Coal preparation industry data modeling system and method, electronic equipment and medium

Abstract

The invention provides a coal preparation industry data modeling system and method, electronic equipment and a medium, and the system comprises a data model building module which is used for classifying and defining data objects of a coal preparation industry according to a plurality of preset dimensions, and obtaining a coal preparation industry data model; wherein the coal preparation industry data model comprises a plurality of sub-modules, and each sub-module corresponds to a category of data objects; and the model configuration module is used for receiving a data object of a target coal preparation scene input by a user, acquiring a corresponding sub-module from the coal preparation industry data model based on the data object of the target coal preparation scene, and constructing a data model of the target coal preparation scene. According to the method, the data modeling speed is increased, and the time cost of repeated data modeling and maintenance is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data modeling, and in particular to a data modeling system, method, electronic equipment and medium for the coal preparation industry. Background Art

[0002] Among the many intelligent products in the coal preparation industry, there is a digitization process for various data indicators such as equipment, systems, and production control. However, in this process, the same data indicator is often repeatedly defined and named differently, which leads to the problem of difficulty in unifying the data. Furthermore, it may interfere with and hinder the digitalization, standardization, and normalization of the product requirements of the upper-level business system, making the business system significantly differentiated, and even causing ambiguity, conceptual differences, and difficulties in business alignment. Therefore, this problem can be improved through data modeling and other methods. However, the existing data modeling in the coal preparation industry often relies on manual operations and lacks standardized and automated processes, resulting in a cumbersome and repetitive modeling process and an inability to quickly respond to changes in the production environment. Summary of the invention

[0003] In view of this, the object of the present invention is to provide a data modeling system, method, electronic device and medium for the coal preparation industry, so as to improve the speed of data modeling and reduce the time cost of repeated data modeling and maintenance.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is as follows: In the first aspect, the present invention provides a data modeling system for the coal preparation industry, including: a data model construction module, used to classify and define data objects of the coal preparation industry according to multiple preset dimensions to obtain a data model of the coal preparation industry; wherein the coal preparation industry data model includes multiple sub-modules, and each sub-module corresponds to a category of data objects; a model configuration module, used to receive data objects of a target coal preparation scenario input by a user, and based on the data objects of the target coal preparation scenario, obtain corresponding sub-modules from the coal preparation industry data model to construct a data model of the target coal preparation scenario.

[0005] Optionally, the model configuration module is specifically used to: determine the category to which the data object of the target coal preparation scenario belongs, and obtain the sub-module corresponding to the category from the coal preparation industry data model to obtain the data model of the target coal preparation scenario; if there is no sub-module of the category to which the data object of the target coal preparation scenario belongs in the coal preparation industry data model, the data object is classified and defined.

[0006] Optionally, the model configuration module is also used to receive custom configuration information of a data object of a target coal preparation scenario input by a user, and to construct a data model of the target coal preparation scenario based on the custom configuration information.

[0007] Optionally, it also includes: a data acquisition module, which is used to collect data indicator values ​​corresponding to data objects from an external system through a pre-set data interface based on the data model of the target coal preparation scenario.

[0008] Optionally, it also includes: a data processing module, which is used to pre-process the data indicator values, eliminate the data indicator values ​​that do not meet the preset rules, and calculate the data indicator values ​​based on the preset processing logic.

[0009] Optionally, it also includes: a data transmission module, used to transmit the processed data indicator value to the upper-level business system, so that the upper-level business system performs business processing based on the processed data indicator value.

[0010] Optionally, it also includes: a model deployment module, which is used to deploy the data model of the target coal preparation scenario to the corresponding target coal preparation scenario using cloud technology.

[0011] In a second aspect, the present invention provides a method for data modeling of a coal preparation industry, which is applied to a data modeling system for a coal preparation industry of any one of the items provided in the first aspect, comprising: classifying and defining data objects of the coal preparation industry according to a preset plurality of dimensions through a data model construction module to obtain a data model for the coal preparation industry; wherein the data model for the coal preparation industry comprises a plurality of sub-modules, and each sub-module corresponds to a category of data objects; receiving data objects of a target coal preparation scenario input by a user through a model configuration module, and based on the data objects of the target coal preparation scenario, obtaining corresponding sub-modules from the coal preparation industry data model to construct a data model for the target coal preparation scenario.

