing of massive business data, but also support the high reliability and high performance retrieval requirements of core business data, thereby well ensuring the business capacity of the space measurement operation control information system.

[0006] The application achieves the purpose by adopting the following technical solutions:

[0007] The method is used for the task center architecture of the spaceflight TT&C ground station network dual data bus, and is used for the architecture construction of data management according to the characteristics of the spaceflight TT&C operation control information system business data.

[0008] According to the processing and use characteristics of the business data, the business data is classified and defined, and the business data contains corresponding configuration information.

[0009] The classified and defined business data is transmitted to the data transceiver software through a dual data bus channel, and the dual data bus channel includes a high-reliability data channel and a high-throughput data channel.

[0010] After the data transceiver software receives the business data, the business data is produced into a data product through a data driving engine, and the data product includes core data, index metadata and strategy metadata, and the core data is the main content information of the business data.

[0011] Through the data driving engine, a storage method is selected for the produced data product according to the strategy metadata, and the storage method includes file archiving, database storage, cache storage, full-text indexing storage and big data storage.

[0012] The stored data product is acquired, and the acquisition method includes query of hot data product and query of historical data product; according to the configuration information of the data product, the corresponding business data of the data product is obtained after the data product is acquired, and the corresponding business operation is performed.

[0013] Further, the classified and defined business data includes core class business data, mass class business data and ordinary class business data.

[0014] The core class business data mainly refers to the business data associated with the core business of the spaceflight TT&C operation control information system, including demand data, task plan data, forecast data and application data; the main characteristics are: real-time response is required, high reliability is required, and the data volume is moderate.

[0015] The mass class business data mainly refers to the business data associated with the resource management and control system, including equipment state parameter data (original frame format data / key value pair data) and task execution parameter data; the main characteristics are large data volume and 7*24 hours real-time transmission, and the reliability requirement is general.

[0016] The common type of business data mainly refers to non-critical business software associated business data, for example, the business data associated with the subsystems such as shift management, test simulation, including report type data and simulation type data; the main features are that the data volume is not large, the data content importance is general, and the reliability requirement is general.

[0017] Further, in the data transmission process, the core type of business data is transmitted through the high-reliability data channel, and the mass type of business data and the common type of business data are transmitted through the high-throughput data channel.

[0018] Further, the high-reliability data channel includes a message queue and a Redis database, and in the process of transmitting data using the high-reliability data channel, the data transmission is guaranteed by output confirmation and reception confirmation; the output confirmation refers to the operation of the business data producer confirming that the business data is delivered to the message queue; and the reception confirmation refers to the operation of the business data consumer confirming that the business data is pulled from the message queue and processed subsequently.

[0019] Further, the high-throughput data channel is composed of a Kafka message queue cluster, and in the process of transmitting data using the high-throughput data channel, the business data producer sends the business data to the high-throughput data channel in a single or batch mode by specifying the partition of the Kafka message queue; and the business data consumer pulls the business data in each partition of the Kafka message queue in a concurrent manner through a multi-thread framework and processes the business data subsequently.

[0020] Further, after the business data transmission is completed, in the production process of the data product, the index metadata is an abstract business data key field, and the business data key field includes occurrence time, source, type, size, associated equipment, associated demand, associated task, associated attribute, associated work plan, agreement, personnel, and location information.

[0021] Further, after the business data transmission is completed, in the production process of the data product, the strategy metadata is a related strategy for storing the business data, including a file archiving strategy, a database storage strategy, a cache storage strategy, a big data storage strategy, and a full-text index storage strategy.

[0022] The file archiving strategy refers to a strategy mode of storing the business data to a disk file system.

[0023] The database storage strategy refers to a strategy mode of storing the business data to a database system.

[0024] The cache storage strategy refers to a strategy mode of storing the business data to a cache.

[0025] Big data storage strategy: refers to the strategy mode of storing business data to the big data platform;

[0026] Full-text index storage strategy: refers to the strategy mode of storing business data index metadata information to the full-text index database.

