CN117114576A - Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence - Google Patents
Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence Download PDFInfo
- Publication number
- CN117114576A CN117114576A CN202311121836.2A CN202311121836A CN117114576A CN 117114576 A CN117114576 A CN 117114576A CN 202311121836 A CN202311121836 A CN 202311121836A CN 117114576 A CN117114576 A CN 117114576A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- equipment
- warehouse
- data
- scheduling
- 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.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 78
- 238000007726 management method Methods 0.000 title claims abstract description 52
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 17
- 238000003860 storage Methods 0.000 claims description 33
- 238000000034 method Methods 0.000 claims description 25
- 238000004422 calculation algorithm Methods 0.000 claims description 24
- 238000012423 maintenance Methods 0.000 claims description 16
- 230000002159 abnormal effect Effects 0.000 claims description 14
- 230000005856 abnormality Effects 0.000 claims description 14
- 238000010586 diagram Methods 0.000 claims description 13
- 238000013500 data storage Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000007405 data analysis Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 8
- 238000003064 k means clustering Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 8
- 230000002035 prolonged effect Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000005286 illumination Methods 0.000 claims description 5
- 239000013618 particulate matter Substances 0.000 claims description 5
- 230000002349 favourable effect Effects 0.000 claims description 4
- 238000012806 monitoring device Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 description 14
- 238000012549 training Methods 0.000 description 12
- 230000008859 change Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 230000004913 activation Effects 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 238000013139 quantization Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- 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
- G06Q10/20—Administration of product repair or maintenance
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a warehouse intelligent scheduling monitoring management method and system based on artificial intelligence, which particularly relates to the technical field of intelligent monitoring.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to a warehouse intelligent scheduling monitoring management method and system based on artificial intelligence.
Background
With the continuous development of the logistics industry and the increasing commercial competition, the requirements of enterprises on warehouse management are also higher and higher. In traditional warehouse management, manual management and handwriting records have become increasingly difficult to meet the needs of enterprises. In fact, warehouse costs are increasing, many warehouses have implemented a three-dimensional warehouse mode to increase warehouse efficiency, and conventional warehouse management methods cannot meet the requirements of logistics management of modern enterprises, so that warehouse management systems are becoming an integral part.
However, the informatization degree of the warehouse is generally low, most of warehouse informatization methods still belong to the traditional warehouse management mode, the material information data of individual warehouses are managed by combining bar codes with a computer system, and few warehouses introduce RFID (radio frequency identification) and other technologies, so that the warehouse informatization and intelligence directions are gradually advanced.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides an intelligent warehouse scheduling monitoring management method and system based on artificial intelligence, which are used for improving the management efficiency of the warehouse by installing a sensor and monitoring equipment on warehouse equipment so as to solve the problems in the background art.
In order to achieve the above purpose, the invention provides a warehouse intelligent scheduling monitoring management method based on artificial intelligence, which specifically comprises the following steps:
101. the state and the running condition of the equipment are monitored in real time by installing a sensor and a monitoring equipment on the storage equipment;
102. the collected data is transmitted to a monitoring system through a sensor to be stored and arranged, so that the subsequent analysis and decision making are convenient;
103. by analyzing the equipment monitoring data, the relationship between the time point and the equipment parameter is displayed by using a thermodynamic diagram, so that warehouse management personnel are helped to know the working condition of the equipment;
104. based on the result of data analysis, a scheduling optimization algorithm is applied to intelligently schedule storage tasks, so that optimal task scheduling and resource utilization are realized;
105. monitoring the storage environment, setting an early warning rule, and timely sending an alarm signal when an abnormal situation occurs, and timely taking measures to avoid accidents;
106. by analyzing the running condition and maintenance history of the equipment, the maintenance requirement of the equipment is predicted, a maintenance plan is generated, the reliability of the equipment is improved, and the service life of the equipment is prolonged.
In a preferred embodiment, in step 101, the state and the running condition of the equipment are monitored in real time by installing the sensor and the monitoring equipment on the warehouse equipment, and the state and the access time of the goods are determined by using the temperature and humidity sensor monitoring, the camera monitoring and the bar code scanning mode, so that the management efficiency of the inventory is improved, and the method specifically comprises the following steps:
s1, a temperature and humidity sensor: the temperature and humidity changes in the storeroom are monitored in real time, the storage environment of the goods is ensured to meet the requirements, and the problems of damp and spoilage of the goods are prevented;
s2, a camera: the camera is arranged at a key position of the storehouse and used for monitoring the storage and taking and placing processes of cargoes, so that the cargoes are prevented from being lost;
s3, warehouse entry and warehouse exit management: and the goods are automatically identified, tracked and managed by utilizing a bar code scanning mode, the time, the position and the number of the goods are recorded, and accurate inventory management is realized.