[0012] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the method provided in the second aspect above.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method provided in the second aspect are executed.

[0014] The present invention brings the following beneficial effects: The above-mentioned coal preparation industry data modeling system, method, electronic device and medium provided by the present invention include: a data model construction module, which is used to classify and define the data objects of the coal preparation industry according to multiple preset dimensions to obtain a data model of the coal preparation industry; wherein the coal preparation industry data model includes multiple sub-modules, each sub-module corresponds to a category of data objects; a model configuration module, which is used to receive the data object of the target coal preparation scenario input by the user, and based on the data object of the target coal preparation scenario, obtain the corresponding sub-module from the coal preparation industry data model to construct the data model of the target coal preparation scenario. In the above-mentioned system, the data objects of the coal preparation industry are classified and defined in advance, and a standardized coal preparation industry data model is constructed. When data modeling is performed for the target coal preparation scenario, the sub-module corresponding to the data object can be selected from the standardized coal preparation industry data model to construct the data model of the target coal preparation scenario, thereby improving the speed of data modeling and reducing the time cost of repeated data modeling and maintenance.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the preferred embodiments are specifically mentioned below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of the structure of a data modeling system for the coal preparation industry provided by an embodiment of the present invention; Figure 2 A schematic diagram of a coal preparation industry data model provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of another coal preparation industry data modeling system provided by an embodiment of the present invention; Figure 4 A flow chart of a method for data modeling in the coal preparation industry provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] At present, traditional data modeling in the coal preparation industry often relies on manual operations and lacks standardized and automated processes, which makes the modeling process cumbersome and repetitive and unable to respond quickly to changes in the production environment; secondly, the quality of data in existing technologies is often affected by human factors, resulting in dirty or inconsistent data, which directly affects the accuracy of data and the reliability of decision-making; the lack of unified data standards between traditional systems will lead to difficulties in data transmission and sharing, and cannot achieve seamless cross-system integration, affecting data management efficiency; in addition, the coal preparation process in existing technologies mostly relies on manual operations to calculate coal quality ratios, which is prone to errors, increases operational complexity, and affects process control accuracy.

[0021] Based on this, the embodiments of the present invention provide a coal preparation industry data modeling system, method, electronic device and medium, which can improve the speed of data modeling and reduce the time cost of repeated data modeling and maintenance.

[0022] To facilitate understanding of this embodiment, a data modeling system for the coal preparation industry disclosed in an embodiment of the present invention is first introduced in detail. Figure 1 The structural diagram of a data modeling system for the coal preparation industry shown in FIG. 1 shows that the system mainly includes the following parts: The data model building module 101 is used to classify and define the data objects of the coal preparation industry according to multiple preset dimensions to obtain a data model of the coal preparation industry; wherein the data model of the coal preparation industry includes multiple sub-modules, each sub-module corresponds to a category of data objects.

[0023] In the specific implementation, see Figure 2 As shown in the figure, according to the dimensions of spatial resources, production systems, coal equipment, coal components, coal resources and process operations, the data objects of the coal preparation industry are classified and defined, and a set of standardized data structures, namely the coal preparation industry data model, is established. Among them, the coal preparation industry data model is a deep integration architecture of physical entities and digital space, and its core components include the following aspects: 1. 3D Visualization High-precision spatial mapping: A three-dimensional model of the coal preparation plant is constructed through aerial modeling, laser scanning and other technologies to achieve visualization of plant layout and equipment distribution.

[0024] Dynamic data overlay: real-time overlay of coal quality parameters (such as particle size distribution, density range), equipment operating status (speed, pressure) and other data streams in the three-dimensional scene to form a three-dimensional mapping of spatial resources and production data.

[0025] 2. Data Interaction Form Real-time two-way communication: Relying on sensor networks and IoT protocols, real-time status synchronization between physical equipment (such as heavy medium cyclones) and digital models is achieved, supporting equipment fault warning and parameter tuning.

