CN117541158A - Misplacement detection method, system and storage medium based on big data - Google Patents

Misplacement detection method, system and storage medium based on big data Download PDF

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Publication number
CN117541158A
CN117541158A CN202311788070.3A CN202311788070A CN117541158A CN 117541158 A CN117541158 A CN 117541158A CN 202311788070 A CN202311788070 A CN 202311788070A CN 117541158 A CN117541158 A CN 117541158A
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China
Prior art keywords
information
valve
misplacement
ore
level
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Pending
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CN202311788070.3A
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Chinese (zh)
Inventor
邓怀军
苟伟
李秀峰
谭友春
王兵
张涛
许文强
姜凯旋
杜和醨
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Chongqing Fuling Daye Building Materials Co ltd
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Chongqing Fuling Daye Building Materials Co ltd
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Priority to CN202311788070.3A priority Critical patent/CN117541158A/en
Publication of CN117541158A publication Critical patent/CN117541158A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/048—Monitoring; Safety
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms

Abstract

The invention relates to the technical field of ore discharge detection, and discloses a method, a system and a storage medium for detecting misplacement based on big data, wherein the method comprises the steps of obtaining an ore transaction order, and identifying basic information of a product and user transaction information required in the ore transaction order; judging whether the ore transaction order is effective or not according to the identified user transaction information, and if so, sending the corresponding ore transaction order to a central control terminal; when the ore transaction order is effective as a judgment result, determining the basic information of the materials according to the basic information of the products, and predicting the valve opening of a warehouse corresponding to the materials to generate valve opening prediction information; acquiring the material information of each corresponding warehouse, the valve opened in real time by each warehouse and the real-time opening information of the corresponding valve; judging whether the opening of the corresponding valve has a problem or not according to the valve opening prediction information and the valve real-time opening information, and if so, sending an alarm signal to the central control terminal.