In a preferred embodiment, in the step 102, various parameters detected by the sensor, including temperature, humidity, pressure, position information, and captured image and video data by the camera are converted into digital signals and transmitted to the data storage unit, which specifically includes the following:
s1, sensing by a sensor: a sensor in the acquisition equipment senses the monitored environmental parameters, a temperature sensor senses temperature change, a pressure sensor senses pressure change, and a camera captures images;
s2, signal conversion: the method for converting the perceived environmental parameter into the analog signal by using the analog-to-digital converter specifically comprises the following steps:
step 1, sampling: firstly, sampling an analog signal by an ADC (analog to digital converter), uniformly acquiring a series of discrete sample points in time, determining the sampling time interval and frequency by using a clock signal, fixing the value of the analog signal in a specific time interval by a sampling hold circuit, keeping the value of the analog signal unchanged until the sampling is completed, triggering the clock signal, opening a channel connected with the analog signal by a switch, recording the value of the analog signal after the switch is opened, and sampling the analog signal in the fixed time interval according to the frequency of the clock signal;
step 2, quantifying: the sampled continuous analog signal is quantized into discrete digital values, in the quantization process, the ADC maps each sample point to the nearest discrete level, and the quantization level with fixed interval is used for representing the accuracy of the digital values;
step 3, coding: the quantized discrete digital value is represented by a series of binary bits;
step 4, outputting: after sampling, quantizing and encoding, the ADC outputs a digital signal to subsequent electronic equipment for processing;
s3, digital signal processing and coding: the sampled digital signals are processed through a digital filter, so that the accuracy and the reliability of the data are ensured;
s4, data transmission and storage: the converted and coded digital signals are transmitted in a wireless mode, and are sent to a data storage unit for processing and storage by using an Ethernet technology, so that the integrity and the safety of data are ensured, and the data are prevented from being lost and tampered.
In a preferred embodiment, in the step 103, a thermodynamic diagram is used to show the relationship between a plurality of time points and equipment parameters, and the working condition and performance of the equipment are evaluated by using data comparison and reference lines, so that the efficiency and accuracy of warehouse management are improved through information sharing, and the method specifically includes the following steps:
s1, thermodynamic diagram: the horizontal axis and the vertical axis respectively represent time and equipment parameters, the color represents the numerical value of the monitoring data, and the data trend is observed through the change of the color, so that warehouse management staff is helped to know the abnormal condition of the equipment;
s2, data comparison and datum line: comparing the current equipment monitoring data with historical data and standard values, evaluating the working condition and performance of equipment, helping to find abnormal conditions and potential problems, and timely taking measures to adjust and repair;
s3, data sharing and collaboration: the equipment monitoring data is shared with other related departments, so that more comprehensive analysis and decision support are realized, and efficiency and accuracy of warehouse management are improved through cross-department cooperation and information sharing.
In a preferred embodiment, in the step 104, in the warehouse task scheduling, according to the priority, the execution time and the resource requirement index of the task, the task that is most advantageous at present is selected to schedule, and an optimal task scheduling scheme is found by a greedy algorithm, which specifically includes the following steps:
s1, initializing: sequencing all tasks according to the execution time, and selecting an initial task scheduling scheme;
s2, selecting an optimal task: selecting a currently best task from the unassigned tasks, which can be completed under defined resources and has the shortest execution time;
s3, allocating resources: assigning the selected tasks to available resources and updating the status of the associated resources;
s4, repeating the step 2 and the step 3 until all tasks are distributed;
s5, outputting a result: the obtained task scheduling scheme is the optimal task scheduling and resource utilization scheme obtained by a greedy algorithm.
In a preferred embodiment, in the step 105, the warehouse environment is monitored, including the temperature, humidity, illumination intensity, particulate matter concentration, and gas concentration inside the warehouse, the K-means clustering algorithm is used to detect the abnormality of the warehouse system, and when the equipment monitoring data exceeds the set threshold, an alarm signal is sent, which specifically includes the following contents:
s1, setting an alarm and a threshold value: according to the working requirements and standards of equipment, setting an alarm and a threshold value, wherein the concentration of carbon dioxide exceeds the safety standard, a smoke sensor can detect an abnormal signal to trigger the alarm, so that fire disaster is prevented, the power supply state in a warehouse is checked, when the power supply is interrupted to trigger the alarm, a system can automatically send the alarm to inform warehouse management personnel, and measures are timely taken;
s2, an abnormality detection algorithm: the K-means clustering algorithm is used for detecting abnormality of the warehousing system, and whether abnormal data points exist or not is determined by comparing the distances between the data points and the clusters to which the data points belong, wherein the abnormal data points comprise the following contents:
step 1, selecting an initial centroid: randomly selecting K centroids from the data set as initial clustering centers;
step 2, data point allocation: for each data point, the Euclidean distance between the data point and each centroid is calculated, and the data point is assigned to the cluster where the nearest centroid is located, and the specific calculation formula is as follows:
wherein P represents a point (x 1 ,x 2 ,....x n ) And point (y) 1 ,y 2 ,...,y n ) Euclidean distance between them; the |X| is the point (X 1 ,x 2 ,....x n ) Euclidean distance to origin;
step 3, updating mass centers: for each cluster, the average of all its data points is calculated to obtain a new centroid position, cluster C contains n data points (x 1 ,y 1 ),(x 2 ,y 2 ),..,(x n ,y n ) The specific calculation formula is as follows:
where avg_x is the average of the longitudes of the data points in cluster C and avg_y is the average of the latitudes of the data points in cluster C;
step 4, repeating step S302 and step S303 until a stopping criterion is met, wherein the stopping criterion comprises the following contents:
1) The maximum iteration times are reached;
2) The allocation of clusters is no longer changed and the data points no longer switch clusters;
3) The centroid variation of the cluster is less than a certain threshold;
step 5, outputting a result: k clusters are obtained, each cluster containing a set of data points, and each data point being associated with a centroid.
