CN116540647A - Intelligent collaborative production management and control system for mineral separation - Google Patents
Intelligent collaborative production management and control system for mineral separation Download PDFInfo
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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Abstract
The invention relates to an intelligent collaborative production control system for mineral separation, which comprises a mine cloud platform, a production command remote control center and an edge optimization control system, and is characterized in that: the intelligent collaborative production control system for mineral separation is in communication connection with an intelligent mine mineral separation system and comprises a factory modeling module, a plan management module, a production scheduling module, a quality management module, a process tracking module, a material consumption management module, an operation guidance management module, a production cockpit module, an index data management module, a basic management module and a system integration module. The invention has the advantages that: from the global mine mineral separation production, the whole process of the production process is penetrated, and the method has different concepts, unified standards and unified methods, so that the informatization and intelligent processes of the mine mineral separation production can be comprehensively popularized and applied.
Description
Technical Field
The invention belongs to the technical field of mining, and particularly relates to an intelligent collaborative production management and control system for mineral separation.
Background
The intelligent mine is based on the premise of mine digitization and information, and is used for actively sensing, automatically analyzing and rapidly processing mine production, occupational health and safety, technical support, logistic guarantee and the like, so that the intelligent mine is built, and finally, the construction of a safe mine, an unmanned mine, a high-efficiency mine and a clean mine is realized. The intelligent mine mineral processing system is a system which is built on the basis of digitalization, informatization, virtualization, intelligence and integration, comprehensively considers various factors such as production, management, operation, safety, benefit, environment and resources, comprehensively and efficiently carries out mineral processing management on various information resources of mines by using technologies such as computers, networks, communication, virtual simulation, automatic control and monitoring, and the like, and covers the whole process of production and operation of mine enterprises. The system reduces the labor intensity of mine production and improves the working efficiency; resolving mine risk hidden danger and enhancing a safety system; resources are reasonably developed, and resource waste and environmental pollution are reduced.
The intelligent mine mineral separation system is built, so that the digitization of safety management can be realized, information guarantee is provided for the construction of intrinsically safe mine mineral separation, the refinement of production management can be realized, and decision basis and means are provided for the construction of high-yield and high-efficiency mineral separation production.
The framework system of the traditional intelligent mine mineral separation system framework lacks of interconnectivity, the overall operation efficiency is relatively low, and no modern intelligent mine mineral separation management method matched with the framework system is available.
Research and construction work related to automation, digital ore dressing and intelligent mine ore dressing are sequentially carried out by each research institution and production enterprises in China, but most of the research institutions start from a local link and do not penetrate through the whole process, and problems of different ideas, data obstruction, different standards, different methods and the like exist in the construction of an intelligent mine ore dressing system, so that the informatization and intelligent processes of mines are seriously hindered, and related research results cannot be comprehensively popularized.
Therefore, the intelligent mine mineral separation management system and method based on the intelligent mine management and control platform, which are safe, efficient, high in management, low in cost, high in interconnectivity, low in manpower resource cost and production cost, simple in method and easy to operate, have wide application prospects.
Disclosure of Invention
The invention aims to provide an intelligent collaborative production management and control system for ore dressing, which starts from the global production of the whole ore dressing plant, runs through the whole flow of the production process, is oriented to intelligent production management and control of 'ore flow', has less humanized and unmanned production of the whole flow, and is an intelligent factory which is green, safe and efficient and integrates intelligent decision-making based on industrial big data.
The object of the present invention is thus achieved.
The invention discloses an intelligent cooperative production control system for mineral separation, which is based on an intelligent mine mineral separation system and comprises a mine cloud platform, a production command remote control center and an edge optimization control system, and is characterized in that: the intelligent collaborative production control system for mineral separation is in communication connection with an intelligent mine mineral separation system and comprises a factory modeling module, a plan management module, a production scheduling module, a quality management module, a process tracking module, a material consumption management module, an operation guidance management module, a production cockpit module, an index data management module, a basic management module and a system
A system integration module; the factory modeling module is used for laying a foundation for production plan organization, process monitoring and tracking, quality detection control, material allocation and realization of equipment operation maintenance business functions by taking a mineral separation process as a main line;
The plan management module is used for realizing the decomposition of a month plan according to a year and quarter plan issued by a company, realizing the decomposition of a week plan and a day plan according to mining ore allocation selectivity and grade information, and optimizing and scheduling the plan scheduling based on ore allocation raw ore properties and equipment inspection maintenance plans and facing the productivity consumption balance target to form equipment process preparation and station time plan and estimation; the production plan is the premise and basis of production scheduling, quality management, process tracking and job guidance management work;
the production scheduling module converts the mineral processing production plan target into mineral processing production process requirements and actual production operation tasks to be decomposed into various production operation areas on the basis of planning production,
the quality management module evaluates the production condition of the factory according to quality inspection and test and online quality inspection data and provides a reliable data source for an upper business system;
the process tracking module is used for collecting energy consumption data, production index data and quality index data of the edge computing system, realizing energy consumption tracking, production plan tracking and quality tracking, and realizing material consumption tracking and on-site worker tracking;
The material consumption management module is used for carrying out statistical analysis on the purchasing, inventory and receiving processes of a large amount of materials, production auxiliary materials, energy and other materials required by production in the production process, and the energy consumption, the material consumption, the output and the cost, and carrying out statistical analysis on the purchasing, inventory and receiving processes of the materials, the energy consumption, the material consumption, the output and the cost according to the relation between the material consumption, the production index, the processing amount and the running condition of equipment;
the operation guidance management module is used for electronizing operation guidance books of the factory, uniformly managing and centrally controlling the operation guidance books of each process of the factory, and realizing accurate issuing to an operation terminal where a worker is located;
the production cockpit module is used as a comprehensive data visualization system of an intelligent factory, and covers the visual display of statistical management data and execution process data of factory selection production, process, quality, energy and equipment inspection and maintenance business;
the index data management module is oriented to the ore dressing whole production flow, a multi-dimensional index system oriented to productivity, equipment, cost, quality and personnel is established, correlation and gradual decomposition among indexes are formed, a method for calculating and counting various indexes and a direct data source are defined, calculation, aggregation and statistics of indexes at each level are realized, index correlation analysis and data mining are realized, and support is provided for continuous improvement of production management;
The basic management module supports basic service functions of system operation, forms personalized function requirements aiming at an intelligent collaborative production management and control system based on overall basic services provided by a cloud platform, and realizes flexible configuration of user role management, resource authority management, message management and log management;
the system integration module is used for transmitting instructions of a business plan to a production site, and timely collecting, uploading and processing information of the production site.
The factory modeling module comprises product material modeling, tissue structure modeling and production process modeling;
the product material modeling is carried out, a whole flow product, intermediate product and related material definition and association from the input of ore preparation raw ore to the production of finished product tailings are established, and an ore product flow model, a production equipment spare part consumable model and an energy consumption model are formed;
the organization structure modeling is carried out, a model is built around key manufacturing resources and capabilities of a factory, the equipment layer is embodied as a hierarchy relation of a production operation area, a production area, an equipment unit, a single machine, equipment and key parts, and the personnel layer is embodied as a hierarchy relation of a production operation area, a post role, a production team and personnel;
The production process modeling is used for realizing the model construction of the technological process, the process control standard, the process and the finished product quality inspection standard related to the mineral separation process.
