Data acquisition and analysis method of fully mechanized coal mining face and related device

Abstract

The invention provides a data acquisition and analysis method for a fully mechanized coal mining face and a related device, the fully mechanized coal mining face comprises at least two fully mechanized coal mining devices, and the acquisition and analysis method comprises the following steps: acquiring instruction information; acquiring basic data of each working face based on the instruction information, wherein the basic data comprises control data and monitoring data; sending the basic data to a Kafka message queue; and analyzing the message queue, and carrying out hierarchical storage on the basic data. According to the data acquisition and analysis method for the fully mechanized coal mining face and the related device provided by the invention, the data is respectively acquired and uniformly distributed, so that the uniform data acquisition and storage of different data sources are realized, and the concurrent processing, the control flow of the client and the related control command are realized by setting the message queue; the efficiency of data acquisition is improved, and meanwhile, the real-time data display is realized by using the message queue, so that the real-time performance of data monitoring is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical fields related to intelligent manufacturing and intelligent production lines, and in particular to the field of remote operation and maintenance of intelligent comprehensive mining working faces. Specifically, it relates to a data collection and analysis method and related devices for comprehensive mining working faces. Background Art

[0002] The digital twin technology in the coal mining industry is still in the initial stage of innovation. Currently, the main research is on remote visual monitoring of large equipment. It has not yet integrated the information of equipment with mining geological conditions and mining environment to form a transparent digital twin mining scene based on a unified geodetic coordinate system, providing a visual remote control environment and platform for realizing truly unmanned or unmanned mining.

[0003] Among them, collecting and analyzing the perception data of the monitoring system of major equipment in coal mines is an important way to achieve traceability management and risk control of mining equipment. In related technologies, although Kafka is used to collect data from the monitoring system of underground target equipment, it can realize real-time data collection of a single mine, but it is not possible to collect data from multiple mines. It lacks a big data foundation and fails to integrate corresponding functions to effectively control the operation of the collection service, such as automatically restarting the collection service when an exception occurs to ensure data continuity and integrity.

[0004] Therefore, there is a technical problem in the prior art that it is impossible to realize real-time collection of data from multiple mines. Summary of the invention

[0005] In view of this, the present disclosure aims to provide a data collection and analysis method and related devices for a comprehensive mining face, so as to solve the technical problem existing in the prior art that it is impossible to realize real-time collection of data from multiple mines.

[0006] An object of the embodiments of the present disclosure is to provide a data collection and analysis method for a fully mechanized mining face, wherein the fully mechanized mining face includes at least two fully mechanized mining devices, and the method includes:

[0007] Get instruction information;

[0008] Based on the instruction information, basic data of each comprehensive mining equipment is collected, wherein the basic data includes control data and monitoring data;

[0009] Send the basic data to the Kafka message queue;

[0010] The message queue is parsed, and the basic data is stored in layers.

[0011] In some embodiments, before sending the basic data to the Kafka message queue, the method further includes: encrypting the basic data.

[0012] In some embodiments, the message queue is parsed and the basic data is stored in layers, including: parsing the real-time data and the historical data separately; sending the real-time data to a buffer database, and sending the historical data to a memory database.

[0013] In some embodiments, the obtaining of instruction information includes: receiving instruction information; parsing the instruction information to obtain the category of the instruction, wherein the category includes configuration commands, restart commands, and start / stop commands; and generating execution information or feedback information based on the category of the instruction.

[0014] In some embodiments, the generation of execution information or feedback information based on the category of the instruction includes: determining whether the instruction is a configuration command, and when the instruction is a configuration command, operating a configuration file and generating an execution result message; when the instruction does not belong to a configuration command, determining whether the instruction is a control command, and when the instruction does not belong to a control command, generating and returning an error message.

[0015] In some embodiments, when the instruction is a control command, it is determined whether the instruction is a restart command. When the instruction is a restart command, it is determined whether to process the acquisition state. If it is in the acquisition state, the acquisition program is controlled to restart and an execution result message is generated. When the instruction is a start-stop command, it is determined whether the instruction action is consistent with the acquisition state. If not, the start-stop command is executed. If consistent, an error message is generated.