In a preferred embodiment, in step 106, by analyzing the device monitoring data, a fault prediction model is established, and a warehouse manager predicts the fault of the device in advance, and takes corresponding measures to improve the reliability of the device and prolong the service life of the device, which specifically includes the following steps:
s1, data division: dividing the data into a training set and a testing set, using a part of the data as the training set for model training, and using the rest of the data as the testing set for evaluating the performance of the model;
s2, training a model: selecting a machine learning algorithm of a perception machine, constructing a model and training, wherein the training process specifically comprises the following steps:
step 1, a perceptron receives an input vector x, performs linear weighted summation on the input vector x and a weight vector w, judges an output result through an activation function, and a linear weighted summation formula is specifically as follows:
S＝w 1 ×x 1 +w 2 ×x 2 +...+w n ×x n
wherein S represents the result of summation, w 1 ,w 2 ,...,w n Representing weights, x 1 ,x 2 ,...,x n Representing the corresponding numerical value.
A step function is a commonly used activation function that maps an input value to one of two discrete output values, and is defined as follows:
the step function produces a sudden change when the input reaches a critical point, from 0 to 1 and from 1 to 0.
Step 2, initializing a weight vector w and a bias b, and calculating a predicted output value for each sample (x, y), wherein the specific formula is as follows:
y_hat＝sign(w·x+b)
w＝w+η×y×x
b＝b+η×y
wherein x represents an input feature vector, y represents a label (1 or-1), and eta represents a learning rate; predicting correct y_hat=y, continuing the next sample, predicting incorrect y_hat is not equal to y, updating weight vector and bias, and continuing to iteratively update the current sample until the prediction is correct;
s3, model evaluation: and evaluating the trained model by using a test set, calculating the recall rate of key indexes, evaluating the prediction performance and stability of the model, wherein the method represents that the model is successfully predicted as the ratio of the number of positive samples to the number of samples of all the positive samples in practice, and the specific calculation formula is as follows:
wherein Z represents the recall rate, TP represents the true instance, and is correctly predicted as the number of positive samples; FN represents a false negative example, is erroneously predicted as the number of negative samples, and the recall rate ranges from 0 to 1.
S4, deployment and monitoring: the trained fault prediction model is deployed into an actual environment, data are monitored in real time, prediction is made, the accuracy of the model is checked regularly, the model is adjusted in time, parts are replaced, and downtime and production interruption are avoided.
The invention has the technical effects and advantages that:
according to the invention, the state and the running condition of the equipment are monitored in real time by installing the sensor and the monitoring equipment on the warehousing equipment, the inventory management efficiency is improved, various parameters detected by the sensor, the image and video data captured by the camera are converted into digital signals and transmitted to the data storage unit, the relationship between a plurality of time points and the equipment parameters is displayed by using a thermodynamic diagram, the warehousing management efficiency and accuracy are improved, in the warehousing task scheduling, an optimal task scheduling scheme is searched by using a greedy algorithm, the abnormality detection of a warehousing system is carried out by using a K-means clustering algorithm, when the equipment monitoring data exceeds a set threshold value, an alarm signal is sent, a fault prediction model is established, a warehousing manager predicts the faults of the equipment in advance, and corresponding measures are taken to improve the reliability of the equipment and prolong the service life of the equipment.
Drawings
FIG. 1 is a flow chart of the system of the present invention.
Fig. 2 is a block diagram of the system architecture of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
Example 1
The embodiment provides a warehouse intelligent scheduling monitoring management method based on artificial intelligence as shown in fig. 1, which specifically comprises the following steps:
101. the state and the running condition of the equipment are monitored in real time by installing a sensor and a monitoring equipment on the storage equipment;
102. the collected data is transmitted to a monitoring system through a sensor to be stored and arranged, so that the subsequent analysis and decision making are convenient;
103. by analyzing the equipment monitoring data, the relationship between the time point and the equipment parameter is displayed by using a thermodynamic diagram, so that warehouse management personnel are helped to know the working condition of the equipment;
104. based on the result of data analysis, a scheduling optimization algorithm is applied to intelligently schedule storage tasks, so that optimal task scheduling and resource utilization are realized;
105. monitoring the storage environment, setting an early warning rule, and timely sending an alarm signal when an abnormal situation occurs, and timely taking measures to avoid accidents;
106. by analyzing the running condition and maintenance history of the equipment, the maintenance requirement of the equipment is predicted, a maintenance plan is generated, the reliability of the equipment is improved, and the service life of the equipment is prolonged.
