Mineral product sampling information management system and method

Abstract

The invention relates to the technical field of mineral product sampling and preparation information management, in particular to a mineral product sampling and preparation information management system and method.The mineral product sampling and preparation information management system comprises an input module, a preprocessing module, a data modification module and a data storage module; according to the system, firstly, sampling information of mineral products is input; then sample weight information in the sample taking and preparing information is preprocessed; in the preprocessing operation process, a set threshold value is set for each sample to screen the potential abnormal points of the weighing data in the sample, so that the setting of the threshold values can adapt to the dynamic change of the data, and the potential abnormal points can be identified more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of information management for ore product sampling and sample preparation, and particularly to an information management system and method for ore product sampling and sample preparation. Background Art

[0002] As one of the technical means for ore product sampling information management, commodity classification during sampling and testing provides key technical support for work such as cargo solid waste identification, access risk screening, cargo grade assessment, and pricing parameter verification. For high-value ore products, due to price settlement requirements, higher requirements are imposed on sampling, sample preparation, and moisture detection. To ensure the accuracy of moisture, the formulation and implementation of the sampling plan, sample preservation, preparation, moisture detection, etc. all need to be strictly executed and recorded according to specifications.

[0003] There are technical solutions for coal mine information management in the prior art. For example, Chinese Invention Patent (CN116308090A) discloses a coal mine operation information management method based on underground operation locations. The method includes: obtaining the operation locations corresponding to the coal mine and the operation information corresponding to the operation locations; constructing a location tree based on the operation locations, where the location tree is used to represent the hierarchical relationship between the operation locations and the life stage where the operation locations are located; associating the operation information to the corresponding operation locations in the location tree; and managing the operation information based on the life stage where the operation locations are located. Through the constructed location tree, various operation information of underground operation locations at different levels is hierarchically managed, affiliated or associated under the corresponding locations, and information control is performed based on the life stage to clearly manage the operation information of the coal mine, so as to screen out the required target locations according to the life stage and be able to quickly and efficiently obtain the operation information of the target locations as needed. However, the above solution does not preprocess the information when inputting coal mine information management, which may lead to errors in the input information. At the same time, in the prior art, abnormal point screening is achieved by setting an adaptive dynamic threshold. However, its research idea is basically limited to analyzing big data to set an adaptive threshold, and does not focus on each sample data, resulting in insufficient accuracy of the dynamic threshold setting in reflecting the data characteristics of each sample. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides an information management system and method for ore product sampling and sample preparation to solve the problems existing in the prior art.

[0005] According to one aspect of the present invention, there is provided an information management system for ore product sampling and sample preparation, including: An input module for performing an input operation on the ore product sampling and sample preparation information; A preprocessing module for performing preprocessing operations on the sample weight information in the sampling information; specifically, the preprocessing module performing preprocessing operations on the sample weight information in the sampling information is as follows: Obtain the sample weight data of each sample; Calculate the moving average and moving standard deviation of the sample weight data of each sample; Compare the data points in the sample weight data with the moving standard deviation to screen out potential outliers; A data modification module for feeding back the sample weight data with the potential outliers to the reviewer for manual review and modification; A data storage module for storing the sampling information of the mineral products after review and modification.

[0006] Preferably, in the preprocessing module, the comparison of the data points in the sample weight data with the moving standard deviation to screen out potential outliers is specifically as follows: If the deviation between the data point in the sample weight data and the moving standard deviation exceeds the set threshold corresponding to the sample, then this data point is considered a potential outlier.

[0007] Preferably, in the preprocessing module, the determination method of the set threshold T i for the i-th sample is as follows: Calculate the maximum deviation D between the data points in the weight data of the i-th sample and the moving standard deviation i ; Set the initial set threshold T0; Determine the set threshold T i for the i-th sample according to the maximum deviation D i ; Among them, the determination formula for the set threshold of the i-th sample is:

[0008] In the formula, β is a dynamic adjustment factor, and sign() is a sign function.

[0009] Preferably, the calculation formula for the moving average Moving Average of the sample weight data is: ; In the formula, k is the size of the moving window, t is the step size, and x a is the a-th weight data of the sample.

[0010] Preferably, the calculation formula for the moving standard deviation Moving Std of the sample weight data is: .

[0011] Preferably, the information on sample preparation of mineral products includes sampling time, sampling location, sampling personnel, sample number, sample weight information, sample particle size, and sample moisture content.

[0012] Preferably, the data storage module uses an SQLite database for storage operations.