[0026] Multi-system collaboration: Integrate subsystems such as the sorting system and safety monitoring system through standardized data interfaces to form a full-process data closed loop (such as coal quality fluctuations automatically triggering sorting medium density adjustments).

[0027] 3. Knowledge-driven Form Industry knowledge graph: Integrates professional knowledge such as coal preparation process flow, coal chemical structure models (such as Given model and Wiser model), equipment maintenance rules, etc. to support intelligent decision-making.

[0028] Empowerment of large models: Based on proprietary large models such as "Zhi Mei Master", in-depth analysis such as coal quality characteristics prediction and process parameter optimization is achieved to form a knowledge base-driven dynamic tuning capability; among them, Zhi Mei Master integrates coal preparation and mineral processing professional knowledge maps and production data, and has the capabilities of accurate question and answer, intelligent decision-making, dynamic tuning, etc. It is the exclusive AI intelligence of the coal industry, supporting intelligent management and control of the entire process from coal quality analysis (such as particle size distribution, density range) to process parameter optimization (such as sorting medium density adjustment). The technical architecture includes: Knowledge-driven: Integrate professional knowledge bases such as coal preparation process flow, equipment maintenance rules, and coal quality chemical models (such as Given / Wiser models) to build a deep industry cognitive framework. Digital twin linkage: Through real-time data collection and simulation modeling, visual monitoring of production system status, fault prediction and dynamic optimization of parameters are achieved.

[0029] 4. Modular layered form Hierarchical modeling: Basic layer: geometric modeling and attribute labeling of physical entities (equipment, coal piles); Functional layer: algorithm encapsulation of process logic (sorting timing, quality inspection rules); Application layer: scenario-based service interfaces such as production scheduling and safety monitoring.

[0030] 5. Closed-loop optimization form Virtual-reality linkage parameter adjustment: The digital twin system is used to compare the physical equipment status with the simulation model in real time, and automatically correct parameters such as suspension density error and equipment load threshold.

[0031] Dynamic identity identification: Assign "color identity information" (such as ash content and sulfur content) to each batch of coal to achieve full-link traceability and optimization from warehousing to sorting.

[0032] Specifically, according to the mapping relationship of the physical reality of the coal preparation industry (i.e., the relationship between spatial resources, production systems, coal equipment, coal components, coal resources, and process operations), the concept of "physical model" is defined according to the dimensions of spatial resources, production systems, coal equipment, coal components, coal resources, and process operations, i.e., sub-models. For example, the sensor in the coal component is a sub-model, which includes all components belonging to the sensor category. Furthermore, the entity behavior in the physical reality is defined as "model attributes", and the model integrates and outputs the data in a unified manner, thereby closely linking the process production indicators, equipment operation, equipment linkage capabilities, etc. of the coal preparation plant, and providing more comprehensive data layer support capabilities for the upper-level business system.

[0033] In practical applications, the physical model is a digital mapping of physical entities and their relationships in the coal preparation industry. The mapping relationships of various dimensions are constructed through the following sub-models: 1. Space Resource Sub-Model Definition: Describes the layout and resource distribution of the physical space of a coal preparation plant, including plant structure, pipeline network, storage area, etc.

[0034] Key attributes: three-dimensional space coordinates, regional functional division, resource capacity limitations.

[0035] ‌Condition monitoring: space utilization, rationality of equipment layout, and dynamic scheduling capabilities.

[0036] 2. Production System Sub-Model Definition: A collaborative operation system covering the entire coal preparation process, such as sorting system, safety supervision system, and joint control system.

[0037] Key attributes: inter-system data interface standards, real-time processing capabilities, and fault response mechanisms.

[0038] Dynamic interaction: automatic adjustment of sorting density and coordinated control of multiple systems are achieved through data fusion.

[0039] 3. Coal Equipment Sub-Model Definition: Digital description of core equipment such as heavy medium cyclones and separators.

[0040] Key attributes: equipment model, processing capacity, energy consumption parameters, operating status (such as speed, pressure).

[0041] Optimization indicators: sorting efficiency, equipment load and wear warning based on centrifugal force strength.