As shown in fig. 2, the embodiment provides an intelligent warehouse dispatching monitoring management system based on artificial intelligence, which specifically comprises an intelligent monitoring module, a data storage module, a data analysis module, a dispatching optimization module, an abnormality detection module and a maintenance management module;
and the intelligent monitoring module: by installing the sensor and the monitoring equipment on the storage equipment, the state and the running condition of the equipment are monitored in real time, the state and the access time of goods are determined by utilizing the temperature and humidity sensor monitoring, the camera monitoring and the bar code scanning mode, and the management efficiency of the storage is improved.
And a data storage module: various parameters detected by the sensor, including temperature, humidity, pressure, position information, and captured image and video data by the camera, are converted into digital signals and transmitted to the data storage unit.
And a data analysis module: and the thermodynamic diagram is used for showing the relation between a plurality of time points and equipment parameters, and the working condition and performance of the equipment are evaluated by utilizing data comparison and a datum line, so that the efficiency and the accuracy of warehouse management are improved through information sharing.
And a scheduling optimization module: in warehouse task scheduling, selecting the currently most favorable task to schedule according to the priority, execution time and resource demand index of the task, and searching an optimal task scheduling scheme through a greedy algorithm.
An abnormality detection module: monitoring the warehouse environment, including temperature, humidity, illumination intensity, particulate matter concentration and gas concentration in the warehouse, using a K-means clustering algorithm to detect abnormality of the warehouse system, and sending an alarm signal when the equipment monitoring data exceeds a set threshold value.
And a maintenance management module: by analyzing the equipment monitoring data, a fault prediction model is established, storage management personnel predict faults of equipment in advance, corresponding measures are taken, reliability of the equipment is improved, and service life of the equipment is prolonged.
101. The state and the running condition of the equipment are monitored in real time by installing a sensor and a monitoring equipment on the storage equipment;
further, by installing the sensor and the monitoring device on the storage device, the state and the running condition of the real-time monitoring device are monitored by using the temperature and humidity sensor, the camera and the bar code scanning mode, the state and the access time of the goods are determined, the management efficiency of the storage is improved, and the method specifically comprises the following steps:
s1, a temperature and humidity sensor: the temperature and humidity changes in the storeroom are monitored in real time, the storage environment of the goods is ensured to meet the requirements, and the problems of damp and spoilage of the goods are prevented;
s2, a camera: the camera is arranged at a key position of the storehouse and used for monitoring the storage and taking and placing processes of cargoes, so that the cargoes are prevented from being lost;
s3, warehouse entry and warehouse exit management: and the goods are automatically identified, tracked and managed by utilizing a bar code scanning mode, the time, the position and the number of the goods are recorded, and accurate inventory management is realized.
102. The collected data is transmitted to a monitoring system through a sensor to be stored and arranged, so that the subsequent analysis and decision making are convenient;
further, various parameters detected by the sensor, including temperature, humidity, pressure, position information, and captured image and video data by the camera, are converted into digital signals and transmitted to the data storage unit, and specifically include the following contents:
s1, sensing by a sensor: a sensor in the acquisition equipment senses the monitored environmental parameters, a temperature sensor senses temperature change, a pressure sensor senses pressure change, and a camera captures images;
s2, signal conversion: the method for converting the perceived environmental parameter into the analog signal by using the analog-to-digital converter specifically comprises the following steps:
step 1, sampling: firstly, sampling an analog signal by an ADC (analog to digital converter), uniformly acquiring a series of discrete sample points in time, determining the sampling time interval and frequency by using a clock signal, fixing the value of the analog signal in a specific time interval by a sampling hold circuit, keeping the value of the analog signal unchanged until the sampling is completed, triggering the clock signal, opening a channel connected with the analog signal by a switch, recording the value of the analog signal after the switch is opened, and sampling the analog signal in the fixed time interval according to the frequency of the clock signal;
step 2, quantifying: the sampled continuous analog signal is quantized into discrete digital values, in the quantization process, the ADC maps each sample point to the nearest discrete level, and the quantization level with fixed interval is used for representing the accuracy of the digital values;
step 3, coding: the quantized discrete digital value is represented by a series of binary bits;
step 4, outputting: after sampling, quantizing and encoding, the ADC outputs a digital signal to subsequent electronic equipment for processing;
s3, digital signal processing and coding: the sampled digital signals are processed through a digital filter, so that the accuracy and the reliability of the data are ensured;
s4, data transmission and storage: the converted and coded digital signals are transmitted in a wireless mode, and are sent to a data storage unit for processing and storage by using an Ethernet technology, so that the integrity and the safety of data are ensured, and the data are prevented from being lost and tampered.