The invention relates to a plan management module, which comprises a plan scheduling management module and an index statistics module;
the plan scheduling management module is used for scheduling modeling, plan decomposition and plan adjustment; optimizing production scheduling according to the mining and ore-distributing property, the running state of equipment and the inspection and maintenance plan;
the index statistics module is used for: the method is used for operation index statistics and index analysis and evaluation; according to the planned production scheduling indexes and the actual production completion conditions, statistics, comparison and analysis of various production indexes are realized, and the execution progress, capacity fluctuation and completion quality of the production plan are evaluated.
Preferably, the production scheduling module comprises a planning scheduling management module and a job execution management module;
the plan scheduling management module is used for carrying out process decomposition on a daily scheduling plan according to process requirements of each process, determining various material input and output plans related to each process, comprehensively calculating the equipment starting quantity and corresponding equipment time according to equipment production capacity configuration conditions, equipment running states and energy consumption cost factors, optimizing and balancing the capacity efficiency and the consumption cost, adopting a surplus energy production process, taking the optimal energy consumption cost as a target, and carrying out operation scheduling optimization by taking the optimal equipment running load and the optimal comprehensive equipment time as a target in a bottleneck process;
The job execution management module issues equipment control instructions and process control parameters to the equipment control system according to the production scheduling plan of each process, and is used for issuing and controlling the equipment instructions, monitoring production and alarming abnormality, recording production process and statistically analyzing the process.
Preferably, the quality management module comprises quality process management, assay data management and quality data analysis;
the quality process management is used for local process quality analysis and global process quality analysis, acquires monitoring data of an online quality detection instrument in real time, analyzes process technical indexes of the production process, takes index control optimization as a guide, analyzes various indexes and influence factors of the production process by summarizing the online acquired data and manually inputting the data, quantifies the influence degree of various influence factors, and timely early warns about the exceeding standard and abnormal trend of the data;
the test data management is used for collecting test data, inquiring test results and analyzing the test data; the method comprises the steps of managing test data of important nodes in a production process, establishing various attribute information aiming at different sample types, and realizing real-time transmission of the test data by the attribute information established by a sampling department and the data butt joint of the test data of the test department, so that each department can master and transmit the test data more in real time and accurately;
The quality data analysis is used for checking, analyzing and managing and synthesizing quality reports, automatically collecting quality data, transmitting the quality data on line, providing data analysis based on the data, carrying out quality data correlation analysis, finding out the root cause of quality problems, and automatically outputting quality assessment results through quality data statistics.
Preferably, the process tracking module is used for production plan tracking, quality tracking, energy consumption tracking, material consumption tracking, production worker tracking and mine warehouse storage tracking;
the production plan tracking is used for comparing the production month plan with the production quarter plan according to the data information fed back by the on-site production, and knowing the production progress state in real time;
the quality tracking is used for displaying the production quality information of each working procedure operation area fed back by on-site production in real time and knowing the quality state of each working procedure in real time;
the energy consumption tracking is used for displaying the production energy consumption information of each working procedure operation area fed back by on-site production in real time and knowing the energy consumption state of each working procedure in real time;
the material consumption tracking is used for displaying the material consumption information of each working procedure operation area fed back by the on-site production in real time and knowing the material consumption state of each working procedure in real time;
The production worker tracking, the design of the worker electronic operation board, the real-time display of the position information of the workers on the application system and the real-time knowledge of the position information of each worker;
the storage tracking of the ore bin, the input of the car dumper information and the dynamic data display of the system in the storage of the ore bin according to the ore deceleration rate in the ore bin.
Preferably, the material consumption management module comprises a off-line warehouse management, a yield management, a cost analysis and a material balance management;
the line side warehouse management is used for material warehouse-in management, material warehouse-out management and material balance management; the warehouse entry, material receiving, checking and inventory accounting of the warehouse are realized, corresponding operation and inventory records are formed, and data support is provided for statistical analysis and consumption accounting;
the yield management is used for concentrate yield management, crushing yield management, grinding yield management and flotation yield management; the method comprises the steps of each working procedure output and concentrate output in each working area, so that the output statistical analysis of the whole process is realized;
the cost analysis is used for material consumption statistical analysis, yield statistical analysis and energy consumption statistical analysis; the method comprises the steps of raw ore consumption statistics, energy consumption statistics, material balance, warehouse entry and exit situation data of each process, and corresponding analysis, wherein the cost-related data are counted according to the classification of month, material category, energy medium category and the like;
The material balance management module is used for evaluating the material balance state from the metal recovery rate of the whole plant to the actual recovery rate of each process, analyzing the theoretical recovery rate and the actual recovery rate, finding out the unbalance state in time and effectively managing the material balance supply in the process.
Preferably, the job guidance management module is used for site management, guidance document management, guidance distribution management and guidance viewing;
the site management defines the positions to be displayed by the operation instruction of each working procedure operation area of the factory, and comprises adding sites, editing sites, deleting sites and inquiring sites, wherein the superior attribute of the sites is the working area, and the superior attribute of the working area is the working procedure;
the instruction document management, after each operation instruction is ready to be started, needs to go through a management business process, including the definition, uploading, auditing and publishing of the instruction;
the instruction distribution management is that the operation instruction is distributed to the sites defined by the working areas of each working procedure, one site can bind a plurality of operation instructions, and the operation instruction distribution records are recorded, so that the later inquiry is convenient;
when a field worker performs a certain working procedure operation, the content of the guide book is checked at a station terminal of the station to guide the field production operation, and the record operation guide book is checked and recorded, so that the operation difficulty and the guiding effect of each working procedure operation can be statistically evaluated at a later period.
Preferably, the index data management module is used for index system management, statistical report management and index analysis management;
the index system management establishes a multi-dimensional index system oriented to efficiency, quality, consumption and personnel, realizes the gradual decomposition of comprehensive indexes, and defines the calculation and statistical methods of index data of each level;
the statistical report management is based on the establishment of a comprehensive index system, automatically calculates index values in a statistical period according to an index data source, a calculation formula and an index aggregation statistical method to form an index data set, establishes a factory-level report library to form a multi-level classification organization of a process equipment level, an operation area level and a factory level, and dynamically updates the report library;
the index analysis management is combined with a comprehensive index system, the production process data is analyzed by utilizing a big data analysis technology, potential association relations among indexes are analyzed, the degree of mutual influence among the indexes is evaluated, and an analysis decision means is provided for tracing and continuously improving abnormal indexes.
Preferably, the basic management module is used for user role management, resource authority management, message notification management and system log management;
And the user role management is based on a cloud platform unified service architecture, platform users and role management services are called, user management in independent tenants of the factory selection is realized, and corresponding user roles are dynamically established according to set operation authorities of all functional modules of the system and actual service positions of the factory selection. The roles and the users can be mapped in a designated way, one user can belong to one or more roles, hierarchical authorization under a unified identity authentication mechanism is supported, and a role authorization mode of a user group is supported;
the resource authority management is used for managing user resources of the whole system, each resource has certain authority, and each user-oriented function in the system can be classified and arranged through system resource management to generate a resource table, and a unique identifier is allocated to each resource;
the message notification management, based on the message communication of various production events, specifically comprises: message subscription, message publishing, message time window timeliness;
and the system log management records key operation information, system update information and the like of all login personnel accessing the system, and supports the maintenance of a standard system log for all data updated by the system so as to track all behaviors of attempting to enter the application system.
