CN117473347B - Ore dressing full-flow data processing method and system based on rule engine - Google Patents
Ore dressing full-flow data processing method and system based on rule engine Download PDFInfo
- Publication number
- CN117473347B CN117473347B CN202311833072.XA CN202311833072A CN117473347B CN 117473347 B CN117473347 B CN 117473347B CN 202311833072 A CN202311833072 A CN 202311833072A CN 117473347 B CN117473347 B CN 117473347B
- Authority
- CN
- China
- Prior art keywords
- data
- beneficiation
- certain
- subsequence
- mineral separation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims description 13
- 229910052500 inorganic mineral Inorganic materials 0.000 claims abstract description 134
- 239000011707 mineral Substances 0.000 claims abstract description 134
- 238000000926 separation method Methods 0.000 claims abstract description 123
- 230000002159 abnormal effect Effects 0.000 claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 44
- 238000012545 processing Methods 0.000 claims abstract description 31
- 230000005856 abnormality Effects 0.000 claims abstract description 8
- 230000005540 biological transmission Effects 0.000 claims description 48
- 230000008569 process Effects 0.000 claims description 19
- 238000003860 storage Methods 0.000 claims description 13
- 238000005215 recombination Methods 0.000 claims description 7
- 230000006798 recombination Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000008521 reorganization Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- -1 electricity Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
Abstract
The invention discloses a method and a system for processing ore dressing whole-flow data based on a rule engine, wherein the method comprises the following steps: clustering each beneficiation data in the first beneficiation data sequence according to the data type; inputting at least one mineral separation data subsequence into a pre-configured rule engine based on a preset input rule, and identifying each mineral separation data in the at least one mineral separation data subsequence according to the rule engine; determining position information of abnormal mineral separation data with abnormality in at least one mineral separation data subsequence according to the identification result, and recombining the at least one mineral separation data subsequence according to the position information of the abnormal mineral separation data to obtain at least one target mineral separation data subsequence and at least one abnormal mineral separation data subsequence; and uploading the recombined at least one target beneficiation data subsequence and at least one abnormal beneficiation data subsequence to a user side respectively. The identification of multi-source beneficiation data can be realized.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a rule engine-based ore dressing whole-flow data processing method and system.
Background
Mineral separation plays a vital role in the field of mining, and is an important link in the process of mineral processing, and related data generally comprise mineral flow, water, electricity, gas data, equipment operation index data and the like. The traditional mineral processing data processing technology is usually based on scientific principles and process experience, performs manual or semi-automatic data processing through modes of configuration monitoring, sensor acquisition, field test measurement and the like, and controls and adjusts equipment and production process by operators through experience and manual operation.
The traditional automatic mode has the problems that the data quality is poor, the data can not be quickly adapted to the complex ore business condition and the like due to the lack of a real-time data processing means. The intelligent mineral processing data processing technology is an important component of the intelligent mineral processing technology, and realizes comprehensive processing and analysis of mineral processing data by cleaning, preprocessing, feature extraction, data conversion, calculation and the like on data acquired in the mineral processing process, so that mineral processing efficiency is improved, cost is reduced, mineral processing process control is assisted, healthy operation of key equipment is ensured, and ore resources are utilized to the greatest extent. However, since the ore processing process is complex, the related data amount and data types are relatively large, so that the data of the whole ore dressing process is processed based on a method which is not very good at present.
Disclosure of Invention
The invention provides a method and a system for processing ore dressing whole-flow data based on a rule engine, which are used for solving the technical problem that the ore dressing whole-flow data cannot be processed efficiently due to complex ore processing process and relatively large related data quantity and data types.
In a first aspect, the present invention provides a rule engine-based beneficiation complete process data processing method, including:
acquiring a first beneficiation data sequence in a preset time period, and analyzing each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data;
judging whether the data types of the beneficiation data in the first beneficiation data sequence are consistent;
if the data types are inconsistent, clustering each beneficiation data in the first beneficiation data sequence according to the data types, and arranging the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence;
inputting the at least one mineral separation data subsequence into a pre-configured rule engine based on a preset input rule, and identifying each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio;
determining position information of abnormal mineral separation data with abnormality in the at least one mineral separation data subsequence according to the identification result, and recombining the at least one mineral separation data subsequence according to the position information of the abnormal mineral separation data to obtain at least one target mineral separation data subsequence and at least one abnormal mineral separation data subsequence;
and uploading the at least one target beneficiation data subsequence obtained by recombination and the at least one abnormal beneficiation data subsequence to a user terminal respectively.