103. By analyzing the equipment monitoring data, the relationship between the time point and the equipment parameter is displayed by using a thermodynamic diagram, so that warehouse management personnel are helped to know the working condition of the equipment;
furthermore, the thermodynamic diagram is used for showing the relation between a plurality of time points and equipment parameters, the working condition and performance of the equipment are evaluated by utilizing data comparison and a datum line, and the efficiency and accuracy of warehouse management are improved through information sharing, and the method specifically comprises the following steps:
s1, thermodynamic diagram: the horizontal axis and the vertical axis respectively represent time and equipment parameters, the color represents the numerical value of the monitoring data, and the data trend is observed through the change of the color, so that warehouse management staff is helped to know the abnormal condition of the equipment;
s2, data comparison and datum line: comparing the current equipment monitoring data with historical data and standard values, evaluating the working condition and performance of equipment, helping to find abnormal conditions and potential problems, and timely taking measures to adjust and repair;
s3, data sharing and collaboration: the equipment monitoring data is shared with other related departments, so that more comprehensive analysis and decision support are realized, and efficiency and accuracy of warehouse management are improved through cross-department cooperation and information sharing.
104. Based on the result of data analysis, a scheduling optimization algorithm is applied to intelligently schedule storage tasks, so that optimal task scheduling and resource utilization are realized;
further, in warehouse task scheduling, according to the priority, execution time and resource requirement index of the task, selecting the current most favorable task to schedule, and searching an optimal task scheduling scheme through a greedy algorithm, wherein the method specifically comprises the following steps:
s1, initializing: sequencing all tasks according to the execution time, and selecting an initial task scheduling scheme;
s2, selecting an optimal task: selecting a currently best task from the unassigned tasks, which can be completed under defined resources and has the shortest execution time;
s3, allocating resources: assigning the selected tasks to available resources and updating the status of the associated resources;
s4, repeating the step 2 and the step 3 until all tasks are distributed;
s5, outputting a result: the obtained task scheduling scheme is the optimal task scheduling and resource utilization scheme obtained by a greedy algorithm.
105. Monitoring the storage environment, setting an early warning rule, and timely sending an alarm signal when an abnormal situation occurs, and timely taking measures to avoid accidents;
further, monitoring the warehouse environment, including temperature, humidity, illumination intensity, particulate matter concentration and gas concentration in the warehouse, using a K-means clustering algorithm to detect abnormality of the warehouse system, and sending an alarm signal when the equipment monitoring data exceeds a set threshold value, wherein the method specifically comprises the following steps:
s1, setting an alarm and a threshold value: according to the working requirements and standards of equipment, setting an alarm and a threshold value, wherein the concentration of carbon dioxide exceeds the safety standard, a smoke sensor can detect an abnormal signal to trigger the alarm, so that fire disaster is prevented, the power supply state in a warehouse is checked, when the power supply is interrupted to trigger the alarm, a system can automatically send the alarm to inform warehouse management personnel, and measures are timely taken;
s2, an abnormality detection algorithm: the K-means clustering algorithm is used for detecting abnormality of the warehousing system, and whether abnormal data points exist or not is determined by comparing the distances between the data points and the clusters to which the data points belong, wherein the abnormal data points comprise the following contents:
step 1, selecting an initial centroid: randomly selecting K centroids from the data set as initial clustering centers;
step 2, data point allocation: for each data point, the Euclidean distance between the data point and each centroid is calculated, and the data point is assigned to the cluster where the nearest centroid is located, and the specific calculation formula is as follows:
wherein P represents a point (x 1 ,x 2 ,....x n ) And point (y) 1 ,y 2 ,...,y n ) Euclidean distance between them; the |X| is the point (X 1 ,x 2 ,....x n ) Euclidean distance to origin;
step 3, updating mass centers: for each cluster, the average of all its data points is calculated to obtain a new centroid position, cluster C contains n data points (x 1 ,y 1 ),(x 2 ,y 2 ),..,(x n ,y n ) The specific calculation formula is as follows:
where avg_x is the average of the longitudes of the data points in cluster C and avg_y is the average of the latitudes of the data points in cluster C;
step 4, repeating step S302 and step S303 until a stopping criterion is met, wherein the stopping criterion comprises the following contents:
1) The maximum iteration times are reached;
2) The allocation of clusters is no longer changed and the data points no longer switch clusters;
3) The centroid variation of the cluster is less than a certain threshold;
step 5, outputting a result: k clusters are obtained, each cluster containing a set of data points, and each data point being associated with a centroid.
106. By analyzing the running condition and maintenance history of the equipment, the maintenance requirement of the equipment is predicted, a maintenance plan is generated, the reliability of the equipment is improved, and the service life of the equipment is prolonged.