[0027] In summary, the calculation formula of data product production is as follows:

[0028] Data_Product[Data_Row, MetaData_Index, MetaData_action] = product(RowData, Model)(RowData.dataType).

[0029] Further, in the storage process of the data product, the data driving engine reads the strategy metadata of the data product, sequentially checks the enabling states of the file archiving strategy, the database storage strategy, the cache storage strategy, the full-text index storage strategy and the big data storage strategy, executes the storage mode corresponding to the strategy whose enabling state is valid, and completes the storage process of the data product according to the configuration information of the corresponding strategy.

[0030] Further, in the process of obtaining the data product, the query of the hot data product is read by the data driving engine, and the configuration definition of the corresponding data product stored is read, wherein the core data in the recent time period is stored by the cache storage mode; the hot data product query mode is adopted, and the user can quickly obtain the corresponding business data information from the cache according to the key configuration information set in the configuration.

[0031] Further, in the process of obtaining the data product, the query of the historical data product includes batch historical data query and associated historical data query; the storage mode of the corresponding data product in the batch historical data query mode is the database storage mode, the traditional database query mode is adopted, the index information issued by the data acquisition party is used to search the database table defined in the business data configuration, and the data is searched through SQL; the storage mode of the corresponding data product in the associated historical data query mode is the full-text index storage mode, the associated historical data query is a query mode through the full-text retrieval database, and the business data related to the keyword can be searched from the full-text retrieval database by reading the search condition issued by the data acquisition party.

[0032] As described above, since the technical solution is adopted, the application has the following advantages:

[0033] The application provides a task center architecture method for a spaceflight measurement and control ground station network double data bus, and focuses on solving the problems of various business data, large data volume, high performance reliability requirement in a spaceflight measurement and control information system; through targeted classification of business data characteristics, and matching of two data transmission channels of high reliability and high throughput, the processing of business data can be more reliable, faster and more orderly, the efficient and reliable operation of core business software of the spaceflight measurement and control system is met, the internal requirements of the spaceflight measurement and control ground information system are met, and the spaceflight business capability is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 It is a schematic diagram of the architecture method of the application;

[0035] Figure 2 It is a working flow schematic diagram of the high-throughput data channel;

[0036] Figure 3 It is a working flow schematic diagram of the high-reliability data channel;

[0037] Figure 4 It is a working flow schematic diagram of data product production;

[0038] Figure 5 It is a working flow schematic diagram of data product storage;

[0039] Figure 6 It is a working flow schematic diagram of data product acquisition. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. The components of the embodiments of the application described and shown in the drawings can be arranged and designed in various different configurations.

[0041] Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0042] As Figure 1As shown, the task center architecture method facing the dual data bus of the spaceflight TT&C ground station network is configured and built according to the characteristics of the spaceflight TT&C operation control information system business data; in the method, the business data involved in the spaceflight TT&C operation control information system is subjected to the whole life cycle operation of classification, transmission, production, storage and acquisition, specifically including:

[0043] According to the disposal and use characteristics of the business data, the business data is classified and defined, and the business data contains corresponding configuration information;

[0044] The classified and defined business data is transmitted to the data transceiving software by using a dual data bus channel, and the dual data bus channel includes a high-reliability data channel and a high-throughput data channel;

[0045] After the data transceiving software receives the business data, the business data is produced into a data product by a data driving engine, the data product includes core data, index metadata and strategy metadata, and the core data is the main content information of the business data;

[0046] According to the strategy metadata, the data driving engine selects a storage mode for the produced data product, and stores the data product, the storage mode includes file archiving, database storage, cache storage, full-text index storage and big data storage;

[0047] The stored data product is acquired, and the acquisition mode includes query of hot data product and query of historical data product; according to the configuration information of the data product, the corresponding business data of the data product is obtained after the data product is acquired, and the corresponding business operation is performed.

[0048] This embodiment will take a specific business data processing as an example to explain the architecture method in detail.