[0016] In some embodiments, when multiple instruction information are received, old instruction information is discarded to save the latest instruction information by controlling the queue length.

[0017] In some embodiments, the hierarchical storage includes desensitizing the data, blurring and encrypting important information while storing the data; and generating data in a unified format by dimensional processing of the data.

[0018] Another object of the embodiment of the present disclosure is to provide a data collection and analysis device for a fully mechanized mining face, the data collection and analysis device comprising:

[0019] An acquisition module, wherein the acquisition module is used to acquire instruction information;

[0021] A collection module, the collection module is used to collect basic data of each comprehensive mining equipment based on the instruction information, wherein the basic data includes control data and monitoring data;

[0022] A sending module, wherein the sending module is used to send the basic data to a Kafka message queue;

[0023] A storage module is used to parse the message queue and store the basic data in a hierarchical manner.

[0024] Another object of the embodiments of the present disclosure is to provide a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the data collection and analysis method for a comprehensive mining face as described in any one of the above is implemented.

[0025] Another object of an embodiment of the present disclosure is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a data collection and analysis method for a comprehensive mining face as described in any one of the above is implemented.

[0026] The data collection and analysis method of the comprehensive mining face provided by the embodiment of the present disclosure includes at least two comprehensive mining equipment, and the collection and analysis method includes the following steps: obtaining instruction information; collecting basic data of each working face based on the instruction information, wherein the basic data includes control data and monitoring data; sending the basic data to the Kafka message queue; parsing the message queue, and storing the basic data in layers. By collecting and distributing the data separately, unified data collection and storage of different data sources are realized, and by setting the message queue, concurrent processing and the control process and related control commands of the client are realized.

[0027] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In the drawings that are not necessarily drawn to scale, the same reference numerals may describe similar parts in different views. The same reference numerals with letter suffixes or different letter suffixes may represent different instances of similar parts. The drawings generally illustrate various embodiments by way of example and not limitation, and together with the specification and claims, are used to illustrate the disclosed embodiments. When appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method. The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of this application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the drawings:

[0029] Figure 1 It is a flow chart of the steps of the data collection and analysis method for a fully mechanized mining face provided by the present disclosure;

[0030] Figure 2 is a flowchart of the steps for obtaining instruction information provided by the present disclosure;

[0031] Figure 3 It is a schematic diagram of steps for generating execution information or feedback information based on the category of the instruction provided by the present disclosure;

[0032] Figure 4 It is a structural block diagram of the data acquisition and analysis device for the fully mechanized mining working face provided by the present disclosure;

[0033] Figure 5 It is a schematic diagram of the structure of the electronic device provided by the present disclosure. DETAILED DESCRIPTION

[0034] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, but are not intended to limit the present disclosure.

[0035] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0036] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0037] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0038] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0039] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0040] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0042] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0043] In order to solve the technical problem in the prior art that it is impossible to realize real-time data collection of multiple mines, the present invention provides a data collection and analysis method and related devices for a fully mechanized mining face.

[0044] The comprehensive mining working face disclosed in the present invention is a comprehensive mining working face including at least two comprehensive mining equipment, wherein the comprehensive mining equipment may be, for example, a coal mining machine, a hydraulic support, a transfer machine, an emulsification pump, and the like.

[0045] Figure 1 The following is a flow chart showing the steps of the data collection and analysis method for a fully mechanized mining face provided by the present disclosure. Figure 1 As shown, the data collection and analysis method of the fully mechanized mining face includes the following steps:

[0046] S101, obtaining instruction information.

[0047] In this step, instruction information is obtained, wherein the instruction information is used to instruct the information collection device on the comprehensive mining equipment to collect information.

[0048] Specifically, the instruction information can be sent by the staff to the server through the control terminal and obtained by the client, so that the information collection device located at the client obtains the control instruction sent by the control terminal by obtaining the instruction information of the server.