Further, by analyzing the equipment monitoring data, a fault prediction model is established, storage management personnel predict faults of the equipment in advance, corresponding measures are taken, the reliability of the equipment is improved, the service life of the equipment is prolonged, and the method specifically comprises the following steps:
s1, data division: dividing the data into a training set and a testing set, using a part of the data as the training set for model training, and using the rest of the data as the testing set for evaluating the performance of the model;
s2, training a model: selecting a machine learning algorithm of a perception machine, constructing a model and training, wherein the training process specifically comprises the following steps:
step 1, a perceptron receives an input vector x, performs linear weighted summation on the input vector x and a weight vector w, judges an output result through an activation function, and a linear weighted summation formula is specifically as follows:
S＝w 1 ×x 1 +w 2 ×x 2 +...+w n ×x n
wherein S represents the result of summation, w 1 ,w 2 ,...,w n Representing weights, x 1 ,x 2 ,...,x n Representing the corresponding numerical value.
A step function is a commonly used activation function that maps an input value to one of two discrete output values, and is defined as follows:
the step function produces a sudden change when the input reaches a critical point, from 0 to 1 and from 1 to 0.
Step 2, initializing a weight vector w and a bias b, and calculating a predicted output value for each sample (x, y), wherein the specific formula is as follows:
y_hat＝sign(w·x+b)
w＝w+η×y×x
b＝b+η×y
wherein x represents an input feature vector, y represents a label (1 or-1), and eta represents a learning rate; predicting correct y_hat=y, continuing the next sample, predicting incorrect y_hat is not equal to y, updating weight vector and bias, and continuing to iteratively update the current sample until the prediction is correct;
s3, model evaluation: and evaluating the trained model by using a test set, calculating the recall rate of key indexes, evaluating the prediction performance and stability of the model, wherein the method represents that the model is successfully predicted as the ratio of the number of positive samples to the number of samples of all the positive samples in practice, and the specific calculation formula is as follows:
wherein Z represents the recall rate, TP represents the true instance, and is correctly predicted as the number of positive samples; FN represents a false negative example, is erroneously predicted as the number of negative samples, and the recall rate ranges from 0 to 1.
S4, deployment and monitoring: the trained fault prediction model is deployed into an actual environment, data are monitored in real time, prediction is made, the accuracy of the model is checked regularly, the model is adjusted in time, parts are replaced, and downtime and production interruption are avoided.
The formula in the invention is a formula which is obtained by removing dimension and taking the numerical calculation, and is closest to the actual situation by acquiring a large amount of data and performing software simulation, and the preset proportionality coefficient in the formula is set by a person skilled in the art according to the actual situation or is obtained by simulating the large amount of data.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. An intelligent warehouse dispatching monitoring management method based on artificial intelligence is characterized in that: the method comprises the following steps:
101. the state and the running condition of the equipment are monitored in real time by installing a sensor and a monitoring equipment on the storage equipment;
102. the collected data is transmitted to a monitoring system through a sensor to be stored and arranged, so that the subsequent analysis and decision making are convenient;
103. by analyzing the equipment monitoring data, the relationship between the time point and the equipment parameter is displayed by using a thermodynamic diagram, so that warehouse management personnel are helped to know the working condition of the equipment;
104. based on the result of data analysis, a scheduling optimization algorithm is applied to intelligently schedule storage tasks, so that optimal task scheduling and resource utilization are realized;
105. monitoring the storage environment, setting an early warning rule, and timely sending an alarm signal when an abnormal situation occurs, and timely taking measures to avoid accidents;
106. by analyzing the running condition and maintenance history of the equipment, the maintenance requirement of the equipment is predicted, a maintenance plan is generated, the reliability of the equipment is improved, and the service life of the equipment is prolonged.
2. The warehouse intelligent scheduling monitoring and managing method based on artificial intelligence as set forth in claim 1, wherein: in step 101, the state and the access time of the goods are determined by installing a sensor and a monitoring device on the storage device, monitoring the state and the running condition of the device in real time, and utilizing the temperature and humidity sensor monitoring, the camera monitoring and the bar code scanning mode, so that the management efficiency of the storage is improved.
3. The warehouse intelligent scheduling monitoring and managing method based on artificial intelligence as set forth in claim 1, wherein: in step 102, various parameters detected by the sensor, including temperature, humidity, pressure, position information, and captured image and video data by the camera, are converted into digital signals and transmitted to the data storage unit.
4. The warehouse intelligent scheduling monitoring and managing method based on artificial intelligence as set forth in claim 1, wherein: in step 103, the thermodynamic diagram is used to show the relationship between a plurality of time points and equipment parameters, and the working condition and performance of the equipment are evaluated by using data comparison and reference lines, so that the efficiency and accuracy of warehouse management are improved through information sharing.
5. The warehouse intelligent scheduling monitoring and managing method based on artificial intelligence as set forth in claim 1, wherein: in step 104, in the warehouse task scheduling, the current most favorable task is selected for scheduling according to the priority, execution time and resource requirement index of the task, and an optimal task scheduling scheme is found through a greedy algorithm.