[0049] Firstly, the classified and defined business data includes core class business data, mass class business data and ordinary class business data.

[0050] Among them, the core class business data mainly refers to the business data associated with the core business of the spaceflight TT&C operation control information system, including demand data, task plan data, forecast data and application data; its main characteristics are: real-time response is required, high reliability is required, and the data volume is moderate.

[0051] The mass class business data mainly refers to the business data associated with the resource management and control system, including equipment state parameter data (original frame format data / key value pair data) and task execution parameter data; its main characteristics are large data volume and 7*24 hours real-time transmission, and the reliability requirement is general.

[0052] The common type of business data mainly refers to the business data associated with non-critical business software, such as the business data associated with the shift management, test simulation and other sub-systems, including report data and simulation data; the main features are that the data volume is not large, the data content importance is general, and the reliability requirement is general.

[0053] In this embodiment, the business data is the state parameter data of the XXXX type device of XX station. The data is reported from the device end every second, 7x24 hours, and belongs to the mass business data type. The business data received from the device end is in JSON format, and the data information is stored in the mode of key value, as follows:

[0054] {"HappendTime":"2021-12-28",

[0055] "DeviceID":"DNBACKOT2_IFRecv215:44:52.092",

[0056] "Station":"XX",

[0057] "IFSend2AmpStatus":0,

[0058] "IFSend2Current12":12,

[0059] "IFSend1AmpStatus":0,

[0060] "IFSend1Current12":1,

[0061] "IFSend1Current5":3.12,

[0062] "IFSend1Vol5":2.3}

[0063] Next, the transmission of business data is carried out. During the data transmission process, the core type of business data is transmitted through the high reliability data channel, and the mass type of business data and the common type of business data are transmitted through the high throughput data channel.

[0064] Please refer to the schematic diagram of Figure 3 The high reliability data channel includes message queue and Redis database. During the process of transmitting data using the high reliability data channel, the data transmission is guaranteed by output confirmation and reception confirmation.

[0065] Production confirmation refers to the process where the business data producer confirms the delivery of business data to the message queue. Before sending a message, the business data producer first caches the business data to be sent in the Redis database. The business data producer then pushes the message to the message queue and waits for a response. If the business data producer does not receive a response from the message queue within the timeout period, it resends the business data to the message queue. This production confirmation mechanism effectively ensures the reliability of business data delivery to the message queue.

[0066] Receiving confirmation refers to the business data consumer's confirmation of retrieving business data from the message queue and proceeding with subsequent processing. Upon successful operation, a manual confirmation message is sent to the message queue, completing message consumption. If the operation fails, the business data consumer can retrieve the data from the message queue again for processing. If the message queue is faulty, the consumer can also retrieve the data from the Redis database for processing, thus completing the business data consumption process. This production and consumption confirmation mechanism effectively ensures the reliable transmission of core business data.

[0067] See also Figure 2 As illustrated, the high-throughput data channel consists of a Kafka message queue cluster. During the data transmission process using the high-throughput data channel, business data producers send business data to the high-throughput data channel individually or in batches by specifying the partitions of the Kafka message queue. Business data receivers use a multi-threaded framework to concurrently pull business data from each partition of the Kafka message queue and perform subsequent processing operations.

[0068] In this embodiment, the status parameter data of the XXXX model equipment at station XX is transmitted through a Kafka message queue on a high-throughput data channel. After receiving the business data, the data transceiver software selects the corresponding partition (XX-XXXX) of the high-throughput data channel message queue and pushes the data to the message queue.

[0069] Next, we will produce data products. (See below for details.) Figure 4 As illustrated, after the business data transmission is completed, during the production process of the data product, the index metadata is an abstract key field of the business data. The key fields of the business data include: occurrence time, source, type, size, associated device, associated requirement, associated task, associated attribute, associated work plan, agreement, personnel, and location information.