Preferably, the system integration module is used for integrating with an SAP-ERP system, an edge computing system, an EAM system, a main data system, a PI system and an energy management system;
the integrated system is integrated with an SAP-ERP system, wherein the main content of the integrated system is production plan information, material purchase information and quality information, and an API interface and intermediate file conversion are adopted in the integrated mode;
the integration with the edge computing system is carried out, wherein the main content of the integration is on-site production control parameters, quality detection information and edge computing information of equipment operation states, and the integration mode is based on an edge cloud coordination mechanism and follows a company industrial interconnection platform data transmission protocol;
the integration with the EAM system is carried out, wherein the main content of the integration is equipment account information, equipment maintenance planning information and equipment technical precision information, and the integration mode comprises an API interface and intermediate file conversion;
the integration with the main data system is realized, wherein the main content of the integration is equipment account information, equipment maintenance plan information and equipment technical precision information, and the integration mode can adopt an API interface, intermediate file conversion and the like;
the integration with the PI system is carried out, the main content of the integration is information such as on-site production technical indexes, equipment running states and the like, and the integration mode comprises data copying and data aggregation;
The integrated main content of the integration with the energy management system is energy consumption data, energy early warning data and energy statistical data information, and the integration mode comprises an API interface and intermediate file conversion.
Preferably, the scheduling modeling is decomposed into a month plan and a week/day plan according to annual and quarterly plans issued by a company, and concretely relates to production plan indexes such as iron concentrate yield, raw ore processing capacity, raw ore crushing capacity, metal recovery rate, ore dressing ratio, geological/ore feeding grade, consumption cost and the like;
the plan decomposition is based on annual and quarterly plan indexes of the company, and the annual and quarterly plan to month plan and week plan decomposition is realized by combining the state of the process equipment of the factory selection, the replacement period of spare parts, the maintenance plan and the like;
the plan adjustment is performed on ore dressing process changes caused by the changes of indexes such as ore selectivity, grade and the like generated by multi-ore-spot mining ore dressing, equipment time is estimated in time through a production scheduling algorithm, and verification is performed through plan simulation, so that a plan adjustment scheme is formed;
The operation index statistics comprises operation index statistics and index analysis and evaluation;
the index decomposition evaluation calculates the plan completion progress through the comparison analysis of the production operation completion index and the plan index, evaluates the production stable state and trend, evaluates Zhou Channeng according to the daily capacity completion condition and the monthly capacity completion condition, discovers the problem and the reason influencing the plan completion, and provides decision basis for overall plan adjustment and propulsion.
Preferably, the local process quality analysis specifically includes: crushing finished product granularity mass analysis, primary grinding granularity mass analysis, primary strong magnetic grade mass analysis, secondary grinding granularity mass analysis, secondary strong magnetic grade mass analysis, mixed magnetic fine quality analysis, flotation dry ore quality analysis, flotation fine tail grade mass analysis and filter pressing concentrate pulp concentration mass analysis;
the global process quality analysis is oriented to the whole process flow of ore dressing, a global process quality control model is established, and dynamic adjustment control parameters are monitored in real time based on process indexes to form closed-loop control;
preferably, the material warehouse-in management records and receives weight and category information of various materials in each procedure, and generates corresponding material warehouse-in bill for preparing the configuration and feeding of the materials;
The material delivery management is to take materials out of the warehouse according to actual production conditions of each day, fill in a material delivery bill in the system, fill in information including names, types, quantity and weight of materials to be taken, and lead time, and trace the delivery management of the materials;
and the material balance management, the timing checking and recording of the stock information of each material, and the decision basis for production scheduling is provided by knowing the stock information.
The invention has the advantages that:
the intelligent mine beneficiation system disclosed by the invention is based on automatic and informationized construction, advances the application of front-edge technologies such as Internet of things, big data, artificial intelligence, edge calculation, virtual reality and the like in mines, builds a green, safe and efficient intelligent factory integrating digital management of resources, intelligent production management and control for 'mine flow', less humanized and unmanned production of the whole process and intelligent decision based on industrial big data, improves the construction level of green mines and intelligent mines, and improves the production quality and economic benefits.
Drawings
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the intelligent collaborative production control system for mineral separation provided by the invention is based on an intelligent mine mineral separation system, and comprises a mine cloud platform, a production command remote control center and an edge optimization control system, and is characterized in that: the intelligent collaborative production control system for mineral separation is in communication connection with an intelligent mine mineral separation system and comprises a factory modeling module, a plan management module, a production scheduling module, a quality management module, a process tracking module, a material consumption management module, an operation guidance management module, a production cockpit module, an index data management module, a basic management module and a system integration module;
the factory modeling module is used for laying a foundation for production plan organization, process monitoring and tracking, quality detection control, material allocation and realization of equipment operation maintenance business functions by taking a mineral separation process as a main line;
the factory modeling module comprises product material modeling, tissue structure modeling and production process modeling;
1.1, modeling product materials, surrounding a mineral separation process flow, establishing a whole-flow product, an intermediate product and related materials which are processed from raw ore input of ore preparation through each process link until finished product tailings are produced, and defining and associating the whole-flow product, the intermediate product and the related materials to form an ore product flow model, a production equipment spare part consumable model and an energy consumption model;
1.2, modeling an organization structure, and building a model around key manufacturing resources and capabilities of a factory, wherein an equipment layer is represented by a hierarchy relationship of 'production operation area-production area-equipment unit-single equipment-key parts', and a personnel layer is represented by a hierarchy relationship of 'production operation area-post role-production team-personnel';
1.3, is used for realizing the model construction of technological processes, process control standards, processes and finished product quality inspection standards related to the mineral separation process.
The plan management module is used for realizing the decomposition of a month plan according to a year and quarter plan issued by a company, realizing the decomposition of a week plan and a day plan according to mining ore allocation selectivity and grade information, and optimizing and scheduling according to ore allocation raw ore properties and equipment inspection maintenance plans and aiming at productivity consumption balance targets to form equipment process preparation and station time plan and estimation; the production plan is the premise and basis of production scheduling, quality management, process tracking and job guidance management work;
the plan management module comprises a plan scheduling management module and an index statistics module;
2.1, the plan scheduling management module comprises scheduling modeling, plan decomposition and plan adjustment; optimizing production scheduling according to the mining and ore-distributing property, the running state of equipment and the inspection and maintenance plan;
2.1.1, decomposing the production scheduling modeling into a month plan and a week/day plan according to annual and quaternary plans issued by a company, and particularly relating to production plan indexes such as iron concentrate yield, raw ore processing capacity, raw ore crushing capacity, metal recovery rate, ore dressing ratio, geological/ore feeding grade, consumption cost and the like, establishing a production plan index multi-objective planning model, determining the priority of each index, solving the model by adopting an intelligent algorithm, realizing minimum index adjustment and planning deviation, and achieving the aim of optimizing production scheduling;
2.1.2, realizing the decomposition of annual and quaternary plan to month plan and week plan based on annual and quaternary plan indexes of companies and combining with the states of process equipment of a factory selection, replacement period of spare parts, maintenance and inspection plan and the like;
2.1.3, aiming at ore dressing process changes caused by the changes of indexes such as ore selectivity, grade and the like generated by multi-ore-spot mining ore dressing, timely evaluating equipment time through a production scheduling algorithm, and verifying through plan simulation to form a plan adjustment scheme;
2.2, an index statistics module: comprising the following steps: operation index statistics and index analysis and evaluation;
2.2.1, based on the collection of the metering data in the production process, realizing the automatic statistics of the indexes, forming the ore dressing production statistics standing account and the technical economic index statistics, providing accurate basis for daily yield evaluation and monthly yield accounting,
2.2.2, calculating the plan completion progress through the comparative analysis of the production operation completion index and the plan index, evaluating the production stable state and trend, evaluating Zhou Channeng according to the daily capacity completion condition and the monthly capacity completion condition, finding out the problem and the reason affecting the plan completion, and providing decision basis for overall plan adjustment and propulsion.