6. The warehouse intelligent scheduling monitoring and managing method based on artificial intelligence as set forth in claim 1, wherein: in step 105, monitoring the warehouse environment, including temperature, humidity, illumination intensity, particulate matter concentration and gas concentration in the warehouse, performing abnormality detection of the warehouse system by using a K-means clustering algorithm, and sending an alarm signal when the equipment monitoring data exceeds a set threshold value, wherein the specific calculation formula is as follows:
wherein P represents a point (x 1 ,x 2 ,....x n ) And point (y) 1 ,y 2 ,...,y n ) The euclidean distance between them, |x| is the point (X 1 ,x 2 ,....x n ) Euclidean distance to origin.
7. The warehouse intelligent scheduling monitoring and managing method based on artificial intelligence as set forth in claim 1, wherein: in step 106, by analyzing the equipment monitoring data, a fault prediction model is established, the warehouse manager predicts the faults of the equipment in advance, and adopts corresponding measures to improve the reliability of the equipment and prolong the service life of the equipment, wherein the specific calculation formula is as follows:
S＝w 1 ×x 1 +w 2 ×x 2 +...+w n ×x n
wherein S represents the result of summation, w 1 ,w 2 ,...,w n Representing weights, x 1 ,x 2 ,...,x n Representing the corresponding numerical value.
8. The warehouse intelligent scheduling monitoring management system based on artificial intelligence is applied to the warehouse intelligent scheduling monitoring management method based on artificial intelligence as set forth in claims 1-7, and is characterized in that: the system comprises an intelligent monitoring module, a data storage module, a data analysis module, a scheduling optimization module, an abnormality detection module and a maintenance management module;
and the intelligent monitoring module: the state and the access time of goods are determined by installing a sensor and a monitoring device on the storage device, monitoring the state and the running condition of the device in real time and utilizing a temperature and humidity sensor for monitoring, a camera for monitoring and a bar code scanning mode, so that the management efficiency of the storage is improved;
and a data storage module: converting various parameters detected by the sensor, including temperature, humidity, pressure and position information, and captured image and video data by the camera into digital signals and transmitting the digital signals to the data storage unit;
and a data analysis module: the thermodynamic diagram is used for showing the relation between a plurality of time points and equipment parameters, the working condition and performance of the equipment are evaluated by utilizing data comparison and a datum line, and the efficiency and the accuracy of warehouse management are improved through information sharing;
and a scheduling optimization module: in warehouse task scheduling, selecting the currently most favorable task to schedule according to the priority, execution time and resource demand index of the task, and searching an optimal task scheduling scheme through a greedy algorithm;
an abnormality detection module: monitoring the storage environment, including temperature, humidity, illumination intensity, particulate matter concentration and gas concentration in the warehouse, using a K-means clustering algorithm to detect abnormality of the storage system, and sending an alarm signal when the equipment monitoring data exceeds a set threshold value;
and a maintenance management module: by analyzing the equipment monitoring data, a fault prediction model is established, storage management personnel predict faults of equipment in advance, corresponding measures are taken, reliability of the equipment is improved, and service life of the equipment is prolonged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311121836.2A CN117114576A (en) | 2023-08-31 | 2023-08-31 | Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311121836.2A CN117114576A (en) | 2023-08-31 | 2023-08-31 | Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117114576A true CN117114576A (en) | 2023-11-24 |
Family
ID=88798075
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311121836.2A Pending CN117114576A (en) | 2023-08-31 | 2023-08-31 | Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117114576A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117349780A (en) * | 2023-12-05 | 2024-01-05 | 凌雄技术(深圳)有限公司 | Warehouse data intelligent identification management and control system and method based on data analysis |
| CN117371762A (en) * | 2023-12-04 | 2024-01-09 | 中兴耀维科技江苏有限公司 | Remote deployment management platform based on environment monitoring |
| CN117407574A (en) * | 2023-12-14 | 2024-01-16 | 江西中汇云链供应链管理有限公司 | Multifunctional state display system for warehouse |
| CN117592865A (en) * | 2023-12-21 | 2024-02-23 | 中国人民解放军军事科学院系统工程研究院 | Equipment spare part quality state prediction method and device |
| CN117709855A (en) * | 2024-01-05 | 2024-03-15 | 东莞益海嘉里粮油食品工业有限公司 | Intelligent storage goods shelf and control system |
| CN117726144A (en) * | 2024-02-07 | 2024-03-19 | 青岛国彩印刷股份有限公司 | Intelligent digital printing management system and method based on data processing |