Description

Misplacement detection method, system and storage medium based on big data
Technical Field
The invention relates to the technical field of ore discharge detection, in particular to a misplacement detection method, a misplacement detection system and a storage medium based on big data.
Background
The ores are placed in a warehouse system after mining, and in order to be able to determine the corresponding material type at the first time, the ores are usually classified and stored, i.e. one ore is stored in each warehouse, and the materials in the warehouse are accessed through the valves of the warehouse, in consideration of the fact that the materials and the proportions of the materials are different when customers purchase the ores.
However, when the valve of the warehouse is opened by an operator, the operator is very easy to have the problem of misplacing due to the fact that the difference between materials in the warehouse is not large, and the possibility of the occurrence of the corresponding misplacing is very high, so that the loaded materials are different from the materials required by customers, and the material loading efficiency is greatly reduced.
Disclosure of Invention
The invention aims to provide a misplacement detection method, a misplacement detection system and a storage medium based on big data, which can realize timely detection of misplacement conditions after a valve of a warehouse is opened, reduce the occurrence of misplacement problems of operators and greatly improve the material loading efficiency.
In order to achieve the above purpose, a method for detecting misplaced materials based on big data is provided, which comprises the following steps:
s1, acquiring an ore transaction order, and identifying basic information of a product required in the ore transaction order and user transaction information;
s2, analyzing and judging the user transaction information according to the identified user transaction information, judging whether the ore transaction order is effective, and if the ore transaction information is effective, sending the corresponding ore transaction order to the central control terminal;
s3, determining material basic information corresponding to the product basic information according to the identified product basic information when the ore transaction order is effective as a judgment result, wherein the material basic information comprises material composition information and material loading type; the material loading types comprise mixed loading type and independent type;
s4, predicting the valve opening of a warehouse corresponding to the materials according to the material composition information and the material loading type, and generating corresponding valve opening prediction information;
s5, monitoring all warehouses in the warehouse system in real time, and acquiring the material information of all corresponding warehouses, the valve opened in real time by each warehouse and the real-time opening information of the corresponding valve;
and S6, judging whether the opening of the corresponding valve is problematic according to the valve opening prediction information and the valve real-time opening information, if so, judging that the material corresponding to the valve is misplaced, and sending an alarm signal to the central control terminal.
The technical principle and effect of this scheme: according to the method, the ore transaction order is acquired at the first time, the product basic information and the user transaction basic information required in the order can be identified through the ore transaction order, then analysis and judgment are carried out by utilizing the user transaction information to determine whether the ore transaction order is effective, verification of the ore transaction order is achieved through the step, and therefore whether ore loading is controlled is achieved. And after verification is passed, the corresponding ore transaction order is sent to the central control end at the first time, so that an operator of the central control end can open a valve of a corresponding warehouse according to the ore transaction order, and loading of materials is realized.
Meanwhile, after the corresponding ore transaction order passes verification, material basic information corresponding to the product basic information is determined according to the identified product basic information at the first time, wherein the material basic information comprises material composition information and material loading types, and the proportions of materials corresponding to the ore transaction order and the corresponding materials can be known through the material composition information.
And then, predicting the valve opening of the warehouse corresponding to the materials according to the corresponding material composition information and the material loading type, so as to generate corresponding valve opening prediction information.
And then, monitoring all warehouses in the warehouse system in real time to acquire the material information of all corresponding warehouses, the valves opened in real time by all warehouses and the real-time opening information of the corresponding valves, monitoring the current discharging operation of all warehouses by the step, judging whether the opening of the corresponding valve has a problem or not according to the valve opening prediction information and the valve real-time opening information, if so, judging that the materials corresponding to the valve are misplaced, and sending an alarm signal to a central control terminal.
In this scheme, realize the management and control to the blowing of material in the warehouse through the verification to ore transaction order, and then make the operating personnel of corresponding well accuse end have the basis when carrying out the blowing operation of ore, simultaneously come the valve opening that corresponds this order through material composition information, material shipment type and predict, simultaneously through detecting the valve opening condition on the scene, whether it is reasonable to this valve opening through comparing valve opening prediction information and valve real-time opening information, whether there is the condition of wrong blowing to appear, the detection to wrong blowing has greatly been improved, the help that can be better carries out the blowing operation to the operating personnel of well accuse end, the possibility that the operating personnel has wrong blowing appears has greatly been avoided.
Further, the S4 includes:
s40, acquiring historical big data from a database, and constructing a valve opening prediction model; the historical big data comprise historical valve opening data and historical order data;
s41, inputting corresponding material composition information and material loading types based on the constructed valve opening prediction model, and outputting corresponding valve opening prediction information.
The beneficial effects are that: in the scheme, the construction of the valve opening prediction model is realized through the historical big data, the accuracy of valve opening prediction is greatly improved, and an effective basis can be better provided for subsequent prediction.
Further, the S6 includes:
s61, calculating a corresponding similarity value based on a similarity calculation formula according to valve opening prediction information and valve real-time opening information;
s62, judging whether the valve opening corresponding to the operator corresponding to the central control end has a problem during discharging according to the calculated similarity value, if so, determining the misplacement level corresponding to the discharging operation of the operator corresponding to the central control end based on a preset judging strategy; the misplacement levels include a first level, a second level, and a third level;
s63, carrying out real-time statistics on the misplacement level corresponding to the central control terminal, and calling a corresponding alarm strategy based on a preset statistical strategy and sending the alarm strategy to the central control terminal.
The beneficial effects are that: in this scheme, through the calculation to the similarity value, thereby know the degree of similarity between valve opening and the valve opening of prediction when the operating personnel of well accuse end carries out the blowing operation, thereby the difference of actual valve opening and prediction valve opening is confirmed in the first time, whether follow-up still utilize the similarity value to the valve opening of this blowing to have the problem to judge simultaneously, and when judging to have the problem, the first time is based on judge the tactics and confirm its misplacement level, afterwards carry out real-time statistics to the misplacement level that this well accuse end corresponds, in the in-process of whole blowing that just so can be clear, the real-time misplacement condition of operating personnel of corresponding well accuse end, thereby realize having the warning of pertinence and inform, greatly improve alarm effect and validity, the operating personnel of better help well accuse end carries out the blowing operation, avoid the appearance of operating personnel misplacement problem.
Further, the similarity calculation formula is:
wherein F is a similarity value, A is valve opening prediction information, B is valve real-time opening information,is a weighting coefficient.
The beneficial effects are that: according to the scheme, the similarity value is calculated rapidly and accurately through the disclosure of the similarity calculation formula, the efficiency and accuracy of similarity value determination are improved greatly, and meanwhile, the corresponding similarity value calculation is higher in reliability and higher in pertinence due to the arrangement of the weighting coefficient.
Further, the preset judgment policy is:
when F is less than or equal to x, the misplacement level is the first level;
when y > F > x, the misplacement level is a second level;
when F is more than or equal to y, the misplacement level is the third level.
The beneficial effects are that: in the scheme, the corresponding misplacement level is judged rapidly and accurately through judging the size of the similarity value.
The invention also provides a misplacement detection system based on the big data, and the misplacement detection method based on the big data is used.
A storage medium is also provided for storing computer-executable instructions that, when executed, implement the above-described big data based misplacement detection method.
Drawings
FIG. 1 is a flow chart of a misplaced material detection method based on big data in an embodiment of the invention.
Detailed Description
The following is a further detailed description of the embodiments:
example 1
The misplacement detection method based on big data is basically as shown in fig. 1, and comprises the following steps:
s1, acquiring an ore transaction order, and identifying basic information of a product required in the ore transaction order and user transaction information;
s2, analyzing and judging the user transaction information according to the identified user transaction information, judging whether the ore transaction order is effective, and if the ore transaction information is effective, sending the corresponding ore transaction order to the central control terminal;
s3, determining material basic information corresponding to the product basic information according to the identified product basic information when the ore transaction order is effective as a judgment result, wherein the material basic information comprises material composition information and material loading type; the material loading types comprise mixed loading type and independent type; in this embodiment, the materials may be set so that one material corresponds to N products, or one product may correspond to N materials, specifically, for example, crushed stone 5-10 may be used as high-speed rail crushed stone 5-10, washed crushed stone 5-10, and high-speed rail crushed stone 5-10 may be sold, or crushed stone 10-25 may be made up of crushed stone 5-10, crushed stone 10-20, and crushed stone 16-31.5 according to a certain proportion. Meanwhile, due to the fact that the difference between the materials is not large, operators at the central control end cannot accurately determine the types of the materials when the corresponding valves are opened, and further whether the materials are misplaced or not cannot be timely judged after the valves are opened.
S4, predicting the valve opening of a warehouse corresponding to the materials according to the material composition information and the material loading type, and generating corresponding valve opening prediction information;
the step S4 comprises the following steps:
s40, acquiring historical big data from a database, and constructing a valve opening prediction model; the historical big data comprise historical valve opening data and historical order data; in this embodiment, the construction of the corresponding valve opening prediction model is performed through the BP neural network model, specifically, a three-layer BP neural network model is constructed first, which includes an input layer, a hidden layer and an output layer. In this embodiment, the material composition information and the material loading type are used as the input of the input layer, so that the number of corresponding nodes is 2, and the output is the corresponding valve opening prediction information, so that one node is shared, for the hidden layer, the following formula is used to determine the number of hidden layer nodes,where l is the number of nodes in the hidden layer, n is the number of nodes in the input layer, m is the number of nodes in the output layer, a is a number between 1 and 10, and in this embodiment is taken as 6, so that the hidden layer shares 7 nodes. BP neural networks typically employ Sigmoid micromanipulations and linear functions as the excitation functions of the network. The sigmoid tangent function tansig is chosen herein as the excitation function of the hidden layer neurons. The predictive model selects an S-shaped logarithmic function tan sig as the excitation function of the neurons of the output layer.
S41, inputting corresponding material composition information and material loading types based on the constructed valve opening prediction model, and outputting corresponding valve opening prediction information.
S5, monitoring all warehouses in the warehouse system in real time, and acquiring the material information of all corresponding warehouses, the valve opened in real time by each warehouse and the real-time opening information of the corresponding valve;
and S6, judging whether the opening of the corresponding valve is problematic according to the valve opening prediction information and the valve real-time opening information, if so, judging that the material corresponding to the valve is misplaced, and sending an alarm signal to the central control terminal.
The step S6 comprises the following steps:
s61, calculating a corresponding similarity value based on a similarity calculation formula according to valve opening prediction information and valve real-time opening information;
the similarity calculation formula is as follows:
wherein F is a similarity value, A is valve opening prediction information, B is valve real-time opening information,is a weighting coefficient.
S62, judging whether the valve opening corresponding to the operator corresponding to the central control end has a problem during discharging according to the calculated similarity value, if so, determining the misplacement level corresponding to the discharging operation of the operator corresponding to the central control end based on a preset judging strategy; the misplacement levels include a first level, a second level, and a third level;
the preset judgment strategy is as follows:
when F is less than or equal to x, the misplacement level is the first level;
when y > F > x, the misplacement level is a second level;
when F is more than or equal to Y, the misplacement level is the third level. In this embodiment, x, y may be dynamically adjusted according to the actual situation.
S63, carrying out real-time statistics on the misplacement level corresponding to the central control terminal, and calling a corresponding alarm strategy based on a preset statistical strategy and sending the alarm strategy to the central control terminal. In this embodiment, the corresponding statistical policy is: when the number of the third level is larger than the preset first number, the operator corresponding to the central control end is judged to be an error-prone operator, and an alarm signal is immediately sent to the service end and the central control end, so that the operator is reminded and simultaneously informed to a remote monitor. When the number of misplaced grades exceeds the preset second number, an alarm signal is sent to the central control end immediately, and a corresponding training scheme is called from a database to the central control end, so that operators of the central control end can browse and learn the training scheme in time, and the level of the operators is improved. Meanwhile, no matter the corresponding misplacement level, the alarm can be given to the central control end at the first identified time, so that an operator at the central control end can finish the misplacement operation at the first time.
The embodiment also provides a misplacement detection system based on big data, and the misplacement detection method based on big data is used.
The embodiment also provides a storage medium for storing computer executable instructions, which when executed, implement the above-mentioned big data-based misplacement detection method.
The foregoing is merely exemplary of the present invention, and the specific structures and features well known in the art will be described in detail herein, so that those skilled in the art will be able to ascertain the general knowledge of the state of the art, including the application date or the priority date, and to ascertain the general knowledge of the state of the art, without the ability to apply the general experimental means before that date, so that those skilled in the art, with the benefit of this disclosure, may make various modifications of the present invention with the ability to work itself, without the ability to work out the present invention, as such typical structures or methods would be considered to be an obstacle for those skilled in the art to practice the present invention. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.