In a second aspect, the present invention provides a rule engine-based beneficiation complete process data processing system, comprising:
the analysis module is configured to acquire a first beneficiation data sequence within a preset time period, and analyze each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data;
the judging module is configured to judge whether the data types of the beneficiation data in the first beneficiation data sequence are consistent;
the clustering module is configured to cluster each beneficiation data in the first beneficiation data sequence according to the data type if the beneficiation data are inconsistent, and arrange the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence;
the identification module is configured to input the at least one mineral separation data subsequence into a preconfigured rule engine based on a preset input rule, and identify each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio;
the reorganization module is configured to determine the position information of the abnormal beneficiation data with the abnormality in the at least one beneficiation data subsequence according to the identification result, reorganize the at least one beneficiation data subsequence according to the position information of the abnormal beneficiation data, and obtain at least one target beneficiation data subsequence and at least one abnormal beneficiation data subsequence;
and the uploading module is configured to upload the at least one target beneficiation data subsequence and the at least one abnormal beneficiation data subsequence obtained through recombination to a user terminal respectively.
In a third aspect, there is provided an electronic device, comprising: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the data transmission method in the beneficiation process of any one of the embodiments of the present invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor causes the processor to perform the steps of the data transmission method in the beneficiation process of any of the embodiments of the present invention.
According to the data transmission method and system in the beneficiation process, the beneficiation data subsequence is input into the rule engine configured in advance based on the preset input rule, and the beneficiation data in the beneficiation data subsequence are identified according to the rule engine, so that the identification of the multi-source beneficiation data can be realized, the rule engine is dynamically updated, the identification efficiency is ensured, the number of transmission links in the rule engine is not increased, and the burden of the rule engine is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a rule engine-based beneficiation whole-flow data processing method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a rule engine-based ore dressing whole-flow data processing system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a rule engine-based beneficiation full-flow data processing method is shown.
As shown in fig. 1, the whole mineral processing flow data processing method based on the rule engine specifically comprises the following steps:
step S101, a first beneficiation data sequence in a preset time period is obtained, and each beneficiation data in the first beneficiation data sequence is analyzed to obtain a data type associated with each beneficiation data.
In the step, after a first beneficiation data sequence in a preset time period, acquiring field information of a target field in each beneficiation data in the first beneficiation data sequence, wherein the field information comprises area information and a field name of the target field; inputting the field information into a preset data type recognition model for recognition to obtain data types associated with each beneficiation data, wherein the data type recognition model comprises a first type recognition model for recognizing the area information and a second type recognition model for recognizing the field names. Note that beneficiation data includes, but is not limited to, geological data, ore data, production data, energy data, and operation and maintenance data. For example, the inspection system provides chemical data of subsequent ores, physical properties of the ores, and thus the inspection system acquires data by manually entering instrument detection indicators. The geological information system provides geological data, which is also manually input, and the dcs system and each production system provide production data, energy data and equipment operation and maintenance data, and the data is automatically uploaded through equipment.
In one particular embodiment, the neural network may be trained using a LightGBM (Light Gradient Boostinglachine, gradient lifting) algorithm to obtain a first type of recognition model and a second type of recognition model. By setting the first type identification model and the second type identification model, double inquiry can be realized, the data types corresponding to the beneficiation data can be determined and output based on the double inquiry, and the accuracy and the efficiency of data type identification of the beneficiation data can be improved.
Step S102, judging whether the data types of the beneficiation data in the first beneficiation data sequence are consistent.
In the step, if the first beneficiation data sequence is consistent, the first beneficiation data sequence is directly input into a pre-configured rule engine, and each beneficiation data in the first beneficiation data sequence is identified according to the rule engine.