| CN118278834A (en) * | 2024-03-06 | 2024-07-02 | 济宁政亚物流信息有限公司 | Logistics optimization system based on Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111611991A (en) * | 2020-05-23 | 2020-09-01 | 中南大学 | Fault processing method and device, electronic equipment and computer readable storage medium |
| CN111915094A (en) * | 2020-08-12 | 2020-11-10 | 王后磊 | Warehousing method and system based on artificial intelligence |
| CN116233198A (en) * | 2023-04-23 | 2023-06-06 | 深圳市科网通科技发展有限公司 | Data processing method and system based on intelligent building networking |
| CN116629741A (en) * | 2023-05-16 | 2023-08-22 | 济南明泉数字商务有限公司 | Multi-mode interaction-based bulk product intelligent warehouse management method and system |
-
2023
- 2023-08-31 CN CN202311121836.2A patent/CN117114576A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111611991A (en) * | 2020-05-23 | 2020-09-01 | 中南大学 | Fault processing method and device, electronic equipment and computer readable storage medium |
| CN111915094A (en) * | 2020-08-12 | 2020-11-10 | 王后磊 | Warehousing method and system based on artificial intelligence |
| CN116233198A (en) * | 2023-04-23 | 2023-06-06 | 深圳市科网通科技发展有限公司 | Data processing method and system based on intelligent building networking |
| CN116629741A (en) * | 2023-05-16 | 2023-08-22 | 济南明泉数字商务有限公司 | Multi-mode interaction-based bulk product intelligent warehouse management method and system |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117371762A (en) * | 2023-12-04 | 2024-01-09 | 中兴耀维科技江苏有限公司 | Remote deployment management platform based on environment monitoring |
| CN117371762B (en) * | 2023-12-04 | 2024-02-20 | 中兴耀维科技江苏有限公司 | Remote deployment management platform based on environment monitoring |
| CN117349780A (en) * | 2023-12-05 | 2024-01-05 | 凌雄技术(深圳)有限公司 | Warehouse data intelligent identification management and control system and method based on data analysis |
| CN117349780B (en) * | 2023-12-05 | 2024-02-23 | 凌雄技术(深圳)有限公司 | Warehouse data intelligent identification management and control system and method based on data analysis |
| CN117407574A (en) * | 2023-12-14 | 2024-01-16 | 江西中汇云链供应链管理有限公司 | Multifunctional state display system for warehouse |
| CN117592865A (en) * | 2023-12-21 | 2024-02-23 | 中国人民解放军军事科学院系统工程研究院 | Equipment spare part quality state prediction method and device |
| CN117592865B (en) * | 2023-12-21 | 2024-04-05 | 中国人民解放军军事科学院系统工程研究院 | Equipment spare part quality state prediction method and device |
| CN117709855A (en) * | 2024-01-05 | 2024-03-15 | 东莞益海嘉里粮油食品工业有限公司 | Intelligent storage goods shelf and control system |
| CN117726144A (en) * | 2024-02-07 | 2024-03-19 | 青岛国彩印刷股份有限公司 | Intelligent digital printing management system and method based on data processing |
| CN117726144B (en) * | 2024-02-07 | 2024-05-07 | 青岛国彩印刷股份有限公司 | Intelligent digital printing management system and method based on data processing |
| CN118278834A (en) * | 2024-03-06 | 2024-07-02 | 济宁政亚物流信息有限公司 | Logistics optimization system based on Internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117114576A (en) | Warehouse intelligent scheduling monitoring management method and system based on artificial intelligence | |
| CN115578015B (en) | Sewage treatment whole process supervision method, system and storage medium based on Internet of things | |
| CN111061620B (en) | Intelligent detection method and detection system for server abnormity of mixed strategy | |
| CN111486893A (en) | Bridge structure health monitoring and early warning system and early warning method | |
| CN110275814A (en) | A kind of monitoring method and device of operation system | |
| US20220414526A1 (en) | Intelligent fault detection system | |
| CN104200288A (en) | Equipment fault prediction method based on factor-event correlation recognition | |
| CN117909784A (en) | Equipment fault detection analysis method and device based on artificial intelligence | |
| CN117972580B (en) | Remote monitoring method and system for aviation obstruction lights | |
| CN108921305B (en) | Component life period monitoring method | |
| CN108629310B (en) | Engineering management supervision method and device | |
| CN113468022B (en) | Automatic operation and maintenance method for centralized monitoring of products | |
| CN117764562B (en) | Intelligent access control equipment management method and system based on Internet of things | |
| CN115914267A (en) | Basin water ecological safety monitoring and early warning system | |
| KR101960755B1 (en) | Method and apparatus of generating unacquired power data | |
| CN117688464B (en) | Hidden danger analysis method and system based on multi-source sensor data | |
| CN116994405B (en) | Intelligent disaster early warning method and system | |
| CN117375147B (en) | Safety monitoring early warning and operation management method and system for energy storage power station | |
| US20240152132A1 (en) | Information processing apparatus, information processing method, and recording medium | |
| CN113835387A (en) | Operation and maintenance management method, system and medium | |
| CN117494009A (en) | Electrical equipment state evaluation method based on insulating material pyrolysis analysis and cloud platform | |
| CN116778688A (en) | Machine room alarm event processing method, device, equipment and storage medium | |
| CN116714469A (en) | Charging pile health monitoring method, device, terminal and storage medium | |
| CN112052714A (en) | Data-driven machine learning for modeling aircraft sensors | |
| CN110517731A (en) | Genetic test quality monitoring data processing method and 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20231124 |