[0013] According to another aspect of the present invention, there is provided a method for managing information on sample preparation of mineral products, which is applied to the above-mentioned information management system for sample preparation of mineral products. The system includes: S1: Perform an input operation on the information on sample preparation of mineral products; S2: Perform a preprocessing operation on the sample weight information in the sample preparation information; Specifically, S2 is as follows: S2.1: Obtain the sample weight data of each sample; S2.2: Calculate the moving average and moving standard deviation of the sample weight data of each sample; S2.3: Compare the data points in the sample weight data with the moving standard deviation to screen out potential abnormal points; S3: Feed back the sample weight data with the potential abnormal points to the reviewer for manual review and modification; S4: Perform a storage operation on the information on sample preparation of mineral products after being reviewed and modified in S3.

[0014] The present invention has the following technical effects: The information management system for sample preparation of mineral products proposed by the present invention includes an input module, a preprocessing module, a data modification module, and a data storage module. The system first performs an input operation on the information on sample preparation of mineral products; then performs a preprocessing operation on the sample weight information in the sample preparation information. During the preprocessing operation, a set threshold is set for each sample to screen out potential abnormal points in the weighing data of the sample, so that the setting of the threshold can adapt to the dynamic change of the data, and thus potential abnormal points can be identified more accurately. Description of the Drawings

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1It is a schematic diagram of a mineral product sampling and sample preparation information management system provided by an embodiment of the present invention; Figure 2 It is a flowchart of a method for managing mineral product sampling and sample preparation information provided by an embodiment of the present invention; Figure 3 It is a flowchart of preprocessing operations on the sample weight information in the sampling and sample preparation information provided by an embodiment of the present invention. Detailed implementation manners

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope protected by the present invention.

[0018] Embodiment 1, as shown in the appended Figure 1 figures, shows a schematic diagram of a mineral product sampling and sample preparation information management system. As shown in the appended Figure 1 figures, a mineral product sampling and sample preparation information management system, the system includes: An input module for performing input operations on the mineral product sampling and sample preparation information; A preprocessing module for performing preprocessing operations on the sample weight information in the sampling and sample preparation information; A data modification module for feeding back the sample weight data with the potential abnormal points to the review personnel for manual review and modification; A data storage module for storing the mineral product sampling and sample preparation information that has been reviewed and modified.

[0019] Among them, in the preprocessing module, the comparison of the data points in the sample weight data with the moving standard deviation to screen out potential abnormal points is specifically: If the deviation between the data point in the sample weight data and the moving standard deviation exceeds the set threshold corresponding to the sample, then this data point is considered a potential abnormal point.

[0020] Among them, in the preprocessing module, the determination method of the set threshold T i for the i-th sample is: Calculate the maximum deviation D i between the data points in the weight data of the i-th sample and the moving standard deviation; Set the initial set threshold T0; Determine the set threshold T i for the i-th sample according to the maximum deviation D i and the initial set threshold T0; Among them, the determination formula for the set threshold of the i-th sample is: ; In the formula, β is the dynamic adjustment factor, and sign() is the sign function.

[0021] Among them, the calculation formula for the moving average Moving Average of the sample weight data is: ; In the formula, k is the size of the moving window, t is the step size, and x a is the a-th weight data of the sample.

[0022] The calculation formula for the moving standard deviation Moving Std of the sample weight data is: .

[0023] Among them, the information on the sampling and sample preparation of mineral products includes sampling time, sampling location, sampling personnel, sample number, sample weight information, sample particle size, and sample moisture content.

[0024] Among them, the data storage module uses the SQLite database for storage operations.

[0025] Example 2, as shown in the appendix Figure 2 This invention also provides a method for managing information on the sampling and sample preparation of mineral products. This method is applied to a system for managing information on the sampling and sample preparation of mineral products in Example 1. The method includes: S1: Perform an input operation on the information on the sampling and sample preparation of mineral products; Among them, the information on the sampling and sample preparation of mineral products includes sampling time, sampling location, sampling personnel, sample number, sample weight information, sample particle size, sample moisture content, etc.; Among them, before the input of the information on the sampling and sample preparation of mineral products, according to the characteristics of the business process, it also includes the following several stages: (1) Declaration stage - The declarant fills in relevant requirement information, including basic information on goods and information on inspection work items, and submits it to the acceptor; (2) Acceptance stage - The acceptor accepts the declaration, assigns a unique business number, and transfers it to the subsequent operator according to the work item information; (3) Sampling stage - If the work item includes sampling, the sampling personnel carry out the sampling work according to the accepted business. After completion, the original sample is transferred to the sample preparer, and the sampling record is filled in; (4) Particle size detection stage - If the work item includes particle size detection, the inspector preferably uses the original sample for determination and fills in the record; (5) Sample preparation stage - After obtaining the original sample, the sample preparer conducts sample reduction. If the work item includes moisture detection, a moisture detection sample is extracted as required and handed over to the inspector for moisture testing. A general analysis sample is extracted and dried in an oven (when the demand for the general analysis sample is not large, usually no additional extraction is required, and the dried sample after moisture testing is used). The original retained sample is extracted, labeled, and stored. After the general analysis sample is dried, it is ground and sieved to obtain an analysis sample with a specified particle size, which is then sealed and transferred to the laboratory and the applicant respectively according to the required quantity of the sample, and a sample preparation record is filled out; (6) Moisture detection stage - If the work item includes moisture detection, after obtaining the moisture detection sample, the inspector conducts moisture detection, fills out the moisture record, and transfers the dried sample to the sample preparer; (7) Analysis sample receipt stage - The analysis sample receiver fills out a receipt record after obtaining the sample.