Step S103, if the data types are inconsistent, clustering the beneficiation data in the first beneficiation data sequence, and arranging the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence.
In this step, clustering the beneficiation data in the first beneficiation data sequence according to the data types means that the beneficiation data of the same data type are arranged and combined into one beneficiation data subsequence based on time sequence, and since the beneficiation data in the first beneficiation data sequence has a plurality of data types, a plurality of beneficiation data subsequences can be obtained.
Step S104, inputting the at least one mineral separation data subsequence into a pre-configured rule engine based on a preset input rule, and identifying each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: and obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio.
In this step, the rule engine includes a matching node and at least one transmission link connected to the matching node, and an identification node is disposed in one transmission link. By inputting at least one sub-sequence of beneficiation data into a preconfigured rule engine through a preset input rule, timeliness can be considered, so that beneficiation data acquired earlier are input into the rule engine for identification.
It should be noted that, a certain data type to which a certain beneficiation data subsequence belongs is obtained, and whether the rule engine contains at least one transmission link associated with the certain data type is judged; if the rule engine does not contain at least one transmission link associated with a certain data type, sequencing at least one currently idle transmission link based on the frequency of use, and adding a target identification node in a certain currently idle transmission link with the least frequency of use, wherein the target identification node is connected with a certain identification node originally set in a certain currently idle transmission link with the least frequency of use in parallel; distributing a certain transmission link which is free at present and has the least frequency of use for a certain mineral separation data subsequence based on the matching node, and carrying out abnormal recognition on each mineral separation data in the certain mineral separation data subsequence based on a target recognition node in the certain transmission link; if the rule engine comprises at least one transmission link associated with a certain data type, a corresponding certain transmission link is randomly allocated to a certain beneficiation data subsequence directly based on the matching node; and carrying out abnormal recognition on each beneficiation data in a certain beneficiation data subsequence based on the recognition node in a certain transmission link.
Specifically, after adding a target identification node in a certain transmission link which is currently idle and has the least frequency, acquiring a first identification frequency of the certain identification node and a second identification frequency of the target identification node in a preset time period, and judging whether the second identification frequency is greater than the first identification frequency; if the second identification frequency is greater than the first identification frequency, removing a certain identification node to obtain a transmission link only comprising the target identification node; and if the second identification frequency is not greater than the first identification frequency, removing the target identification node to obtain a transmission link only comprising a certain identification node. In this way, the method of updating the identification nodes in the transmission links is adopted, so that the number of the transmission links in the rule engine is not increased while the identification efficiency is ensured, and the burden of the rule engine is reduced.
For example, the identification node includes a data content identification model corresponding to the data type. And carrying out abnormal recognition on each beneficiation data in a certain beneficiation data subsequence based on a certain data content recognition model corresponding to the data type of the certain beneficiation data subsequence.
Step S105, determining the position information of the abnormal mineral separation data in the at least one mineral separation data subsequence according to the identification result, and recombining the at least one mineral separation data subsequence according to the position information of the abnormal mineral separation data to obtain at least one target mineral separation data subsequence and at least one abnormal mineral separation data subsequence.
In this step, the position information of the abnormal beneficiation data in which the abnormality exists in at least one beneficiation data subsequence can be determined according to the identification structure. By extracting the abnormal beneficiation data and arranging the abnormal beneficiation data based on time sequence, at least one abnormal beneficiation data subsequence can be obtained, and the rest beneficiation data is arranged based on time sequence, so that at least one target beneficiation data subsequence is obtained.
And S106, uploading the recombined at least one target beneficiation data subsequence and the at least one abnormal beneficiation data subsequence to a user side respectively.
In summary, the method of the application inputs the mineral separation data subsequence into the preconfigured rule engine based on the preset input rule, and identifies each mineral separation data in the mineral separation data subsequence according to the rule engine, so that the identification of multi-source mineral separation data can be realized, and the method of dynamically updating the rule engine is adopted, so that the identification efficiency is ensured, the number of transmission links in the rule engine is not increased, and the burden of the rule engine is reduced.