[0049] During the device status monitoring process, control commands can only be used to operate online devices. When using control commands, the front end will change the current acquisition status according to the command request, including running, starting, stopping, and restarting. If the status is not running, you cannot click on the operation-related commands. In order to ensure that the client receives the correct instruction information, when multiple commands arrive at the server at the same time, the length of the RPC queue is controlled to discard the old operation commands and only save the latest operation command.

[0050] Since there are multiple fully mechanized mining equipment on the working surface, and the control instructions of each fully mechanized mining equipment have their own independent message queue, the control commands of different acquisition terminals do not interfere with each other. At the same time, the data acquisition start time, end time and data source of the acquisition terminal can be realized by modifying the configuration file.

[0051] Further, Figure 2 The flowchart of the steps of obtaining instruction information is shown. As shown in the figure, obtaining instruction information includes the following steps:

[0052] S201, receiving instruction information.

[0053] In this step, instruction information is received, wherein the instruction information is sent by the server and temporarily stored in the RabbitMQ service queue.

[0054] Specifically, the control end can be a web page or a mobile phone app, which is not further limited in the present disclosure. For example, when the control end is in the form of a web, the control end sends control instruction information to the server end through an Http request, and the server end parses the instruction information after receiving the instruction information.

[0055] First, determine whether the instruction information content includes configuration parameters. If so, parse the configuration file. Next, determine whether it is an update command. If it is an update command, update the persistent database. If not, generate a message and encrypt it and send it to the RPC queue.

[0056] S202, parsing the instruction information to obtain the category of the instruction, wherein the category includes configuration command, restart command and start / stop command; and generating execution information or feedback information based on the category of the instruction.

[0057] After completing the above step S201, in this step, the instruction information is parsed to obtain the category of the instruction, wherein the category includes configuration command, restart command and start / stop command; based on the category of the instruction, execution information or feedback information is generated.

[0058] Specifically, the process of controlling the client mainly involves commands such as configuring files, modifying configuration files, starting, stopping, and restarting. After receiving the corresponding command, the server parses the parameters and determines the type of the parsed command, and generates execution information or feedback information based on the type of the command.

[0059] like Figure 3 As shown, based on the category of the instruction, generating execution information or feedback information includes the following steps:

[0060] S301, determining whether the instruction is a configuration-related command.

[0061] In this step, it is first determined whether the instruction request content contains configuration parameters. When the instruction is a configuration command, the configuration file is parsed, and an execution result message is generated and sent to the Rpc_ack queue specified by the server.

[0062] S302: Determine whether the instruction is a control command.

[0063] After completing the above step S301, when the instruction does not belong to a configuration-related command, in this step, it is determined whether the instruction belongs to a control command.

[0064] Specifically, the control command is used to instruct the device to perform a specific action. When the instruction does not belong to a control command, an error message is generated and the message is sent to the Rpc_ack queue specified by the server.

[0065] S303: Determine whether the instruction is a restart command.

[0066] After completing the above step S302, when the instruction is a control command, in this step, it is determined whether the instruction is a restart command.

[0067] Specifically, when the instruction is a control command, it is further determined whether the instruction is a restart command. If the instruction is a restart command, the current state of the acquisition program is determined. If the current acquisition program is in operation, the restart command is executed; if the current acquisition program is in a stopped state, the acquisition program is started, an execution result message is generated, and the message is sent to the Rpc_ack queue specified by the server.

[0068] S304, determining whether the instruction is a start / stop command.

[0069] After completing the above step S303, when the instruction is not a restart command, in this step, it is determined whether the instruction is a start-stop command.

[0070] Specifically, when the instruction does not belong to a restart command, it is determined whether the instruction is a start or stop command. When the instruction is a start-stop command, it is determined whether the instruction action is consistent with the acquisition state. If not, the start-stop command is executed, an execution result message is generated, and the message is sent to the Rpc_ack queue specified by the server; if consistent, an error message is generated.

[0071] After receiving the message, the server decrypts it to determine whether it is for obtaining configuration. If it is

[0073] The configuration file is parsed and stored in a persistent database, and finally returned to the control end to display the results.

[0074] S102: Collect basic data of each comprehensive mining equipment based on the instruction information, wherein the basic data includes control data and monitoring data.