[0026] S2: Perform a preprocessing operation on the sample weight information in the sampling and sample preparation information; In the stage of filling out the moisture detection record, since the sample weight information needs to be weighed multiple times, there are a large number of sample weight information data to be recorded, and it is easy for the inspector to enter the weight data incorrectly. In the prior art, generally, the inspector needs to spend a lot of time checking and verifying the accuracy of the data, which undoubtedly leads to low efficiency in the management of sampling and sample preparation information. Therefore, this embodiment proposes a model-based preprocessing operation for sampling and sample preparation information to screen out samples with incorrect weighing information; Specifically, as shown in the appendix Figure 3 shown, the specific content of S2 is as follows: S2.1: Obtain the sample weight data of each sample; Among them, the sample weight data of each sample is a data set. After being input into the sampling and sample preparation information management system by the inspector, the system will automatically obtain the sample weight data of each sample.

[0027] S2.2: Calculate the moving average and moving standard deviation of the sample weight data of each sample; Among them, the calculation formula for the moving average Moving Average of the sample weight data is: ; In the formula, k is the size of the moving window, t is the step size, and x a is the a-th weight data of the sample; Among them, the calculation formula for the moving standard deviation Moving Std of the sample weight data is: ; S2.3: Compare the data points in the sample weight data with the moving standard deviation to screen out potential outliers; Among them, the specific content of S2.3 is as follows: If the deviation between the data point in the sample weight data and the moving standard deviation exceeds the set threshold, then this data point is considered a potential outlier; Determining an appropriate threshold is one of the key steps. The selection of the threshold directly affects the accuracy and sensitivity of outlier detection. Therefore, this embodiment further improves in the determination of the threshold.

[0028] Among them, the set threshold T of the i-th sample i is determined as follows: Sa: Calculate the maximum deviation D between the data points in the weight data of the i-th sample and the moving standard deviation i ; Sb: Set the initial set threshold T0; Sc: Determine the set threshold T of the i-th sample according to the maximum deviation D i and the initial set threshold T0 i ; Among them, the determination formula of the set threshold of the i-th sample is: ; In the formula, β is a dynamic adjustment factor, and sign() is a sign function; In this step, by setting a corresponding set threshold for the data characteristics of each sample, the setting of the threshold can adapt to the dynamic changes of the data, and then potential outliers can be identified more accurately.

[0029] S3: Feed back the sample weight data with the potential outliers to the reviewer for manual review and modification; In this step, through step S2, potential outliers can be identified by the algorithm, and then through the manual review step of this step, the manual review process only reviews the potential outliers screened by the algorithm, improving the review efficiency.

[0030] S4: Perform a storage operation on the sample preparation information of the mineral products after being reviewed and modified in S3; In this step, it is first necessary to determine the database for data storage. There are many types of commercial databases supported by Python, including relational databases such as MySQL and Oracle, and non-relational databases such as MongoDB. These databases require the additional installation of a database service system and client libraries, which is rather troublesome. Python comes with a simpler SQLite database as a support library that can be directly called by application programs. Therefore, in this embodiment, the SQLite database is directly used for storage operations. When deploying to the server later, only the configuration file needs to be modified to the relevant MySQL configuration to use the installed MySQL service system and client libraries on the server.

[0031] Meanwhile, to facilitate the association operation between the program code and the database, an object-oriented programming method is used to create three data object models: Cargo, User, and OptionSet. Flask-SQLAlchemy is used to map the data objects to the database, making the objects correspond to the tables in the database and the object attributes correspond to the columns in the tables.

[0032] Examples of the attributes of the three data object models are shown in Table 1. The Cargo object model defines 71 attributes, most of which correspond to the basic information and record information of mineral products, and a small number of attributes are used for node process control; the User object model defines 17 attributes, including 1 primary key id attribute, 5 user information attributes, and 11 role marking attributes; the OptionSet object model defines 3 attributes, including 1 primary key id attribute, 1 category label attribute, and 1 information content attribute. After the program is deployed, first log in to the system using the administrator account, and add 12 pieces of information required in the page form in the "Information Maintenance" module, and submit them to the database for storage so that they can be called in the record filling page.

[0033] Table 1 Examples of the Attributes of the Data Object Model

[0034] Among them, when storing the sampling information of mineral products, different user permissions are also set to achieve information entry. Specifically, the operations at different stages and with different permissions are placed in the specified blueprints for modular management of the program code. According to the business process, 11 modules are divided, and the corresponding user roles and blueprints are shown in Table 2. In addition, each user can set multiple roles to achieve operations on multiple modules. The display and hiding of the user role modules are uniformly configured in the base template base.html, and the user permissions are verified each time an HTTP request is initiated.

[0035] Table 2 Correspondence Table of Modules, User Roles, and Blueprints

[0036] Example 3. The present invention further provides an electronic device, including one or more processors and a memory.

[0037] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0038] The memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage media, and the processor may run the program instructions to implement a method for managing information on sampling and sample preparation of mineral products according to any embodiment of the present application as described above and / or other desired functions. Various contents such as initial external parameters and thresholds may also be stored in the computer-readable storage media.

[0039] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected through a bus device and / or other forms of connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including early warning prompt information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0040] Of course, for simplicity, components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device may further include any other appropriate components.

[0041] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, and when the computer program instructions are run by the processor, the processor is caused to implement the functions of a method for managing information on sampling and sample preparation of mineral products provided by any embodiment of the present application.

[0042] The computer program product can be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The programming code can be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0043] In addition, an embodiment of the present application can also be a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are run by a processor, the processor implements a method for managing information on ore product sampling and sample preparation provided by any embodiment of the present application.

[0044] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, apparatuses, or components, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0045] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" do not specifically refer to the singular and may also include the plural. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, or device including the element.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A mineral product sampling information management system, characterized in that: include: An input module, used for inputting the sampling information of the mineral products; A preprocessing module is used to perform a preprocessing operation on the sample weight information in the sample preparation information; wherein the preprocessing module is used to perform a preprocessing operation on the sample weight information in the sample preparation information specifically as follows: Obtain sample weight data for each sample; Calculate the moving average and moving standard deviation of the sample weight data for each sample; Comparing the data points in the sample weight data with the moving standard deviation to screen out potential abnormal points; A data modification module, used to feed back the sample weight data with the potential abnormal points to the auditor for manual review and modification; The data storage module is used to store the mineral product sampling information that has been reviewed and modified.

2. A mineral product sampling information management system according to claim 1, characterized in that: In the preprocessing module, the data points in the sample weight data are compared with the moving standard deviation to screen out potential abnormal points. Specifically, If the deviation between a data point in the sample weight data and the moving standard deviation exceeds a set threshold corresponding to the sample, the data point is considered to be a potential abnormal point.

3. A mineral product sampling information management system according to claim 2, characterized in that: In the preprocessing module, the threshold T of the i-th sample is set i The method to determine is: Calculate the maximum deviation D of the data point in the weight data of the i-th sample from the moving standard deviation i ; Set the initial setting threshold T0; According to the maximum deviation D i The threshold value T of the i-th sample is determined by the initial threshold value T0. i ; The formula for determining the set threshold of the i-th sample is: Where β is the dynamic adjustment factor and sign() is the sign function.

4. A mineral product sampling information management system according to claim 1, characterized in that: The calculation formula of the moving average of the sample weight data is: ； In the formula, k is the size of the moving window, t is the step size, and x a is the ath weight data of the sample.

5. A mineral product sampling information management system according to claim 4, characterized in that: The calculation formula of the moving standard deviation Moving Std of the sample weight data is: 。 6. A mineral product sampling information management system according to claim 1, characterized in that: The mineral product sampling information includes sampling time, sampling location, sampling personnel, sample number, sample weight information, sample particle size, and sample moisture content.

7. A mineral product sampling information management system according to claim 1, characterized in that: The data storage module uses the SQLite database to perform storage operations.

8. A method for managing mineral sampling information, characterized in that: The method is applied to a mineral product sampling information management system according to any one of claims 1 to 7, and the method comprises: S1: Input the sampling information of the mineral product; S2: performing a preprocessing operation on the sample weight information in the sample preparation information; The S2 is specifically: S2.1: Obtain sample weight data for each sample; S2.2: Calculate the moving average and moving standard deviation of the sample weight data for each sample; S2.3: Compare the data points in the sample weight data with the moving standard deviation to screen out potential abnormal points; S3: Feedback the sample weight data with the potential abnormal points to the auditor for manual review and modification; S4: storing the mineral product sampling information that has been reviewed and modified in S3.

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