[0070] Policy metadata refers to the policies related to business data storage, including file archiving policies, database storage policies, cache storage policies, full-text index storage policies, and big data storage policies, among which:

[0071] File archiving strategy: refers to the strategy pattern for storing business data in a disk file system;

[0072] Database storage strategy: refers to the strategy mode of storing business data into the database system;

[0073] Cache storage strategy: refers to the strategy mode of storing business data into the cache;

[0074] Big data storage strategy: refers to the strategy mode of storing business data into the big data platform;

[0075] Full-text index storage strategy: refers to the strategy mode of storing business data index metadata information into the full-text index database.

[0076] In summary, the calculation formula of data product production is as follows:

[0077] Data_Product[Data_Row,MetaData_Index,MetaData_action]=product(RowData,Model)(RowData.dataType).

[0078] In this embodiment, the data driving engine reads the configuration file according to the type of the XX station XXXX type device state parameter of the business data. The configuration information of the XX station XXXX type device state parameter is written in YAML format, and the configuration of the data product can be referred to the schematic diagram of Figure 1 The configuration of the data product includes index element configuration and strategy element configuration. The index element configuration defines the selected field of the index; the strategy element configuration defines the subsequent storage requirement of the data product.

[0079] The data driving engine reads the data product configuration information to obtain the configuration information of the business data production, that is: "Compressed: true". According to the configuration, the data driving engine will compress the business data, and after compression, the data driving engine will remove the corresponding key value and store in the form of Value array. The compressed business data is as follows:

[0080] {2021-12-28 15:44:52.092 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 1; 3.12; 2.3;}.

[0081] The data driving engine reads the data product configuration information to obtain the configuration information of the index metadata, that is: "Fields: DeviceID, HappendTime, Station". The configuration defines the fields of the index combination as device ID, occurrence time and station information. According to the configuration, the system extracts the value of the corresponding key value of the original business data to generate the index information of the index metadata, as follows:

[0082] {"DeviceID":"DNBACKOT2_IFRecv2",

[0083] "HappendTime":"2021-12-28 15:44:52.092",

[0084] "Station":"XX",}

[0085] The data-driven engine reads the data product configuration information, copies the configuration information of the policy metadata, and reads the key fields in the original business data to generate the policy metadata, as follows:

[0086] Storage:

[0087] FileMode:

[0088] Enabled:true

[0089] Pattern:${Root_PATH} / RESMCS / XX-XXXX-DNBACKOT2_IFRecv2-{yyyy-MM-dd}.log

[0090] MySQLMode:

[0091] Enabled:true

[0092] Schema:ZYGK_RESMCS

[0093] Table:TB_ZYGK_RESMCS_XX-XXXX-DNBACKOT2_IFRecv2

[0094] FullIndexMode:

[0095] Enabled:true

[0096] Publish:

[0097] Enabled:true

[0098] ExpireDate:100000

[0099] FixKey:RESMCS_XX-XXXX-DNBACKOT2_IFRecv2

[0100] Next, the storage process of the data product is performed, which can be referred to in Figure 5The schematic diagram is shown in the figure. In the storage process of the data product, the data driving engine reads the policy metadata of the data product, sequentially checks the enabling states of the file archiving policy, the database storage policy, the cache storage policy, the big data storage policy and the full-text index storage policy, and sequentially executes the storage mode corresponding to the policy with the valid enabling state, while completing the storage process of the data product according to the configuration information of the corresponding policy.

[0101] The specific checking process of the enabling state is as follows: first, the data driving engine reads the configuration of the file storage, and if it is determined to be enabled, the business data is stored into the file system according to the file name defined by the configuration, and the position information in the index metadata is updated; second, the data driving engine reads the configuration of the database storage, and if it is determined to be enabled, the business data is inserted into the corresponding database table according to the definition of the database and the data table, and the position information in the index metadata is updated; third, the data driving engine reads the configuration of the cache storage, and if it is determined to be enabled, the business data is inserted into the cache according to the setting of the key and the overage time, and the position information in the index metadata is updated; fourth, the data driving engine reads the configuration information of the big data storage, and if it is determined to be enabled, the business data is stored into the big data system according to the address corresponding to the big data; and finally, the data driving engine reads the configuration information of the full-text index, and if it is determined to be enabled, the updated index metadata is stored into the full-text index database, thereby completing the storage process of the data product.

[0102] In this embodiment, the data driving engine reads the policy metadata information of the data product to complete the storage function of the data product. For the XX station XXXX type device state parameter business data product, the storage of the data product includes four modes: file storage, database storage, full-text index storage and big data storage.

[0103] The file storage is a process of storing the business data in the file system for archiving. The data driving engine reads the policy of the file storage in the policy metadata in the data product, and stores the business data in the data product according to the storage policy. The policy of the file storage in the policy metadata in the data product is as follows:

[0104] FileMode:

[0105] Enabled: true

[0106] Pattern: ${Root_PATH} / RESMCS / XX-XXXX-DNBACKOT2_IFRecv2-{yyyy-MM-dd}.log

[0107] Among them, the configuration information Enabled option is required to store files; the Pattern option confirms the specification of the stored file name.

[0108] The business data in the data product is as follows:

[0109] {2021-12-28 15:44:52.092 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 1; 3.12; 2.3;}

[0110] After file storage is completed, the final file location is as follows:

[0111] / DataRoot / RESMCS / XX-XXXX-DNBACKOT2_IFRecv2-2021-12-28.log

[0112] The content of the file is as follows: (The last line is newly added this time)

[0113] 2021-12-28 15:44:49.062 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 1; 2.12; 2.2;

[0114] 2021-12-28 15:44:50.042 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 0; 3.3; 2.3;

[0115] 2021-12-28 15:44:51.052 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 1; 2.14; 2.3;

[0116] 2021-12-28 15:44:52.092 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 1; 3.12; 2.3;

[0117] After file storage is completed, the storage location information of the business data is updated to the index metadata at the same time, and the updated index metadata is as follows:

[0118]

[0119] Database storage refers to the process of storing business data in a database for saving. The data driving engine reads the database storage strategy in the strategy metadata in the data product, and stores the business data in the data product according to the storage strategy. In the data product, the strategy metadata about the database storage strategy is as follows:

[0120] MySQLMode:

[0121] Enabled: true

[0122] Schema: ZYGK_RESMCS

[0123] Table: TB_ZYGK_RESMCS_XX-XXXX-DNBACKOT2_IFRecv2

[0124] Among them, the configuration information Enabled is configured to determine whether database storage is required; Schema is configured to determine the database for storing business data; and Table is configured to determine the data table for storing business data. The business data in the data product is as follows:

[0125] {2021-12-28 15:44:52.092 DNBACKOT2_IFRecv2; XX; 0; 12; 0; 1; 3.12; 2.3;}

[0126] The data driving engine automatically implements the splicing of the database SQL according to the read business data field and inserts it into the database, as shown in Table 1 below (the last line is newly added this time):

[0127] Table 1

[0128]

[0129]

[0130] After the database storage is completed, the data driving engine updates the storage location information of the business data to the index metadata, and the updated index metadata is as follows:

[0131]

[0132] Cache storage refers to storing the hot data of business data in the cache database. The configuration of the cache in the strategy metadata file is as follows:

[0133] Redis:

[0134] Enabled: true

[0135] ExpireDate: 100000

[0136] FixKey: RESMCS_XX-XXXX-DNBACKOT2_IFRecv2_XX

[0137] Among them, the configuration information Enabled option is explicitly required to publish data products; ExpireDate defines the expiration time of the data product; FixKey defines the key value of the data product storage.

[0138] The data driving engine stores the data product into the cache of Redis according to the above configuration definition, as follows:

[0139] Key: RESMCS_XX-XXXX-DNBACKOT2_IFRecv2_52

[0140] Value: {"HappendTime": "2021-12-28",

[0141] "DeviceID": "DNBACKOT2_IFRecv2_15:44:52.092",

[0142] "Station": "XX",

[0143] "IFSend2AmpStatus": 0,

[0144] "IFSend2Current12": 12,

[0145] "IFSend1AmpStatus": 0,

[0146] "IFSend1Current12": 1,

[0147] "IFSend1Current5": 3.12,

[0148] "IFSend1Vol5": 2.3}

[0149] Full-text indexing refers to storing the index metadata of business data in a full-text search database. The data driving engine reads the full-text search strategy in the strategy metadata in the data product, and stores the business data in the data product according to the storage strategy. The strategy metadata about the full-text search strategy in the data product is as follows:

[0150] FullIndexMode:

[0151] Enabled: true

[0152] Among them, the configuration information Enabled configuration determines whether full-text storage is required; the index metadata in the data product is as follows:

[0153]

[0154] The data driving engine stores the index into a full-text index database, where the index ID is the current date plus a workflow number, as follows:

[0155]

[0156]

[0157] Next, the data product is acquired, which can be seen from the schematic diagram of Figure 6 In the process of acquiring the data product, the query of the hot data product is performed by reading the stored configuration definition of the corresponding data product through the data driving engine, where the core data of the nearest time period is stored in the cache storage mode; using the hot data product query mode, the user can quickly acquire the corresponding business data information from the cache through the key configuration information set in the configuration.

[0158] The query of the historical data product includes batch historical data query and associated historical data query; the batch historical data query mode uses the database storage mode for the corresponding data product, uses the traditional database query mode, and performs data retrieval in the database table defined in the business data configuration according to the index information issued by the data acquisition party through SQL; the associated historical data query mode uses the full-text index storage mode for the corresponding data product, and the associated historical data query is a query mode performed through the full-text retrieval database, and the business data related to the keyword can be retrieved from the full-text retrieval database by reading the retrieval condition issued by the data acquisition party.

[0159] In this embodiment, the XX station XXXX type device state parameter data can be used for three types of data acquisition scenarios. Specifically, the hot data is acquired through the cache database, the historical business data is batch queried through the database, and the associated historical business data is queried through the full-text retrieval database.

[0160] The hot data acquired through the cache database is mainly used for real-time display of the device state parameter, and the index of the information to be displayed is sent to the data acquisition service in the format of the JSON protocol by the front-end browser, as follows:

[0161]

[0162] The data acquisition service reads the business data configuration information and the index parameter transmitted by the front end, and the device state parameter only retains the hot data for 1 minute, and the key of the cache database is a combination of the sub-system, the station, the type, the device ID, and the second, as follows:

[0163] RESMCS_XX-XXXX-DNBACKOT2_IFRecv2_52

[0164] The data content obtained from the cache is as follows:

[0165]

[0166] The mode of batch querying historical business data through a database is mainly used for querying historical device state data. The front-end browser sends the index of information to be displayed to the data acquisition service in the format of a JSON protocol, and the specific mode is as follows:

[0167]

[0168] The data acquisition service queries the corresponding records from the database table ZYGK_RESMCS.TB_ZYGK_RESMCS_XX-XXXX-DNBACKOT2_IFRecv2 through SQL according to the configuration, and the specific retrieval statement is as shown in Table 2:

[0169] Table 2

[0170] Table 2

[0171]

[0172] The mode of querying associated historical business data through a full-text retrieval database is mainly used for querying business data associated with the relationship. In the present example, the related records of the device DNBACKOT2_IFRecv2 with the parameter IFSend1Current5 in the range of 3.12 to 3.14 can be checked, and the specific retrieval statement is as shown below:

[0173]

[0174] The obtained results are as follows:

[0175]

[0176]

[0177] In summary, through the governance process of specific business data in the present example, the task architecture method of the dual data bus of the spaceflight measurement and control information system is formed, and the problem of various types of business data, large volume, high performance reliability requirements in the spaceflight measurement and control information system is solved.

Claims

1. A mission center architecture method for aerospace telemetry, tracking, and command (TT&C) ground station network with dual data buses, characterized in that: The entire lifecycle of operations, including classification, transmission, production, storage, and retrieval, is performed on the business data involved in the aerospace telemetry, tracking, and command (TT&C) information system. This specifically includes: Based on the characteristics of business data processing and usage, business data is classified and defined, and the business data contains corresponding configuration information; The business data after classification definition is completed is transmitted to the data transceiver software using a dual data bus channel, which includes a high-reliability data channel and a high-throughput data channel. After receiving business data, the data transceiver software uses a data-driven engine to produce data products from the business data. These data products include core data, index metadata, and strategy metadata. The core data is the main content information of the business data. The data-driven engine selects a storage method for the data products after production based on the strategy metadata, and stores the data. The storage methods include file archiving, database storage, cache storage, full-text index storage, and big data storage. The data products that have been stored are retrieved, including queries for hot data products and queries for historical data products; based on the configuration information of the data products, the corresponding business data is obtained after retrieving the data products, and corresponding business operations are performed. The business data, after being categorized and defined, includes core business data, massive business data, and general business data; the core business data includes demand data, task plan data, forecast data, and application data; the massive business data includes equipment status parameter data and task execution parameter data; and the general business data includes report data and simulation data. During data transmission, the core business data is transmitted through the highly reliable data channel, while the massive business data and ordinary business data are transmitted through the high-throughput data channel. The high-throughput data channel consists of a Kafka message queue cluster. During the data transmission process using the high-throughput data channel, business data producers send business data to the high-throughput data channel individually or in batches by specifying the partitions of the Kafka message queue. Business data consumers use a multi-threaded framework to concurrently pull business data from each partition of the Kafka message queue and perform subsequent processing operations.

2. The mission center architecture method for aerospace telemetry and control ground station network with dual data buses as described in claim 1, characterized in that: The highly reliable data channel includes a message queue and a Redis database. During data transmission using the highly reliable data channel, data transmission is ensured to be completed through output confirmation and reception confirmation. Output confirmation refers to the operation of the business data producer confirming the delivery of the business data to the message queue. Reception confirmation refers to the operation of the business data consumer confirming the retrieval of the business data from the message queue for subsequent processing.

3. The mission center architecture method for aerospace telemetry and control ground station network with dual data buses as described in claim 1, characterized in that: After the business data transmission is completed, during the production process of the data product, the index metadata is an abstract key field of business data. The key fields of business data include: occurrence time, source, type, size, associated device, associated requirement, associated task, associated attribute, associated work plan, agreement, personnel, and location information.

4. The mission center architecture method for aerospace telemetry and control ground station network with dual data buses as described in claim 1, characterized in that: After the business data transmission is completed, during the production process of the data product, the strategy metadata refers to the relevant strategies for business data storage, including file archiving strategy, database storage strategy, cache storage strategy, big data storage strategy, and full-text index storage strategy.

5. The mission center architecture method for aerospace telemetry and control ground station network with dual data buses as described in claim 4, characterized in that: During the storage process of data products, the data-driven engine reads the policy metadata of the data products, checks the enabling status of file archiving policy, database storage policy, cache storage policy, big data storage policy and full-text index storage policy in turn, executes the storage method corresponding to the policy with the effective enabling status in turn, and completes the storage process of data products according to the configuration information of the corresponding policy.

6. The mission center architecture method for aerospace telemetry and control ground station network with dual data buses as described in claim 1, characterized in that: During the acquisition of data products, the query of the hot data products is carried out by reading the configuration definition of the corresponding data products that have been stored through the data-driven engine. The core data of the most recent time period is stored in a high-speed cache storage method.

7. The mission center architecture method for aerospace telemetry and control ground station network with dual data buses as described in claim 6, characterized in that: In the process of acquiring data products, the query of the historical data products includes batch historical data query and associated historical data query; the data product storage method corresponding to the batch historical data query method is database storage method; the data product storage method corresponding to the associated historical data query method is full-text index storage method.

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