[0075] After obtaining the instruction information, in this step, basic data of each comprehensive mining equipment is collected based on the instruction information, wherein the basic data includes control data and monitoring data.

[0076] Specifically, the basic data includes control data and monitoring data, and the basic data is collected and obtained through sensors installed on the terminal equipment. Among them, the sensors include: speed sensors, current sensors and temperature sensors installed on the traction motor and cutting motor of the coal mining machine, which are used to collect the traction speed, cutting current and cutting temperature of the coal mining machine; angle sensors, pressure sensors and mining height sensors installed on the top beam and column of the hydraulic support, which are used to collect the angle of the top beam / base, column pressure and support mining height data; temperature sensors, current sensors and voltage sensors installed on the motor of the transfer machine, which are used to collect the temperature, current and voltage of the transfer machine; temperature sensors and pressure sensors installed on the pump station and oil tank of the emulsification pump, which are used to collect bearing temperature, oil temperature and oil pressure; speed sensors and current sensors installed on the tail drum motor of the belt conveyor, which are used to collect belt speed and motor current.

[0077] S103, sending the basic data and historical data to the Kafka message queue.

[0078] After completing the above step S102, in this step, the basic data and historical data are sent to the Kafka message queue; by using the message queue method to perform concurrent data collection, it is beneficial to improve the efficiency of data collection, and at the same time, by using the message queue method to realize the display of real-time data, the real-time nature of data monitoring is improved.

[0079] Specifically, after the basic data is collected, the basic data and historical data are encrypted and pushed to the corresponding Kafka topics, corresponding to the real-time and historical data of different working faces respectively. For real-time data, the rapid consumption of multiple data is realized by means of message queues located in the buffer database, which improves the timeliness, accuracy and response delay of the real-time data display of multiple working faces. For historical data, it is directly stored in the original data layer of the data warehouse through the data analysis module, providing a unified data format for subsequent data.

[0080] By using encryption technology during the data transmission process, the security of the data transmission process is improved. At the same time, it can support breakpoint resumption during the transmission process. When affected by network fluctuations, the data is stored locally and uploaded again when the network is unobstructed, ensuring the integrity of data collection.

[0081] S104, parsing the message queue and storing the basic data in layers.

[0082] After completing the above step S103, in this step, the message queue is parsed and the basic data is stored in layers.

[0083] Specifically, in this step, the basic data collected is stored in layers, wherein the layered storage includes an operation data layer to store raw data, a data detail layer, a data service layer, and an application data layer. The raw data mainly includes collection control data and monitoring data; the data detail layer performs desensitization processing on the data, blurs and encrypts important information; the data service layer performs dimension processing on the data, and generates daily and weekly data; the application data layer performs data analysis on the collected data to provide business support for the report.

[0084] In a specific embodiment of the present disclosure, during the information interaction between the client and the server, the data acquisition server pushes messages to the message queue, and the acquisition client consumes the messages in the specified topic, parses the commands, and controls whether the acquisition program continues to collect data, thereby achieving remote control of the server; effective monitoring of the client by the server is realized, and the management of the data acquisition end is more convenient, which is conducive to timely understanding of the situation of the data acquisition end. At the same time, different control commands are isolated through the message queue, which solves the problem of no monitoring of the data acquisition terminal.

[0085] Specifically, after the client is started, it generates heartbeat and progress messages and pushes the ciphertext to the specified Topic after asymmetric encryption by the client. The client's work is basically completed.

[0086] When the server is initialized, it obtains historical data through the data persisted in the database, creates cache data by obtaining historical data, and then registers the service that listens to the message queue. The server starts a separate site to check the validity of the heartbeat data, and compares the data stored in the memory with the data in the redis cache to determine whether each data collection is online. If it is online, continue; if it is not online, update the status in redis and write its corresponding information to the persistent database.