Claims (7)

1. The misplacement detection method based on big data is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring an ore transaction order, and identifying basic information of a product required in the ore transaction order and user transaction information;
s2, analyzing and judging the user transaction information according to the identified user transaction information, judging whether the ore transaction order is effective, and if the ore transaction information is effective, sending the corresponding ore transaction order to the central control terminal;
s3, determining material basic information corresponding to the product basic information according to the identified product basic information when the ore transaction order is effective as a judgment result, wherein the material basic information comprises material composition information and material loading type; the material loading types comprise mixed loading type and independent type;
s4, predicting the valve opening of a warehouse corresponding to the materials according to the material composition information and the material loading type, and generating corresponding valve opening prediction information;
s5, monitoring all warehouses in the warehouse system in real time, and acquiring the material information of all corresponding warehouses, the valve opened in real time by each warehouse and the real-time opening information of the corresponding valve;
and S6, judging whether the opening of the corresponding valve is problematic according to the valve opening prediction information and the valve real-time opening information, if so, judging that the material corresponding to the valve is misplaced, and sending an alarm signal to the central control terminal.
2. The big data based misplacement detection method as claimed in claim 1, wherein: the step S4 comprises the following steps:
s40, acquiring historical big data from a database, and constructing a valve opening prediction model; the historical big data comprise historical valve opening data and historical order data;
s41, inputting corresponding material composition information and material loading types based on the constructed valve opening prediction model, and outputting corresponding valve opening prediction information.
3. The big data based misplacement detection method as claimed in claim 2, wherein: the step S6 comprises the following steps:
s61, calculating a corresponding similarity value based on a similarity calculation formula according to valve opening prediction information and valve real-time opening information;
s62, judging whether the valve opening corresponding to the operator corresponding to the central control end has a problem during discharging according to the calculated similarity value, if so, determining the misplacement level corresponding to the discharging operation of the operator corresponding to the central control end based on a preset judging strategy; the misplacement levels include a first level, a second level, and a third level;
s63, carrying out real-time statistics on the misplacement level corresponding to the central control terminal, and calling a corresponding alarm strategy based on a preset statistical strategy and sending the alarm strategy to the central control terminal.
4. The big data based misplacement detection method as claimed in claim 3, wherein: the similarity calculation formula is as follows:
wherein F is a similarity value, A is valve opening prediction information, B is valve real-time opening information,is a weighting coefficient.
5. The big data based misplacement detection method as claimed in claim 4, wherein: the preset judgment strategy is as follows:
when F is less than or equal to x, the misplacement level is the first level;
when y > F > x, the misplacement level is a second level;
when F is more than or equal to y, the misplacement level is the third level.
6. Misplacement detection system based on big data, its characterized in that: use of the big data based misplacement detection method of any of the preceding claims 1-5.
7. A storage medium storing computer-executable instructions, characterized by: the computer executable instructions, when executed, implement the big data based misplacement detection method of any of the above claims 1-5.
CN202311788070.3A 2023-12-22 2023-12-22 Misplacement detection method, system and storage medium based on big data Pending CN117541158A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311788070.3A CN117541158A (en) 2023-12-22 2023-12-22 Misplacement detection method, system and storage medium based on big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311788070.3A CN117541158A (en) 2023-12-22 2023-12-22 Misplacement detection method, system and storage medium based on big data

Publications (1)

Publication Number Publication Date
CN117541158A true CN117541158A (en) 2024-02-09

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