The production scheduling module of the invention converts the mineral processing production plan target into mineral processing production process requirements and actual production operation tasks to be decomposed into various production operation areas on the basis of planning production scheduling,
the production scheduling module comprises a planning scheduling management module and a job execution management module;
the plan scheduling management module described in 3.1 is configured to decompose a daily scheduling plan according to process requirements of each process, determine various material input and output plans related to each process, calculate the number of equipment opening and corresponding time of the equipment comprehensively according to the configuration situation of production capacity of each process equipment, the running state of the equipment and energy consumption cost factors, optimize and balance productivity efficiency and consumption cost, adopt surplus energy production process, optimize operation scheduling with the energy consumption cost as a target, and bottleneck process with the equipment running load and the comprehensive time of the equipment as a target;
And 3.2, the job execution management module issues equipment control instructions and process control parameters to the equipment control system according to the production scheduling plan of each process, and is used for issuing and controlling the equipment instructions, monitoring production and alarming abnormality, recording the production process and statistically analyzing the process.
3.2.1, device instruction issuing and controlling: and according to the production scheduling plan of each process, an equipment control instruction and a process control parameter are issued to an equipment control system, so that remote centralized control of operation of equipment of each process is realized.
3.2.2, production monitoring and abnormality alarming: establishing a cross-process collaborative monitoring index system, setting abnormality judgment standards of each dimension index facing to equipment state, process quality and productivity efficiency according to the production plan and process standard requirements of each process, and setting a corresponding alarm threshold range. Based on the data collected by the control system, real-time state monitoring and fluctuation trend analysis of various indexes are formed, and alarm and early warning are carried out on index events meeting abnormal conditions.
3.2.3, recording the production process: the detailed records of daily production processes of each team are realized, wherein the detailed records comprise yield data, quality data, equipment operation data, energy consumption data, auxiliary consumable data, time, reasons, processing procedures and result data of various production process anomalies.
3.2.4, statistical analysis of the process: and forming the convergence and statistics of the whole-flow production process information of each day-each group based on the complete process record of each working procedure operation task, and realizing the evaluation and analysis of the mineral separation global production state. And forming index statistics of performance assessment of each team, and realizing comprehensive evaluation and assessment of equipment, process and personnel. The statistical analysis of various process monitoring indexes is formed, including the mechanical faults, electrical faults, automatic faults, personnel misoperation and the like of typical equipment in various processes, the influence of various abnormal conditions on production quality, yield, equipment utilization rate and the like is analyzed, and decision basis is provided for lean control and continuous improvement of the production process.
The quality management module evaluates the production condition of the factory according to quality inspection and test and online quality detection data, and provides a reliable data source for an upper service system;
the quality management module comprises quality process management, assay data management and quality data analysis;
4.1, quality process management, which is used for local process quality analysis and global process quality analysis, collecting monitoring data of an online quality detection instrument in real time, analyzing process technical indexes of the production process, taking index control optimization as a guide, analyzing various indexes and influencing factors of the production process by summarizing the online collected data and manually inputting the data, quantifying the influence degree of various influencing factors, and timely early warning on the exceeding standard and abnormal trend of the data; including local process quality analysis and global process quality analysis,
The local process mass spectrometry of 4.1.1, comprising: crushing finished product granularity mass analysis, primary grinding granularity mass analysis, primary strong magnetic grade mass analysis, secondary grinding granularity mass analysis, secondary strong magnetic grade mass analysis, mixed magnetic fine quality analysis, flotation dry ore quality analysis, flotation fine tail grade mass analysis and filter pressing concentrate pulp concentration mass analysis;
4.1.2, global process quality analysis: the method is oriented to the whole process flow of ore dressing, a global process quality control model of 'raw ore grade-concentrate grade-recovery rate' is established, dynamic adjustment control parameters are monitored in real time based on process indexes, cooperative closed-loop control of 'ore grinding-magnetic separation-flotation' is formed, the large closed-loop is fed back to the small closed-loop, the small closed-loop is fed forward to the small closed-loop, the optimal result of the small closed-loop control is realized, and the large closed-loop concentrate is stably output.
4.2, the assay data management: and (3) performing test data management on important nodes in the production process, establishing various attribute information aiming at different sample types, and realizing real-time transmission of test data by the attribute information established by the sampling department and the data butt joint of the test data of the test department, so that each department can master and transmit the test data more real-time and accurate. Comprising the following steps: collecting test data, inquiring test results and analyzing test data;
4.2.1, the assay data acquisition: the assay data acquisition can be realized in two modes, one is realized by the butt joint of the system and the instrument, and the system can automatically acquire and process the analysis test result of the instrument; the other is to export the data from the instrument assay and then import it into the system through a custom interface.
4.2.2, the assay result query: for each test result, a laboratory test report is generated, and the report content includes a laboratory sheet number, a department, a sampling date, a test item, an analysis method, and the like, in addition to the test result. Each production and administration may view the test results through the system.
4.2.3, analysis of the assay data: all raw assay data and final analysis results are stored in the system, including all data of sample registration information, assay personnel, instrumentation, detection methods, calculation formulas, etc., and detailed modification information, etc. And comparing and analyzing the test index, the qualification rate and the like of each production link.
4.3, analyzing quality data: the data is not landed through automatic acquisition and online transmission of the quality data, deviation and artificial modification in the data transmission process are prevented, and the stability of the quality data is ensured. Based on the data, a data analysis function is provided, quality management staff and production organization management staff can conduct quality data correlation analysis through a quality analysis tool, and a source of quality problems is found. And automatically outputting a quality check result through quality data statistics. Comprising the following steps: checking, analyzing and managing and synthesizing a quality report;
4.3.1, check analysis management: the system provides product quality trend chart prediction according to the test result data, and provides effective guiding suggestion for process operation and process parameters;
4.3.2, comprehensive quality report: the system provides quality reports of class, day, quarter, month and year, the report compiling data gives accurate and real quality detection and metering data, a comprehensive quality report is provided for production technology management staff, the production management staff is helped to grasp the production quality index achievement condition in time, and support is provided for analyzing production abnormality and adjusting production process control indexes.