[0087] The data collection and analysis method of the comprehensive mining face provided by the embodiment of the present disclosure includes at least two comprehensive mining equipment, and the collection and analysis method includes the following steps: obtaining instruction information; collecting basic data of each working face based on the instruction information, wherein the basic data includes control data and monitoring data; sending the basic data to the Kafka message queue; parsing the message queue, and storing the basic data in layers. By collecting and distributing the data separately, unified data collection and storage of different data sources are realized, and by setting the message queue, concurrent processing and the control process and related control commands of the client are realized.

[0088] Based on the same inventive concept, the second embodiment of the present disclosure provides a data acquisition and analysis device for a fully mechanized mining face, which can be integrated on an electronic device. The fully mechanized mining face includes at least two fully mechanized mining devices, such as Figure 4 As shown, the collection and analysis device 200 includes: an acquisition module 210, a collection module 220, a sending module 230 and a storage module 240, which are specifically as follows:

[0089] An acquisition module 210, wherein the acquisition module 210 is used to acquire instruction information;

[0090] A collection module 220, wherein the collection module 220 collects basic data of each fully mechanized mining equipment based on the instruction information, wherein the basic data includes control data and monitoring data;

[0091] A sending module 230, wherein the sending module 230 is used to send the basic data to a Kafka message queue;

[0092] The storage module 240 is used to parse the message queue and store the basic data in a hierarchical manner.

[0093] In some embodiments, the collection and analysis device 200 further includes an encryption module, and the encryption module is used to encrypt the basic data.

[0094] In some embodiments, the storage module 240 includes a parsing unit and a storage unit, wherein the parsing unit is used to parse real-time data and historical data respectively; the storage unit is used to send real-time data to a buffer database and send historical data to a memory database.

[0095] In some embodiments, the acquisition module 210 includes a receiving unit and a classification unit, the receiving unit is used to receive instruction information; the classification unit parses the instruction information to obtain the category of the instruction, wherein the category includes configuration commands, restart commands and start-stop commands; based on the category of the instruction, generates execution information or feedback information.

[0096] In some embodiments, the classification unit is also used to determine whether the instruction is a configuration command. When the instruction is a configuration command, operate the configuration file and generate an execution result message; when the instruction is not a configuration command, determine whether the instruction is a control command. When the instruction is not a control command, generate and return an error message.

[0097] In some embodiments, the classification unit is also used to determine whether the instruction is a restart command when the instruction is a control command. When the instruction is a restart command, determine whether to process the acquisition state. If it is in the acquisition state, control the acquisition program to restart and generate an execution result message; when the instruction is a start-stop command, determine whether the instruction action is consistent with the acquisition state. If not, execute the start-stop command. If consistent, generate an error message.

[0098] The data collection and analysis device for the comprehensive mining face provided by the embodiment of the present disclosure includes at least two comprehensive mining equipment, and the collection and analysis method includes the following steps: obtaining instruction information; collecting basic data of each working face based on the instruction information, wherein the basic data includes control data and monitoring data; sending the basic data to the Kafka message queue; parsing the message queue, and storing the basic data in layers. By collecting and distributing the data separately, unified data collection and storage of different data sources are realized, and by setting the message queue, concurrent processing and the control process and related control commands of the client are realized.

[0099] The third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the first embodiment of the present disclosure is implemented, including the following steps S11 to S14:

[0100] S11, obtaining instruction information;

[0101] S12, collecting basic data of each fully mechanized mining equipment based on the instruction information, wherein the basic data includes control data and monitoring data;

[0102] S13, sending the basic data to the Kafka message queue;

[0103] S14, parsing the message queue and storing the basic data in layers.

[0104] Furthermore, when the computer program is executed by a processor, other methods provided in the second embodiment of the present disclosure are implemented.

[0105] The data collection and analysis method for the comprehensive mining face provided by the embodiment of the present disclosure is applied. The comprehensive mining face includes at least two comprehensive mining equipment. The collection and analysis method includes the following steps: obtaining instruction information; collecting basic data of each working face based on the instruction information, wherein the basic data includes control data and monitoring data; sending the basic data to the Kafka message queue; parsing the message queue, and storing the basic data in layers. By collecting and distributing the data separately, unified data collection and storage of different data sources are realized, and concurrent processing, client control flow and related control commands are realized by setting the message queue.