Referring now to FIG. 2, a block diagram of a rule engine based beneficiation whole process data processing system is shown.
As shown in fig. 2, the beneficiation complete process data processing system 200 includes an analysis module 210, a judgment module 220, a clustering module 230, an identification module 240, a reorganization module 250, and an uploading module 260.
The analyzing module 210 is configured to obtain a first beneficiation data sequence within a preset time period, and analyze each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data; a judging module 220 configured to judge whether the data types of the beneficiation data in the first beneficiation data sequence are consistent; the clustering module 230 is configured to cluster each beneficiation data in the first beneficiation data sequence according to the data type if the beneficiation data are inconsistent, and arrange the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence; the identifying module 240 is configured to input the at least one sub-sequence of beneficiation data into a preconfigured rule engine based on a preset input rule, and identify each beneficiation data in the at least one sub-sequence of beneficiation data according to the rule engine, wherein the preset input rule is: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio; the reorganizing module 250 is configured to determine, according to the identification result, location information of abnormal beneficiation data in the at least one beneficiation data subsequence, and reorganize the at least one beneficiation data subsequence according to the location information of the abnormal beneficiation data, to obtain at least one target beneficiation data subsequence and at least one abnormal beneficiation data subsequence; and the uploading module 260 is configured to upload the at least one target beneficiation data subsequence and the at least one abnormal beneficiation data subsequence obtained by recombination to the user terminal respectively.
It should be understood that the modules depicted in fig. 2 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 2, and are not described here again.
In other embodiments, the present invention further provides a computer readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to perform the rule engine-based beneficiation whole-flow data processing method in any of the above method embodiments;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
acquiring a first beneficiation data sequence in a preset time period, and analyzing each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data;
judging whether the data types of the beneficiation data in the first beneficiation data sequence are consistent;
if the data types are inconsistent, clustering each beneficiation data in the first beneficiation data sequence according to the data types, and arranging the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence;
inputting the at least one mineral separation data subsequence into a pre-configured rule engine based on a preset input rule, and identifying each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio;
determining position information of abnormal mineral separation data with abnormality in the at least one mineral separation data subsequence according to the identification result, and recombining the at least one mineral separation data subsequence according to the position information of the abnormal mineral separation data to obtain at least one target mineral separation data subsequence and at least one abnormal mineral separation data subsequence;
and uploading the at least one target beneficiation data subsequence obtained by recombination and the at least one abnormal beneficiation data subsequence to a user terminal respectively.
The computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of a rule engine based beneficiation whole process data processing system, and the like. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located with respect to the processor, which may be connected to the rule engine based beneficiation whole process data processing system via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 3, where the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 3. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory 320, i.e. implements the rule engine-based beneficiation full-flow data processing method of the above-described method embodiment. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the rule engine based beneficiation whole process data processing system. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
As an implementation manner, the electronic device is applied to a rule engine-based ore dressing whole-flow data processing system, and is used for a client, and comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
acquiring a first beneficiation data sequence in a preset time period, and analyzing each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data;
judging whether the data types of the beneficiation data in the first beneficiation data sequence are consistent;
if the data types are inconsistent, clustering each beneficiation data in the first beneficiation data sequence according to the data types, and arranging the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence;
inputting the at least one mineral separation data subsequence into a pre-configured rule engine based on a preset input rule, and identifying each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio;
determining position information of abnormal mineral separation data with abnormality in the at least one mineral separation data subsequence according to the identification result, and recombining the at least one mineral separation data subsequence according to the position information of the abnormal mineral separation data to obtain at least one target mineral separation data subsequence and at least one abnormal mineral separation data subsequence;
and uploading the at least one target beneficiation data subsequence obtained by recombination and the at least one abnormal beneficiation data subsequence to a user terminal respectively.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A mineral dressing whole-flow data processing method based on a rule engine is characterized by comprising the following steps:
acquiring a first beneficiation data sequence in a preset time period, and analyzing each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data;
judging whether the data types of the beneficiation data in the first beneficiation data sequence are consistent;
if the data types are inconsistent, clustering each beneficiation data in the first beneficiation data sequence according to the data types, and arranging the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence;
inputting the at least one mineral separation data subsequence into a pre-configured rule engine based on a preset input rule, and identifying each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio;
the rule engine comprises a matching node and at least one transmission link connected with the matching node, wherein an identification node is arranged in one transmission link;
the identifying each beneficiation data in the at least one subsequence of beneficiation data according to the rules engine comprises:
acquiring a certain data type of a certain beneficiation data subsequence, and judging whether the rule engine comprises at least one transmission link associated with the certain data type or not;
if the rule engine does not contain at least one transmission link associated with a certain data type, sequencing at least one transmission link which is currently idle based on the frequency of use, and adding a target identification node in a certain transmission link which is currently idle and has the least frequency of use, wherein the target identification node is connected with a certain identification node which is originally arranged in a certain transmission link which is currently idle and has the least frequency of use in parallel;
assigning a certain transmission link which is free at present and has the least frequency of use to the certain mineral separation data subsequence based on the matching node, and carrying out abnormal recognition on each mineral separation data in the certain mineral separation data subsequence based on a target recognition node in the certain transmission link;
determining position information of abnormal mineral separation data with abnormality in the at least one mineral separation data subsequence according to the identification result, and recombining the at least one mineral separation data subsequence according to the position information of the abnormal mineral separation data to obtain at least one target mineral separation data subsequence and at least one abnormal mineral separation data subsequence;
and uploading the at least one target beneficiation data subsequence obtained by recombination and the at least one abnormal beneficiation data subsequence to a user terminal respectively.
2. The method for processing beneficiation complete flow data based on a rule engine according to claim 1, wherein the parsing each beneficiation data in the first beneficiation data sequence to obtain the data type associated with each beneficiation data comprises:
acquiring field information of a target field in each beneficiation data, wherein the field information comprises area information and field names of the target field;
and inputting the field information into a preset data type identification model for identification to obtain data types associated with each beneficiation data, wherein the data type identification model comprises a first type identification model for identifying the area information and a second type identification model for identifying the field names.
3. The rule engine-based beneficiation complete process data processing method according to claim 1, wherein after determining whether data types of respective beneficiation data in the first beneficiation data sequence are consistent, the method further comprises:
if the first beneficiation data sequence is consistent, the first beneficiation data sequence is directly input into a pre-configured rule engine, and each beneficiation data in the first beneficiation data sequence is identified according to the rule engine.
4. The method for processing mineral separation full-flow data based on a rule engine according to claim 1, wherein after adding a target identification node to a transmission link that is currently idle and has the least frequency of use, the method further comprises:
acquiring a first identification frequency of a certain identification node and a second identification frequency of the target identification node in a preset time period, and judging whether the second identification frequency is greater than the first identification frequency;
if the second identification frequency is greater than the first identification frequency, removing the certain identification node to obtain a transmission link only comprising the target identification node;
and if the second identification frequency is not greater than the first identification frequency, removing the target identification node to obtain a transmission link only comprising a certain identification node.
5. A rule engine-based beneficiation whole process data processing method in accordance with claim 1, wherein after determining whether at least one transmission link associated with the certain data type is contained in the rule engine, the method further comprises:
if the rule engine comprises at least one transmission link associated with a certain data type, directly distributing a corresponding certain transmission link for a certain beneficiation data subsequence based on the matching node;
and carrying out abnormal recognition on each beneficiation data in a certain beneficiation data subsequence based on the recognition node in the certain transmission link.
6. The full-flow data processing method based on the rule engine according to claim 5, wherein the identification node comprises a data content identification model corresponding to the data type;
the performing anomaly identification on each beneficiation data in the certain beneficiation data subsequence based on the identification node in the certain transmission link comprises the following steps:
and carrying out abnormal recognition on each beneficiation data in a certain beneficiation data subsequence based on a certain data content recognition model corresponding to the data type of the certain beneficiation data subsequence.