The process tracking module is used for collecting energy consumption data, production index data and quality index data of the edge computing system, realizing energy consumption tracking, production plan tracking and quality tracking, and realizing material consumption tracking and on-site worker tracking;
the process tracking module is used for tracking production plans, quality, energy consumption, material consumption, production workers and mine warehouse storage;
5.1, tracking a production plan, comparing a production month plan with a production quarter plan according to the data information of on-site production feedback, and knowing the production progress state in real time;
5.2, carrying out quality tracking, namely displaying production quality information of each working procedure operation area fed back by on-site production in real time, and knowing the quality state of each working procedure in real time;
5.3, tracking the energy consumption, displaying the production energy consumption information of each working procedure operation area fed back by on-site production in real time, and knowing the energy consumption state of each working procedure in real time;
5.4, tracking the material consumption, displaying the material consumption information of each working procedure operation area fed back by the field production in real time, and knowing the material consumption state of each working procedure in real time;
5.5, tracking production workers, designing worker electronic operation cards, displaying the position information of the workers on an application system in real time, and knowing the position information of each worker in real time;
5.6, tracking the storage of the ore bin, inputting the car dumper information, and displaying dynamic data in the storage of the ore bin according to the ore deceleration rate in the ore bin by the system.
The material consumption management module is used for carrying out statistical analysis on the purchasing, inventory and leading processes of bulk materials, production auxiliary materials, energy and other materials required by production in the production process, and the energy consumption, the material consumption, the output and the cost, analyzing the relation between the material consumption and the production index, the processing amount and the running condition of equipment, and carrying out statistical analysis on the purchasing, inventory and leading processes of the materials and the energy consumption, the material consumption, the output and the cost;
6.1, the material consumption management module comprises a line side library management, a yield management, a cost analysis and a material balance management;
6.1.1, material warehouse-in management: and recording the received weight, category and other information of various materials in each process, and generating corresponding material warehouse-in receipts by the system so as to prepare the configuration and feeding of the materials.
6.1.2, the material ex-warehouse management: according to the actual production condition of each day, material receiving and delivering are carried out, a material delivering bill is filled in the system, filling information comprises the names, types, quantity and weight of the received materials, the receiving and delivering time of the receiving users and the receiving users, and the like, and trace is realized on the delivering management of the materials.
6.1.3, material balance management: and (5) checking and recording inventory information of each material at fixed time, and providing decision basis for production scheduling by knowing the inventory information.
6.2, the method is used for concentrate yield management, crushing yield management, grinding yield management and flotation yield management; the method comprises the steps of each working procedure output and concentrate output in each working area, so that the output statistical analysis of the whole process is realized;
6.2.1, concentrate yield management: the concentrate yield on the same day is automatically calculated by collecting parameter information such as the ore feeding weight, the circulation times and the like of a filter press in a filter pressing operation area control system, data support is provided for a related data use module, and visual statistical analysis data is provided for a manager;
6.2.2, crushing yield management: calculating the output of each group comprising coarse breakage, medium breakage and fine breakage by collecting the data of each process section belt scale and combining a manual input mode, and providing data support for calculating the time of a crusher and the unit consumption of electricity;
6.2.3, grinding magnetic yield management: the method comprises the steps of collecting and calculating the output of each team of each process section, including ball milling ore grinding amount and mixed magnetic refined output through a magnetic grinding output management module;
6.2.4, flotation yield management: the actual concentrate yield to the large well of concentrate is calculated from flow data of froth and underflow concentrates in the flotation control system.
The cost analysis is used for material consumption statistical analysis, yield statistical analysis and energy consumption statistical analysis; the method comprises the steps of raw ore consumption statistics, energy consumption statistics, material balance, warehouse entry and exit situation data of each process, and corresponding analysis, wherein the cost-related data are counted according to the classification of month, material category, energy medium category and the like;
6.3.1, statistical analysis of material consumption: the system counts the material consumption conditions of all factories and groups of each process by collecting data of instruments such as belt scales, flow meters and the like or docking DCS systems according to cycles such as days, months, quarters, years and the like, comprises raw ore quantity, ore feeding quantity of each process, steel ball consumption, medicament consumption and the like, and calculates each material unit consumption by combining actual yield.
6.3.2, yield statistical analysis: and counting the yield conditions of each working procedure in each working area, and carrying out comparison analysis by combining the raw ore quantity and the consumption.
6.3.3, energy consumption statistical analysis: the energy cost is analyzed and calculated through statistics of various energy consumption such as water, electricity and steam, classified inquiry of the energy expenditure situation is provided, and decision support is provided for enterprise energy consumption estimation, energy use cost estimation and the like through inquiry of the energy cost under the conditions of energy type, time, operation area and the like in a specific period.
And 6.4, material balance management: the material balance state is evaluated from the metal recovery rate of the whole plant to the actual recovery rate of each process, and the theoretical recovery rate and the actual recovery rate are analyzed, so that a production manager can find out an unbalanced state in time, the balanced material supply in the process is effectively managed, the production is stabilized, and the yield is improved.
The operation guidance management module is used for electronizing the operation guidance book of the factory, uniformly managing and centrally controlling the operation guidance book of each procedure of the factory, and realizing accurate issuing to an operation terminal where a worker is located;
the job guidance management module is used for site management, guidance book file management, guidance book distribution management and guidance book viewing;
7.1, site management, defining the positions to be displayed by the operation instruction of each process operating area of the factory, wherein the positions comprise adding sites, editing sites, deleting sites and inquiring sites, the superior attribute of the sites is an operating area, and the superior attribute of the operating area is a process;
7.2, managing the guide book file, wherein each operation guide book needs to pass through a management business process after being ready to be started, and the management business process comprises the definition, uploading, auditing and publishing of the guide book;
7.3, distributing the operation instruction to sites defined by working areas of each working procedure, binding a plurality of operation instructions by one site, and recording the distribution record of the operation instruction, thereby facilitating later inquiry;
and 7.4, checking the instruction book, namely checking the content of the instruction book at a station terminal of a site to guide the on-site production operation when a worker performs certain working procedure operation, and recording the checking record of the operation instruction book, so that the operation difficulty and the guiding effect of each working procedure operation can be conveniently and statistically evaluated in the later period.
The production cockpit module provided by the invention is used as a comprehensive data visualization system of an intelligent factory, and covers the visual display of statistical management data and execution process data of factory selection production, process, quality, energy and equipment inspection and maintenance business;
The index data management module of the invention is oriented to the ore dressing whole production flow, establishes a multi-dimensional index system oriented to productivity, equipment, cost, quality and personnel, forms correlation and gradual decomposition among indexes, defines a method for calculating and counting various indexes and a direct data source, realizes calculation, convergence and statistics of indexes at all levels, realizes index correlation analysis and data mining, and provides support for continuous improvement of production management;
the process indexes and control parameters of the factory production process are numerous, the association relationship between the process indexes and the control parameters is complex, the interaction mechanism of the indexes is unclear, and a model needs to be built for analysis. The relevance of the process indexes refers to the mutual influence relationship and influence degree of the process indexes, and the relevance of the process indexes is analyzed to analyze the influence relationship and the influence strength. The main index association analysis methods comprise causal relation matrix (C & E matrix), failure mode and result analysis (FEMA), regression analysis, orthogonal analysis and the like.