[0106] A fourth embodiment of the present disclosure provides an electronic device, such as Figure 5 As shown, the electronic device at least includes a memory and a processor, the memory stores a computer program, and the processor implements the method provided by any embodiment of the present disclosure when executing the computer program on the memory. Exemplarily, the steps of the electronic device computer program are as follows S21 to S24:

[0107] S21, obtaining instruction information;

[0108] S22, collecting basic data of each fully mechanized mining equipment based on the instruction information, wherein the basic data includes control data and monitoring data;

[0109] S23, sending the basic data to the Kafka message queue;

[0110] S24, parsing the message queue and storing the basic data in layers.

[0111] In specific implementation, the acquisition module 210, the collection module 220, the sending module 230 and the storage module 240 are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0112] Furthermore, the processor also executes the computer program in the fourth embodiment described above.

[0113] The disclosed embodiment can make a comprehensive evaluation based on multiple key indicators of a product, and use the comprehensive results to predict product abnormalities, making the judgment on product abnormalities of a product line more accurate.

[0114] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0115] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0116] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0117] Computer program code for performing operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a stand-alone software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including, for example, a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any storage medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0119] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0121] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0122] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0124] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0125] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0126] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A data collection and analysis method for a fully mechanized mining face, wherein the fully mechanized mining face comprises at least two fully mechanized mining devices, characterized in that: The collection and analysis comprises the following steps: Get instruction information; Based on the instruction information, basic data of each comprehensive mining equipment is collected, wherein the basic data includes control data and monitoring data; Send the basic data to the Kafka message queue; The message queue is parsed, and the basic data is stored in layers.

2. The data collection and analysis method for a fully mechanized mining face according to claim 1 is characterized in that: Before sending the basic data to the Kafka message queue, the method further includes: The basic data is encrypted.

3. The data collection and analysis method for a fully mechanized mining face according to claim 1 is characterized in that: Parsing the message queue and storing the basic data in layers includes: Analyze real-time data and historical data separately; Send real-time data to a buffered database and historical data to an in-memory database.

4. The data collection and analysis method for a fully mechanized mining face according to claim 1 is characterized in that: Get the command information, including: Receive instruction information; Parsing the instruction information to obtain the category of the instruction, wherein the category includes configuration command, restart command and start / stop command; Based on the type of the instruction, execution information or feedback information is generated.

5. The data collection and analysis method for fully mechanized mining face according to claim 4 is characterized in that: The generating execution information or feedback information based on the type of the instruction includes: Determine whether the instruction is a configuration command, and when the instruction is a configuration command, operate the configuration file and generate an execution result message; When the instruction does not belong to a configuration command, it is determined whether the instruction is a control command; when the instruction does not belong to a control command, an error message is generated and returned.

6. The data collection and analysis method for a fully mechanized mining face according to claim 5 is characterized in that: When the instruction is a control command, determine whether the instruction is a restart command. When the instruction is a restart command, determine whether to process the acquisition state. If it is in the acquisition state, control the acquisition program to restart and generate an execution result message. When the instruction is a start-stop command, it is determined whether the instruction action is consistent with the acquisition state. If not, the start-stop command is executed. If consistent, an error message is generated.

7. The data collection and analysis method for fully mechanized mining working face according to claim 1 is characterized in that: The hierarchical storage includes desensitizing the data, blurring and encrypting important information while storing the data; and generating data in a unified format by dimensional processing of the data.

8. A data collection and analysis device for a fully mechanized mining face, wherein the fully mechanized mining face comprises at least two fully mechanized mining devices, characterized in that: The analysis device comprises: An acquisition module, wherein the acquisition module is used to acquire instruction information; A collection module, wherein the collection module collects basic data of each fully mechanized mining equipment based on the instruction information, wherein the basic data includes control data and monitoring data; A sending module, wherein the sending module is used to send the basic data to a Kafka message queue; A storage module is used to parse the message queue and store the basic data in a hierarchical manner.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data collection and analysis method for a fully mechanized mining face as described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the data collection and analysis method for the fully mechanized mining face according to any one of claims 1 to 7 is implemented.

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