7. A rule engine-based beneficiation complete process data processing system, comprising:
the analysis module is configured to acquire a first beneficiation data sequence within a preset time period, and analyze each beneficiation data in the first beneficiation data sequence to obtain a data type associated with each beneficiation data;
the judging module is configured to judge whether the data types of the beneficiation data in the first beneficiation data sequence are consistent;
the clustering module is configured to cluster each beneficiation data in the first beneficiation data sequence according to the data type if the beneficiation data are inconsistent, and arrange the beneficiation data based on a time sequence to obtain at least one beneficiation data subsequence;
the identification module is configured to input the at least one mineral separation data subsequence into a preconfigured rule engine based on a preset input rule, and identify each mineral separation data in the at least one mineral separation data subsequence according to the rule engine, wherein the preset input rule is as follows: obtaining time nodes of each mineral separation data in a certain mineral separation data subsequence, accumulating each time node to obtain a certain time span of the certain mineral separation data subsequence, calculating the ratio of the certain time span of the certain mineral separation data subsequence to the time span of the first mineral separation data sequence, and inputting at least one mineral separation data subsequence into a preconfigured rule engine according to the ratio;
the rule engine comprises a matching node and at least one transmission link connected with the matching node, wherein an identification node is arranged in one transmission link;
the identifying each beneficiation data in the at least one subsequence of beneficiation data according to the rules engine comprises:
acquiring a certain data type of a certain beneficiation data subsequence, and judging whether the rule engine comprises at least one transmission link associated with the certain data type or not;
if the rule engine does not contain at least one transmission link associated with a certain data type, sequencing at least one transmission link which is currently idle based on the frequency of use, and adding a target identification node in a certain transmission link which is currently idle and has the least frequency of use, wherein the target identification node is connected with a certain identification node which is originally arranged in a certain transmission link which is currently idle and has the least frequency of use in parallel;
assigning a certain transmission link which is free at present and has the least frequency of use to the certain mineral separation data subsequence based on the matching node, and carrying out abnormal recognition on each mineral separation data in the certain mineral separation data subsequence based on a target recognition node in the certain transmission link;
the reorganization module is configured to determine the position information of the abnormal beneficiation data with the abnormality in the at least one beneficiation data subsequence according to the identification result, reorganize the at least one beneficiation data subsequence according to the position information of the abnormal beneficiation data, and obtain at least one target beneficiation data subsequence and at least one abnormal beneficiation data subsequence;
and the uploading module is configured to upload the at least one target beneficiation data subsequence and the at least one abnormal beneficiation data subsequence obtained through recombination to a user terminal respectively.
8. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311833072.XA CN117473347B (en) | 2023-12-28 | 2023-12-28 | Ore dressing full-flow data processing method and system based on rule engine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311833072.XA CN117473347B (en) | 2023-12-28 | 2023-12-28 | Ore dressing full-flow data processing method and system based on rule engine |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117473347A CN117473347A (en) | 2024-01-30 |
| CN117473347B true CN117473347B (en) | 2024-04-02 |
Family
ID=89638356
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311833072.XA Active CN117473347B (en) | 2023-12-28 | 2023-12-28 | Ore dressing full-flow data processing method and system based on rule engine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117473347B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101035131A (en) * | 2007-02-16 | 2007-09-12 | 杭州华为三康技术有限公司 | Protocol recognition method and device |
| CN101582776A (en) * | 2008-05-15 | 2009-11-18 | 浙江大学 | Service charging method and service charging system |
| CN114418146A (en) * | 2022-01-24 | 2022-04-29 | 浙江省机电设计研究院有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on rule engine |