[0042] 4. Coal Component Sub-Model ‌Definition: A refined model of the functional components inside the device, such as the suspension preparation unit and sensor module.

[0043] Key attributes: component material specifications, maintenance cycles, real-time monitoring data (such as magnetite powder concentration).

[0044] Anomaly detection: prediction of component failure probability and setting of replacement thresholds.

[0045] 5. Coal Resource Sub-Model Definition: Digital expression of the physical and chemical properties of raw coal and finished products.

[0046] Key attributes: particle size distribution (content below 0.5 mm ≤ 5%), density range (above 1 kg / m³), crushing parameters.

[0047] Dynamic adjustment: Automatically optimize separation medium density based on coal quality fluctuations.

[0048] 6. Process Operation Sub-Model Definition: Dynamic execution rules of coal preparation process, covering sorting, dehydration, quality inspection and other links.

[0049] ‌Key attributes: process parameter thresholds (such as suspension density error range), operation timing logic.

[0050] Intelligent decision-making: realize self-optimization of process parameters and emergency plans for abnormal working conditions through expert knowledge base.

[0051] Each sub-model forms a closed-loop mapping through real-time data flow. For example, spatial resource constraints affect equipment layout, changes in coal quality parameters trigger process adjustments, and equipment status data feeds back to production system optimization. This multi-dimensional mapping system realizes the deep coupling of physical entities and digital space. It is a digital coupling of physical entities (equipment, coal quality), spatial relationships (layout, pipelines), and business processes (processes, quality inspections). It realizes the full-factor intelligent management of coal preparation systems through three-dimensional visualization, data drive, and knowledge fusion.

[0052] The model configuration module 102 is used to receive the data object of the target coal preparation scenario input by the user, and based on the data object of the target coal preparation scenario, obtain the corresponding submodule from the coal preparation industry data model to construct the data model of the target coal preparation scenario.

[0053] In specific implementation, the user can perform data modeling for different types of coal and different types of coal preparation plants through the model configuration module 102. Specifically, for a target coal preparation scenario (such as an anthracite coal preparation plant), the user can input the data objects involved in the target coal preparation scenario (including space resources, production systems, coal equipment, coal components, coal resources and process operations, etc.) into the system, and the model configuration module 102 can select corresponding submodules from the pre-established coal preparation industry data model according to the category of the acquired data object to form a data model of the target coal preparation scenario.

[0054] The above-mentioned coal preparation industry data modeling system provided by the embodiment of the present invention classifies and defines the data objects of the coal preparation industry in advance, and constructs a standardized coal preparation industry data model. When performing data modeling for a target coal preparation scenario, the standardized coal preparation industry data model can be used to select sub-modules corresponding to the data objects and construct a data model for the target coal preparation scenario, thereby improving the speed of data modeling and reducing the time cost of repeated data modeling and maintenance.

[0055] In one embodiment, the model configuration module 102 is specifically used to: determine the category to which the data object of the target coal preparation scenario belongs, and obtain the sub-module corresponding to the category from the coal preparation industry data model to obtain the data model of the target coal preparation scenario; if there is no sub-module of the category to which the data object of the target coal preparation scenario belongs in the coal preparation industry data model, the data object is classified and defined.

[0056] In specific implementation, the model configuration module 102 first determines the category to which each data object belongs based on the data object of the target coal preparation scenario, and then selects the submodule corresponding to the category from the pre-established coal preparation industry data model to construct the data model of the target coal preparation scenario.

[0057] If the pre-established coal preparation industry data model does not contain a sub-module of the category to which the data object of the target coal preparation scenario belongs, that is, the data object is not modeled in the coal preparation industry data model, the data object can be reclassified and defined, its corresponding sub-module can be constructed, and the coal preparation industry data model can be updated.

[0058] In one embodiment, the data object of the target coal preparation scene may contain the unique features of the target coal preparation scene. For such data objects, the embodiment of the present invention can be customized. Based on this, the model configuration module 102 in the embodiment of the present invention is also used to receive the customized configuration information of the data object of the target coal preparation scene input by the user, and build the data model of the target coal preparation scene based on the customized configuration information.

[0059] See also Figure 3 As shown, the coal preparation industry data modeling system provided by the embodiment of the present invention further includes: a data acquisition module 103 , a data processing module 104 , a data transmission module 105 and a model deployment module 106 .

[0060] The data collection module 103 is used to collect data index values ​​corresponding to data objects from an external system through a pre-set data interface based on the data model of the target coal preparation scenario.

[0061] In specific implementation, the above-mentioned coal preparation industry data modeling system is provided with a unified data interface, through which data can be collected across systems, that is, data indicator values ​​corresponding to objects are collected from external systems, such as: alarm data, production data, process flow calculation data, equipment control data, etc., thereby improving the problems of cross-system data differences, chaotic data management and data islands in the existing technology, ensuring seamless connection of data between different systems, realizing the uniformity and integrity of overall data processing, and greatly improving work efficiency.

[0062] The data processing module 104 is used to pre-process the data indicator values, eliminate the data indicator values ​​that do not meet the preset rules, and calculate the data indicator values ​​based on the preset processing logic.

[0063] In the specific implementation, in order to avoid the problem of dirty data caused by insufficient human experience, the data processing module 104 can pre-set the data processing process according to the actual business needs, including but not limited to: the preset rules that the data indicators need to comply with (such as: maximum value, minimum value, etc.), processing logic (such as: coal quality ratio calculation scheme). Based on this, after the data indicator values ​​are collected, the collected data indicator values ​​can be pre-processed first to eliminate the data that does not comply with the preset rules (such as: greater than the set maximum value, or less than the set minimum value); then calculate according to the set calculation scheme data indicator values, so that the coal quality data can be processed automatically, the complexity and error risk of manual operation can be reduced, and the accuracy of process control can be improved.

[0064] The data transmission module 105 is used to transmit the processed data indicator value to the upper-layer business system, so that the upper-layer business system performs business processing based on the processed data indicator value.

[0065] The model deployment module 106 is used to deploy the data model of the target coal preparation scenario to the corresponding target coal preparation scenario using cloud technology.

[0066] In the specific implementation, the model deployment module 106 adopts cloud technology, which can quickly deploy the data model of the target coal preparation scenario to the site, support cloud implementation and multi-site synchronization, and can flexibly customize the data model according to needs. It has strong adaptability, is easy to operate, and simplifies the on-site implementation process.

[0067] The above-mentioned coal preparation industry data modeling system provided by the embodiment of the present invention significantly improves the speed of data modeling through standardized data modeling methods, reduces the time cost of repeated data modeling and maintenance, and improves overall work efficiency; adopts cloud technology, can be quickly deployed and implemented at any site, completes data coverage, and has strong adaptability; supports customized configuration according to different needs, ensures that it can meet the needs of changing production environments, and greatly facilitates the use of on-site operators; based on accurate coal preparation industry data model design, it effectively avoids dirty data problems caused by lack of human experience; in the process of data integration, it can continuously optimize and improve data The processing flow ensures that the system can be directly reused in different production environments, thereby improving the reliability and stability of the system; through a unified data interface and data model, it solves the problems of cross-system data differences, chaotic data management and data islands in the existing technology, ensures the seamless connection of data between different systems, realizes the uniformity and integrity of overall data processing, and greatly improves work efficiency; supports data modeling requirements for different types of coal and different types of coal preparation plants, and has good business coverage; has a high degree of standardization, can reduce the workload of repeated iterations, and provide a unified solution to meet the needs of various coal qualities and production processes, and improves the scalability and flexibility of the business.

[0068] For the coal preparation industry data modeling system provided in the above embodiment, the present invention also provides a method for coal preparation industry data modeling, see Figure 4 The flowchart of a method for coal preparation industry data modeling shown in FIG. 1 illustrates that the method mainly includes the following steps S401 to S402: Step S401: Classify and define the data objects of the coal preparation industry according to multiple preset dimensions through the data model construction module to obtain a data model of the coal preparation industry.

[0069] Among them, the coal preparation industry data model includes multiple sub-modules, each sub-module corresponds to a category of data objects.

[0070] Step S402: receiving the data object of the target coal preparation scenario input by the user through the model configuration module, and based on the data object of the target coal preparation scenario, obtaining the corresponding submodule from the coal preparation industry data model to construct the data model of the target coal preparation scenario.

[0071] The above-mentioned coal preparation industry data modeling method provided by the embodiment of the present invention greatly improves the data processing efficiency and accuracy of the coal preparation industry through standardized and automated data modeling methods. Compared with the prior art, the present invention has significant high efficiency, can significantly shorten the time of data modeling and maintenance, and improve the response speed in the production process; in terms of ease of use, the application of cloud technology enables the model to be quickly deployed and flexibly customized according to needs, ensuring that the system can run smoothly in different production environments; through precise data modeling and abstraction, the present invention improves the reusability of the model, reduces the dirty data problem caused by human factors, and ensures the continuous optimization of the data integration process; in terms of standard uniformity, through a unified data interface and data model, the problem of cross-system data differentiation and management chaos is solved, and the overall work efficiency is improved; at the same time, the wide coverage of the system enables it to adapt to the diverse needs of different types of coal and coal preparation plants, further improving the system's adaptability and business scalability.

[0072] It should be noted that the implementation principle and technical effects of the method provided in the embodiment of the present invention are the same as those of the aforementioned system embodiment. For the sake of brief description, for matters not mentioned in the method embodiment, reference may be made to the corresponding contents in the aforementioned system embodiment.

[0073] An embodiment of the present invention further provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above implementation methods.

[0074] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0075] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0076] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0077] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0078] The processor 50 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 50. The above processor 50 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0079] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0080] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0081] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A data modeling system for coal preparation industry, characterized in that: include: A data model building module is used to classify and define the data objects of the coal preparation industry according to multiple preset dimensions to obtain a data model of the coal preparation industry; wherein the data model of the coal preparation industry includes multiple submodules, each of which corresponds to a category of data objects; The model configuration module is used to receive the data object of the target coal preparation scenario input by the user, and based on the data object of the target coal preparation scenario, obtain the corresponding sub-module from the coal preparation industry data model to construct the data model of the target coal preparation scenario.

2. The system according to claim 1, characterized in that The model configuration module is specifically used to: determine the category to which the data object of the target coal preparation scenario belongs, and obtain the submodule corresponding to the category from the coal preparation industry data model to obtain the data model of the target coal preparation scenario; If the coal preparation industry data model does not include a submodule of the category to which the data object of the target coal preparation scenario belongs, the data object is classified and defined.

3. The system according to claim 1, characterized in that The model configuration module is also used to receive user-defined configuration information of the data object of the target coal preparation scenario input by a user, and to construct a data model of the target coal preparation scenario based on the user-defined configuration information.

4. The system according to claim 1, characterized in that Also includes: The data acquisition module is used to collect data indicator values ​​corresponding to the data objects from an external system through a preset data interface based on the data model of the target coal preparation scenario.

5. The system according to claim 4, characterized in that Also includes: The data processing module is used to pre-process the data indicator values, eliminate the data indicator values ​​that do not meet the preset rules, and calculate the data indicator values ​​based on the preset processing logic.

6. The system according to claim 5, characterized in that Also includes: The data transmission module is used to transmit the processed data indicator value to the upper-layer business system so that the upper-layer business system performs business processing based on the processed data indicator value.

7. The system according to claim 1, characterized in that Also includes: The model deployment module is used to deploy the data model of the target coal preparation scenario into the corresponding target coal preparation scenario by using cloud technology.

8. A method for data modeling in the coal preparation industry, characterized in that: The coal preparation industry data modeling system applied to any one of claims 1 to 7 comprises: The data objects of the coal preparation industry are classified and defined according to the preset multiple dimensions through the data model construction module to obtain a data model of the coal preparation industry; wherein the data model of the coal preparation industry includes multiple submodules, each of which corresponds to a category of data objects; The data object of the target coal preparation scenario input by the user is received through the model configuration module, and based on the data object of the target coal preparation scenario, the corresponding submodule is obtained from the coal preparation industry data model to construct the data model of the target coal preparation scenario.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the method according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are performed.

Images