The main analysis process includes:
counting the distribution condition of the index, determining the index value and the confidence interval,
a 5M1E method is adopted to determine the potential influence factor range of the index,
combining the index potential influencing factors, determining key influencing factors by utilizing a causal relation matrix, a failure mode and result analysis,
On the basis of key factors, analysis of significance level of index influence is performed by using analysis of variance, regression and orthogonal analysis.
The index data management module is used for index system management, statistical report management and index analysis management;
8.1, establishing a multi-dimensional index system oriented to efficiency, quality, consumption and personnel, realizing step-by-step decomposition of comprehensive indexes, and defining calculation and statistics methods of index data of each level;
8.2, on the basis of the establishment of a comprehensive index system, automatically calculating index values in a statistical period according to an index data source, a calculation formula and an index aggregation statistical method to form an index data set, establishing a factory-level report library to form a multi-level classification organization of a process equipment level, an operation area level and a factory level, and dynamically updating the report library;
and 8.3, analyzing production process data by utilizing a big data analysis technology in combination with a comprehensive index system, analyzing potential association relations among indexes, evaluating the degree of mutual influence among indexes, and providing an analysis decision means for tracing and continuously improving abnormal indexes.
The basic management module supports basic service functions of system operation, forms personalized function requirements aiming at an intelligent collaborative production management and control system based on the whole basic service provided by a cloud platform, and realizes flexible configuration of user role management, resource authority management, message management and log management;
9.1, based on a cloud platform unified service architecture, calling platform users and role management services, realizing user management in independent tenants of a factory selection, and dynamically establishing corresponding user roles according to set operation authorities of all functional modules of the system and actual service positions of the factory selection. The roles and the users can be mapped in a designated way, one user can belong to one or more roles, hierarchical authorization under a unified identity authentication mechanism is supported, and a role authorization mode of a user group is supported;
9.2, the resource authority management is used for managing the user resources of the whole system, each resource has a certain authority, and each user-oriented function in the system can be classified and arranged through the system resource management to generate a resource table, and a unique identifier is allocated to each resource;
on the basis, binding of resource rights is performed based on roles. The resource specified by the grant also includes all sub-resources under the resource unless its sub-resources have a separate grant definition to cover it. The authorization mode supports permission authorization and refusal authorization, and the permission authorization allows authorization of a certain authority of a designated role, namely, the permission authorization sets a behavior of a certain role on a certain resource; the refusal of the authority refuses the authority of a specific character, namely, the authority refuses the action of the character on a resource.
The message notification management of 9.3, message communication based on various production events, specifically comprising: message subscription, message publishing, message time window timeliness;
9.3.1, message subscription: according to different user roles, message subscription based on different topics of production notification, abnormality, quality and the like can be realized;
9.3.2, message publishing: the message bus classifies messages to be published according to topics, and publishes the messages based on a message subscription list, wherein the publishing modes comprise WeChat, short message, mail, mobile terminal APP, PC client, large screen billboard and the like;
9.3.3, message time window timeliness: starting timing after the message bus receives the message, enabling the message to enter a time window, and carrying out processing such as priority improvement, persistence storage and the like on the message exceeding timeliness through timing scanning;
9.4, recording key operation information of all login personnel accessing the system, system update information and the like, and supporting to keep a standard system log for all data of system update and tracking all actions attempting to enter the application system.
For all data updated by the system, a standard system log is kept, information such as who changes the data from what to what, date and time of data change and the like is recorded, and all actions of attempting to enter the application system are tracked, whether login is successful or failed. The system log has an archiving mechanism, and can access the archiving record of the log when needed, and the archiving log is convenient for inquiring and generating a report.
The system log inquiry according to various conditions such as personnel, functional operation, time and the like is provided, and classification, screening and maximum efficiency storage of different log formats are supported. The log supports log management functions such as automatic export, import, deletion, backup, recovery, etc. The method and the system provide various and flexible log information inquiry, simultaneously support related inquiry of different logs according to the conditions set by the user, and help an administrator to realize more comprehensive and deep analysis of the events.
The system integration module of the invention is a system facing to a production site, an information transmission system connecting an operation layer and a site control layer, an upper business system and a bottom edge computing system together form a nervous system of an enterprise, an instruction of a business plan is transmitted to the production site, and information of the production site is timely collected, uploaded and processed.
The system integration module is used for integrating with an SAP-ERP system, an edge computing system, an EAM system, a main data system, a PI system and an energy management system;
10.1, integrating the SAP-ERP system, wherein the main content of the integration comprises production planning information, material purchasing information and quality information, and the integration mode adopts an API interface and intermediate file conversion;
10.2, integrating with an edge computing system, wherein the main content of the integration comprises on-site production control parameters, quality detection information and edge computing information of equipment operation states, and the integration mode is based on an edge cloud cooperation mechanism and conforms to a company industrial interconnection platform data transmission protocol;
10.3, integrating with an EAM system, wherein the integrated main content comprises equipment ledger information, equipment maintenance plan information and equipment technical precision information, and the integration mode comprises an API interface and intermediate file conversion;
10.4, integrating with a main data system, wherein the main content of the integration comprises equipment account information, equipment maintenance plan information and equipment technical precision information, and the integration mode can adopt an API interface, intermediate file conversion and the like;
10.5, integrating the PI system, wherein the main content of the integration comprises information such as field production technical indexes, equipment running states and the like, and the integration mode comprises data copying and data aggregation;
the integration with an energy management system described in 1.6, wherein the integrated main content includes energy consumption data, energy early warning data and energy statistics data information, and the integration mode includes API interface and intermediate file conversion.
The traditional management and control mode of the traditional mining industry is improved through the digital and intelligent information technology, intelligent personal management, intelligent decision support and intelligent business cooperation are promoted, the novel intelligent mineral processing factory with comprehensive information acquisition, high intelligent management and control, safety, reliability, economy, high efficiency, green environmental protection and sustainable development is realized, and the integral construction of intelligent mine of saddle steel mining industry is further promoted.
The intelligent collaborative production management and control system for mineral processing is based on an end-to-end network cloud architecture of an industrial Internet platform, and based on automation and informatization construction, the application of front edge technologies such as Internet of things, big data, artificial intelligence, edge calculation and virtual reality in mines is promoted, the purposes of digital management of resources, intelligent production management and control for 'mine flow', less humanization of the whole process, unmanned production and intelligent decision based on industrial big data are built, and the intelligent factory is green, safe and efficient, the transformation and upgrading and high-quality development of enterprises are promoted, the construction level of green mines and intelligent mines is improved, and the production quality and economic benefit are improved.
Claims (14)
1. Ore dressing intelligence is production management and control system in coordination, based on wisdom mine ore dressing system, including mine cloud platform, production command remote control center and edge optimization control system, its characterized in that: the intelligent collaborative production control system for mineral separation is in communication connection with an intelligent mine mineral separation system and comprises a factory modeling module, a plan management module, a production scheduling module, a quality management module, a process tracking module, a material consumption management module, an operation guidance management module, a production cockpit module, an index data management module, a basic management module and a system integration module;
The factory modeling module is used for laying a foundation for production plan organization, process monitoring and tracking, quality detection control, material allocation and realization of equipment operation maintenance business functions by taking a mineral separation process as a main line;
the plan management module is used for realizing the decomposition of a month plan according to a year and quarter plan issued by a company, realizing the decomposition of a week plan and a day plan according to mining ore allocation selectivity and grade information, and optimizing and scheduling the plan scheduling based on ore allocation raw ore properties and equipment inspection maintenance plans and facing the productivity consumption balance target to form equipment process preparation and station time plan and estimation; the production plan is the premise and basis of production scheduling, quality management, process tracking and job guidance management work;
the production scheduling module converts the mineral processing production plan target into mineral processing production process requirements and actual production operation tasks to be decomposed into various production operation areas on the basis of planning production,
the quality management module evaluates the production condition of the factory according to quality inspection and test and online quality inspection data and provides a reliable data source for an upper business system;
the process tracking module is used for collecting energy consumption data, production index data and quality index data of the edge computing system, realizing energy consumption tracking, production plan tracking and quality tracking, and realizing material consumption tracking and on-site worker tracking;
The material consumption management module is used for carrying out statistical analysis on the purchasing, inventory and receiving processes of a large amount of materials, production auxiliary materials, energy and other materials required by production in the production process, and the energy consumption, the material consumption, the output and the cost, and carrying out statistical analysis on the purchasing, inventory and receiving processes of the materials, the energy consumption, the material consumption, the output and the cost according to the relation between the material consumption, the production index, the processing amount and the running condition of equipment;
the operation guidance management module is used for electronizing operation guidance books of the factory, uniformly managing and centrally controlling the operation guidance books of each process of the factory, and realizing accurate issuing to an operation terminal where a worker is located;
the production cockpit module is used as a comprehensive data visualization system of an intelligent factory, and covers the visual display of statistical management data and execution process data of factory selection production, process, quality, energy and equipment inspection and maintenance business;
the index data management module is oriented to the ore dressing whole production flow, a multi-dimensional index system oriented to productivity, equipment, cost, quality and personnel is established, correlation and gradual decomposition among indexes are formed, a method for calculating and counting various indexes and a direct data source are defined, calculation, aggregation and statistics of indexes at each level are realized, index correlation analysis and data mining are realized, and support is provided for continuous improvement of production management;
The basic management module supports basic service functions of system operation, forms personalized function requirements aiming at an intelligent collaborative production management and control system based on overall basic services provided by a cloud platform, and realizes flexible configuration of user role management, resource authority management, message management and log management;
the system integration module is used for transmitting instructions of a business plan to a production site, and timely collecting, uploading and processing information of the production site.
2. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the factory modeling module comprises product material modeling, tissue structure modeling and production process modeling;
the product material modeling is carried out, a whole flow product, intermediate product and related material definition and association from the input of ore preparation raw ore to the production of finished product tailings are established, and an ore product flow model, a production equipment spare part consumable model and an energy consumption model are formed;
The organization structure modeling is carried out, a model is built around key manufacturing resources and capabilities of a factory, the equipment layer is embodied as a hierarchy relation of a production operation area, a production area, an equipment unit, a single machine, equipment and key parts, and the personnel layer is embodied as a hierarchy relation of a production operation area, a post role, a production team and personnel;
the production process modeling is used for realizing the model construction of the technological process, the process control standard, the process and the finished product quality inspection standard related to the mineral separation process.
3. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the plan management module comprises a plan scheduling management module and an index statistics module;
the plan scheduling management module comprises scheduling modeling, plan decomposition and plan adjustment; optimizing production scheduling according to the mining and ore-distributing property, the running state of equipment and the inspection and maintenance plan;
the index statistics module is used for: the method is used for operation index statistics and index analysis and evaluation; according to the planned production scheduling indexes and the actual production completion conditions, statistics, comparison and analysis of various production indexes are realized, and the execution progress, capacity fluctuation and completion quality of the production plan are evaluated.
4. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the production scheduling module comprises a planning scheduling management module and a job execution management module;
the plan scheduling management module is used for carrying out process decomposition on a daily scheduling plan according to process requirements of each process, determining various material input and output plans related to each process, comprehensively calculating the equipment starting quantity and corresponding equipment time according to equipment production capacity configuration conditions, equipment running states and energy consumption cost factors, optimizing and balancing the capacity efficiency and the consumption cost, adopting a surplus energy production process, taking the optimal energy consumption cost as a target, and carrying out operation scheduling optimization by taking the optimal equipment running load and the optimal comprehensive equipment time as a target in a bottleneck process;
the job execution management module issues equipment control instructions and process control parameters to the equipment control system according to the production scheduling plan of each process, and is used for issuing and controlling the equipment instructions, monitoring production and alarming abnormality, recording production process and statistically analyzing the process.
5. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the quality management module comprises quality process management, assay data management and quality data analysis;
The quality process management is used for local process quality analysis and global process quality analysis, acquires monitoring data of an online quality detection instrument in real time, analyzes process technical indexes of the production process, takes index control optimization as a guide, analyzes various indexes and influence factors of the production process by summarizing the online acquired data and manually inputting the data, quantifies the influence degree of various influence factors, and timely early warns about the exceeding standard and abnormal trend of the data;
the test data management is used for collecting test data, inquiring test results and analyzing the test data; the method comprises the steps of managing test data of important nodes in a production process, establishing various attribute information aiming at different sample types, and realizing real-time transmission of the test data by the attribute information established by a sampling department and the data butt joint of the test data of the test department, so that each department can master and transmit the test data more in real time and accurately;
the quality data analysis is used for checking, analyzing and managing and synthesizing quality reports, automatically collecting quality data, transmitting the quality data on line, providing data analysis based on the data, carrying out quality data correlation analysis, finding out the root cause of quality problems, and automatically outputting quality assessment results through quality data statistics.
6. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the process tracking module is used for tracking production plans, quality, energy consumption, material consumption, production workers and mine warehouse storage;
the production plan tracking is used for comparing the production month plan with the production quarter plan according to the data information fed back by the on-site production, and knowing the production progress state in real time;
the quality tracking is used for displaying the production quality information of each working procedure operation area fed back by on-site production in real time and knowing the quality state of each working procedure in real time;
the energy consumption tracking is used for displaying the production energy consumption information of each working procedure operation area fed back by on-site production in real time and knowing the energy consumption state of each working procedure in real time;
the material consumption tracking is used for displaying the material consumption information of each working procedure operation area fed back by the on-site production in real time and knowing the material consumption state of each working procedure in real time;
the production worker tracking, the design of the worker electronic operation board, the real-time display of the position information of the workers on the application system and the real-time knowledge of the position information of each worker;
the storage tracking of the ore bin, the input of the car dumper information and the dynamic data display of the system in the storage of the ore bin according to the ore deceleration rate in the ore bin.
7. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the material consumption management module comprises a line side library management, a yield management, a cost analysis and a material balance management;
the line side warehouse management is used for material warehouse-in management, material warehouse-out management and material balance management; the warehouse entry, material receiving, checking and inventory accounting of the warehouse are realized, corresponding operation and inventory records are formed, and data support is provided for statistical analysis and consumption accounting;
the yield management is used for concentrate yield management, crushing yield management, grinding yield management and flotation yield management; the method comprises the steps of each working procedure output and concentrate output in each working area, so that the output statistical analysis of the whole process is realized;
the cost analysis is used for material consumption statistical analysis, yield statistical analysis and energy consumption statistical analysis; the method comprises the steps of raw ore consumption statistics, energy consumption statistics, material balance, warehouse entry and exit situation data of each process, and corresponding analysis, wherein the cost-related data are counted according to the classification of month, material category, energy medium category and the like;
the material balance management is used for evaluating the material balance state from the metal recovery rate of the whole plant to the actual recovery rate of each process, analyzing the theoretical recovery rate and the actual recovery rate, finding out the unbalanced state in time and effectively managing the material balance supply in the process.
8. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the job guidance management module is used for site management, guidance book file management, guidance book distribution management and guidance book viewing;
the site management defines the positions to be displayed by the operation instruction of each working procedure operation area of the factory, and comprises adding sites, editing sites, deleting sites and inquiring sites, wherein the superior attribute of the sites is the working area, and the superior attribute of the working area is the working procedure;
the instruction document management, after each operation instruction is ready to be started, needs to go through a management business process, including the definition, uploading, auditing and publishing of the instruction;
the instruction distribution management is that the operation instruction is distributed to the sites defined by the working areas of each working procedure, one site can bind a plurality of operation instructions, and the operation instruction distribution records are recorded, so that the later inquiry is convenient;
when a field worker performs a certain working procedure operation, the content of the guide book is checked at a station terminal of the station to guide the field production operation, and the record operation guide book is checked and recorded, so that the operation difficulty and the guiding effect of each working procedure operation can be statistically evaluated at a later period.
9. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the index data management module is used for index system management, statistical report management and index analysis management;
the index system management establishes a multi-dimensional index system oriented to efficiency, quality, consumption and personnel, realizes the gradual decomposition of comprehensive indexes, and defines the calculation and statistical methods of index data of each level;
the statistical report management is based on the establishment of a comprehensive index system, automatically calculates index values in a statistical period according to an index data source, a calculation formula and an index aggregation statistical method to form an index data set, establishes a factory-level report library to form a multi-level classification organization of a process equipment level, an operation area level and a factory level, and dynamically updates the report library;
the index analysis management is combined with a comprehensive index system, the production process data is analyzed by utilizing a big data analysis technology, potential association relations among indexes are analyzed, the degree of mutual influence among the indexes is evaluated, and an analysis decision means is provided for tracing and continuously improving abnormal indexes.
10. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the basic management module is used for user role management, resource authority management, message notification management and system log management;
And the user role management is based on a cloud platform unified service architecture, platform users and role management services are called, user management in independent tenants of the factory selection is realized, and corresponding user roles are dynamically established according to set operation authorities of all functional modules of the system and actual service positions of the factory selection. The roles and the users can be mapped in a designated way, one user can belong to one or more roles, hierarchical authorization under a unified identity authentication mechanism is supported, and a role authorization mode of a user group is supported;
the resource authority management is used for managing user resources of the whole system, each resource has certain authority, and each user-oriented function in the system can be classified and arranged through system resource management to generate a resource table, and a unique identifier is allocated to each resource;
the message notification management, based on the message communication of various production events, specifically comprises: message subscription, message publishing, message time window timeliness;
and the system log management records key operation information, system update information and the like of all login personnel accessing the system, and supports the maintenance of a standard system log for all data updated by the system so as to track all behaviors of attempting to enter the application system.
11. The intelligent co-production management and control system for mineral separation according to claim 1, wherein: the system integration module is used for integrating with an SAP-ERP system, an edge computing system, an EAM system, a main data system, a PI system and an energy management system;
the integrated system is integrated with an SAP-ERP system, wherein the main content of the integrated system is production plan information, material purchase information and quality information, and an API interface and intermediate file conversion are adopted in the integrated mode;
the integration with the edge computing system is carried out, wherein the main content of the integration is on-site production control parameters, quality detection information and edge computing information of equipment operation states, and the integration mode is based on an edge cloud coordination mechanism and follows a company industrial interconnection platform data transmission protocol;
the integration with the EAM system is carried out, wherein the main content of the integration is equipment account information, equipment maintenance planning information and equipment technical precision information, and the integration mode comprises an API interface and intermediate file conversion;
the integration with the main data system is realized, wherein the main content of the integration is equipment account information, equipment maintenance plan information and equipment technical precision information, and the integration mode can adopt an API interface, intermediate file conversion and the like;
The integration with the PI system is carried out, the main content of the integration is information such as on-site production technical indexes, equipment running states and the like, and the integration mode comprises data copying and data aggregation;
the integrated main content of the integration with the energy management system is energy consumption data, energy early warning data and energy statistical data information, and the integration mode comprises an API interface and intermediate file conversion.
12. A mineral processing intelligent co-production control system according to claim 3, characterized in that: the scheduling modeling is decomposed into a month plan and a week/day according to the annual and quarterly plans issued by the company
Planning, namely, establishing a multi-objective planning model of production planning indexes, namely, the yield of iron ore concentrate, the processing amount of raw ore, the crushing amount of raw ore, the metal recovery rate, the ore dressing ratio, the geological/ore feeding grade, the consumption cost and the like, determining the priority of each index, solving the model by adopting an intelligent algorithm, and achieving the purposes of optimizing the production by realizing minimum adjustment and planning deviation of the indexes;
the plan decomposition is based on annual and quarterly plan indexes of the company, and the annual and quarterly plan to month plan and week plan decomposition is realized by combining the state of the process equipment of the factory selection, the replacement period of spare parts, the maintenance plan and the like;
The plan adjustment is performed on ore dressing process changes caused by the changes of indexes such as ore selectivity, grade and the like generated by multi-ore-spot mining ore dressing, equipment time is estimated in time through a production scheduling algorithm, and verification is performed through plan simulation, so that a plan adjustment scheme is formed;
the operation index statistics comprises operation index statistics and index analysis and evaluation;
the index decomposition evaluation calculates the plan completion progress through the comparison analysis of the production operation completion index and the plan index, evaluates the production stable state and trend, evaluates Zhou Channeng according to the daily capacity completion condition and the monthly capacity completion condition, discovers the problem and the reason influencing the plan completion, and provides decision basis for overall plan adjustment and propulsion.
13. The intelligent co-production control system for mineral separation according to claim 5, wherein: the local process quality analysis specifically comprises the following steps: crushing finished product granularity mass analysis, primary grinding granularity mass analysis, primary strong magnetic grade mass analysis, secondary grinding granularity mass analysis, secondary strong magnetic grade mass analysis, mixed magnetic fine quality analysis, flotation dry ore quality analysis, flotation fine tail grade mass analysis and filter pressing concentrate pulp concentration mass analysis;
The global process quality analysis is oriented to the whole process flow of ore dressing, a global process quality control model is established, and dynamic adjustment control parameters are monitored in real time based on process indexes to form closed-loop control.
14. The intelligent co-production control system for mineral separation according to claim 7, wherein: the material warehouse-in management records and receives weight and category information of various materials in each procedure, and generates corresponding material warehouse-in bill for preparing the configuration and feeding of the materials;
the material delivery management is to take materials out of the warehouse according to actual production conditions of each day, fill in a material delivery bill in the system, fill in information including names, types, quantity and weight of materials to be taken, and lead time, and trace the delivery management of the materials;
and the material balance management, the timing checking and recording of the stock information of each material, and the decision basis for production scheduling is provided by knowing the stock information.
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