| CN116956068A (en) * | 2023-07-26 | 2023-10-27 | 中国平安人寿保险股份有限公司 | Intention recognition method and device based on rule engine, electronic equipment and medium |
| CN117093619A (en) * | 2023-08-24 | 2023-11-21 | 中国建设银行股份有限公司 | Rule engine processing method and device, electronic equipment and storage medium |
| CN117234156A (en) * | 2023-06-21 | 2023-12-15 | 首钢滦南马城矿业有限责任公司 | Ore dressing plant inspection system and inspection method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10885038B2 (en) * | 2018-07-03 | 2021-01-05 | Cognizant Technology Solutions India Pvt. Ltd. | System and method for adaptive information storage management |
| US11223642B2 (en) * | 2019-09-14 | 2022-01-11 | International Business Machines Corporation | Assessing technical risk in information technology service management using visual pattern recognition |
| GB2587224B (en) * | 2019-09-19 | 2021-09-15 | Imagination Tech Ltd | Methods and tiling engines for hierarchially tiling primitives in a graphics processing system |
-
2023
- 2023-12-28 CN CN202311833072.XA patent/CN117473347B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101035131A (en) * | 2007-02-16 | 2007-09-12 | 杭州华为三康技术有限公司 | Protocol recognition method and device |
| CN101582776A (en) * | 2008-05-15 | 2009-11-18 | 浙江大学 | Service charging method and service charging system |
| CN114418146A (en) * | 2022-01-24 | 2022-04-29 | 浙江省机电设计研究院有限公司 | Electromechanical equipment fault diagnosis method, system and terminal based on rule engine |
| CN117234156A (en) * | 2023-06-21 | 2023-12-15 | 首钢滦南马城矿业有限责任公司 | Ore dressing plant inspection system and inspection method |
| CN116956068A (en) * | 2023-07-26 | 2023-10-27 | 中国平安人寿保险股份有限公司 | Intention recognition method and device based on rule engine, electronic equipment and medium |
| CN117093619A (en) * | 2023-08-24 | 2023-11-21 | 中国建设银行股份有限公司 | Rule engine processing method and device, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| Research on Intelligent Early warning Strategy based on Rule engine;xiao zeng 等;IEEE;20221122;662-669 * |
| 基于自主运行体系结构的通用推理引擎设计;刘博;严云红;程云龙;彭立章;陈元;;上海航天;20170825(04);122-128 * |
| 基于规则引擎及智能阈值的实时业务风控系统;张鲁男;常宝岗;梅利;;通信技术;20191110(11);146-150 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117473347A (en) | 2024-01-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115122155B (en) | Machine tool remote diagnosis method and system based on industrial internet big data | |
| CN111090643B (en) | Mass electricity consumption data mining method based on data analysis system | |
| CN111782460A (en) | Large-scale log data anomaly detection method and device and storage medium | |
| CN116862081B (en) | Operation and maintenance method and system for pollution treatment equipment | |
| CN114757587B (en) | Product quality control system and method based on big data | |
| CN110895506A (en) | Construction method and construction system of test data | |
| CN110134040B (en) | Method and system for processing operation data of industrial equipment | |
| CN117473347B (en) | Ore dressing full-flow data processing method and system based on rule engine | |
| CN114020715A (en) | Log data processing method, device, medium and equipment | |
| CN115081485B (en) | AI-based magnetic flux leakage internal detection data automatic analysis method | |
| CN116435995A (en) | Time series processing method, computer readable storage medium and electronic device | |
| CN115687406A (en) | Sampling method, device and equipment of call chain data and storage medium | |
| CN115348293A (en) | Intelligent control remote operation and maintenance method and platform for industrial internet equipment | |
| CN113610225A (en) | Quality evaluation model training method and device, electronic equipment and storage medium | |
| CN117081666B (en) | Fault prediction method, device, electronic equipment, storage medium and program product | |
| CN115204424A (en) | Intelligent operation and maintenance method, device, equipment and storage medium of cloud native system | |
| CN117974155A (en) | User complaint pre-processing method, device and equipment | |
| CN116974862A (en) | SQL processing method, device, equipment and storage medium | |
| CN117495422A (en) | Cost management system and method based on power communication network construction | |
| CN114896463A (en) | Problem entry method and system based on automobile quality problem database | |
| CN116805176A (en) | Load prediction method, device and equipment for transformer area and storage medium | |
| CN115796307A (en) | Automatic learning method and device for artificial intelligence system of generator set and readable medium | |
| CN116860889A (en) | Electric power Internet of things information collection method | |
| CN117579537A (en) | Big data transmission method and system for real-time communication | |
| CN115237606A (en) | Edge calculation control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |