CN108873697A - A kind of storage method and system based on artificial intelligence - Google Patents
A kind of storage method and system based on artificial intelligence Download PDFInfo
- Publication number
- CN108873697A CN108873697A CN201810655109.7A CN201810655109A CN108873697A CN 108873697 A CN108873697 A CN 108873697A CN 201810655109 A CN201810655109 A CN 201810655109A CN 108873697 A CN108873697 A CN 108873697A
- Authority
- CN
- China
- Prior art keywords
- storage
- equipment
- status data
- data
- warehouse
- 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
Classifications
-
- 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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- 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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/029—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks and expert systems
Landscapes
- Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Warehouses Or Storage Devices (AREA)
Abstract
The present invention relates to storage fields, are related to a kind of storage method and system based on artificial intelligence.In an embodiment of the present invention, the intelligent storage model is by first constructing deep neural network machine learning model, then expert's operation data of the device status data by periodically uploading current storage status data, O&M equipment of storing in a warehouse and corresponding storage O&M equipment is trained as training dataset and established.By collecting storage status data and device status data, and it is inputted intelligent storage model；And according to the intelligent storage model, the concrete operations instruction of the required storage O&M equipment called and O&M equipment of storing in a warehouse is calculated, according to calculated result, the corresponding storage O&M equipment of control completes the concrete operations instruction corresponding to it.In this way, just solving the problems, such as that existing storage O&M method efficiency is poor, warehouse cost is high.
Description
Technical field
The present invention relates to storage fields, in particular to a kind of storage method and system based on artificial intelligence.
Background technique
Warehouse can be divided into free warehouse and special warehouse, and free warehouse refers to room temperature keeping, gravity-flow ventilation, without special function
The warehouse of energy, and special warehouse is the warehouse for being assigned with the facilities such as refrigeration, heat preservation, drying, is mainly used for storing such as grain, vegetable
Fruit, meat, medicinal material etc. are perishable or need the article of other particular memory conditions.
Currently, warehouseman needs each in periodically detection warehouse in the daily management of all kinds of special warehouses
Item index（Such as temperature, humidity, gas concentration, number of pest）, and corresponding establishment is operated according to abnormal index（As air-conditioning,
Blower, storehouse window, gassing machine etc.）, to ensure the long-term storage of article.
In the long-term operation management in warehouse, that there are warehouseman's levels is low, professional is at high cost, equipment operation effect
Rate it is low and it is abnormal there is a situation where when take measures not in time.Such as：Scene 1：Warehouse item is stored by prolonged, by
In the influence of a variety of uncontrollable factors, article is it is possible that dampness, infested, mildew, rotten, even burning, explosion, Toxic
Situations such as matter leaks.Common O&M method is：Inspection and record are periodically carried out to warehouse by warehouseman, work as discovery
When abnormal data index, administrator rule of thumb judges whether to need to enable equipment, enables which equipment, enables much power,
Enable how long etc., and then operate corresponding installations and facilities to improve abnormal conditions.But this mode has the following defects：It is first
First, stores keeping practitioner is horizontal universal lower, frequently result in abnormal problem it is more apparent when just discovery, when taking corresponding measure
The loss having resulted in can not be made up, cannot be asked in advance according to what abnormal data prediction will occur as storage expert
Topic, and then take preventive measures.Secondly, the cost for employing storage expert to carry out analysis maintenance for a long time comes most of warehouse
Say it is all unacceptable, and the experience for the expert that stores in a warehouse also has limitation, the actual conditions of different reservoir areas are different, different
Weather and season under management method be also different, same set of management method can not be suitable for all warehouses.
Scene 2：When abnormal index occurs in warehouse, warehouseman needs to operate corresponding installations and facilities.Often
The operating method seen is：Warehouseman comes into the manually opened equipment in warehouse, and equipment is turned off manually after restoring normal in index again.But
This mode has the following defects：Firstly, manual control equipment can only be gone to by manpower each warehouse to each equipment one by one into
Row switch operation, consumes a large amount of manpowers and time.Secondly because equipment needs to operate one by one, some equipment can not be opened in time
It opens and closes, on the one hand cause the waste of the energy, on the one hand can not save the article in abnormal warehouse in time, it is unnecessary to cause
Loss.
Again, even for having been realized in for the warehouse that storage facilities remotely controls, occur from abnormal index to storehouse
Library administrator notes abnormalities, and then analyzes data, then carries out remote equipment and operate this process also consuming many times, equally
It is that will cause unnecessary loss.
Summary of the invention
The purpose of the present invention is to provide a kind of storage methods based on artificial intelligence, it is intended to solve existing storage O&M
The problem that method efficiency is poor, warehouse cost is high.
The invention is realized in this way a kind of storage method based on artificial intelligence, the described method comprises the following steps：
Real-time collecting is stored in a warehouse the storage status data of sensor and the device status data for O&M equipment of storing in a warehouse, and by the storage
Status data and device status data input intelligent storage model；
According to the intelligent storage model, the concrete operations of the storage O&M equipment and O&M equipment of storing in a warehouse called needed for calculating
Instruction；
According to calculated result, the corresponding storage O&M equipment of control completes the concrete operations instruction corresponding to it,
The intelligent storage model is worked as by first constructing deep neural network machine learning model, then by periodically uploading
Expert's operation data of preceding storage status data and corresponding storage O&M equipment is built as training dataset to train
Vertical.
Further, in the storage status data of real-time collecting storage sensor and the equipment shape for O&M equipment of storing in a warehouse
State data, and before further include step by the step of storage status data and device status data input intelligent storage model
Suddenly：
According to the acquisition demand of storage status data, corresponding storage sensor is laid in warehouse；
According to the demand for control of storage O&M equipment, the storage O&M equipment of Electronic control formula is laid；
Things-internet gateway is set up, the gateway is connected by communication interface with storage sensor and storage O&M equipment, so as to
It is collected in the device status data of storage status data and O&M equipment of storing in a warehouse.
Further, the foundation of the intelligent storage model includes the following steps：
Periodically the storage status data of the current storage sensor of acquisition, the device status data for O&M equipment of storing in a warehouse and right
The expert's operation data for the storage O&M equipment answered is as training data, by training data storage to server database；
By the storage status data stored in server database, the expert of device status data and corresponding storage O&M equipment
The relationship mapped one by one is established in operation data and intelligent storage model as the feature data types of input layer；
Deep learning classifier is constructed, is connected input layer and output layer by multilayer hidden layer neuron, is arranged each hidden
The neuronal quantity for hiding layer, is arranged the number of the target type of output layer, and initialize the linear relationship coefficient of each neuron
With bias value；
Data set is docked with classifier, using storage status data, device status data as input value, by equipment operation data
As target output type, the frequency of training of every group of data is set.
Further, in the storage status data of real-time collecting storage sensor and the equipment shape for O&M equipment of storing in a warehouse
State data, and before further include step by the step of storage status data and device status data input intelligent storage model
Suddenly：
Intelligent storage model is loaded into memory, is quickly calculated with realizing.
Another object of the present invention is to provide a kind of warehousing system based on artificial intelligence, the system comprises：
Data collection module, for the storage status data of real-time collecting storage sensor and the equipment state for O&M equipment of storing in a warehouse
Data, and the storage status data and device status data are inputted into intelligent storage model；
Intelligence computation module, storage O&M equipment and storage for being called needed for according to the intelligent storage model, calculating
The concrete operations of O&M equipment instruct；
Intelligent control module, for according to calculated result, the corresponding storage O&M equipment of control to complete the specific behaviour corresponding to it
It instructs,
The intelligent storage model is worked as by first constructing deep neural network machine learning model, then by periodically uploading
Expert's operation data of preceding storage status data and corresponding storage O&M equipment is built as training dataset to train
Vertical.
Further, the system comprises：
Training data collects module, for periodically acquiring storage status data, the storage O&M of current storage sensor
Expert's operation data of the device status data of equipment and corresponding storage O&M equipment is as training data, by the trained number
Server database is arrived according to storage；
Data respective modules, storage status data, device status data for will be stored in server database and corresponding
Feature data types in the expert's operation data and intelligent storage model of O&M equipment of storing in a warehouse as input layer are established
The relationship mapped one by one；
Input layer is connect by building module for constructing deep learning classifier by multilayer hidden layer neuron with output layer
Get up, the neuronal quantity of each hidden layer is set, the number of the target type of output layer is set, and initializes each neuron
Linear relationship coefficient and bias value；
Setup module, for data set to be docked with classifier, the status data that will store in a warehouse, device status data as input value,
Using equipment operation data as target output type, the frequency of training of every group of data is set.
Further, the system comprises：
Loading module is quickly calculated for intelligent storage model to be loaded into memory with realizing.
In an embodiment of the present invention, which is by first constructing deep neural network machine learning mould
Type, then the device status data by periodically uploading current storage status data, O&M equipment of storing in a warehouse and corresponding
Expert's operation data of storage O&M equipment, which is trained as training dataset, to be established.By collect storage status data and
Device status data, and it is inputted intelligent storage model；And according to the intelligent storage model, the storehouse of calling needed for calculating
The concrete operations instruction of equipment and O&M equipment of storing in a warehouse is tieed up in storage and transportation, and according to calculated result, it is complete to control corresponding storage O&M equipment
At the concrete operations instruction corresponding to it.In this way, just solving, existing storage O&M method efficiency is poor, warehouse cost is high
Problem.
Detailed description of the invention
Fig. 1 is the preparation flow chart of the storage method provided in an embodiment of the present invention based on artificial intelligence；
Fig. 2 is building and the training method flow chart of intelligent storage model provided in an embodiment of the present invention；
Fig. 3 is the O&M flow chart of the storage method provided in an embodiment of the present invention based on artificial intelligence；
Fig. 4 is the structure chart of the warehousing system provided in an embodiment of the present invention based on artificial intelligence.
Specific embodiment
The present invention is described in further detail below by specific embodiment and in conjunction with attached drawing.
It refering to fig. 1, is the preparation process of the storage method provided in an embodiment of the present invention based on artificial intelligence, including following
Step：
In step s101, according to the acquisition demand of storage status data, corresponding storage sensor is laid in warehouse.
Depot data index includes but is not limited to including sensors such as temperature and humidity, gas, pressure, insect pest situation.
In step s 102, according to the demand for control of storage O&M equipment, the storage O&M equipment of Electronic control formula is laid.
According to the actual situation, the storage O&M equipment of Electronic control formula can directly be purchased.It can also be to existing manual
It controls equipment and carries out electronic transformation.
O&M equipment of storing in a warehouse includes but is not limited to the equipment such as storehouse window, blower, air-conditioning, gassing machine.
In step s 103, things-internet gateway is set up, the gateway is transported by communication interface and storage sensor and storage
Dimension equipment is connected, in order to which the device status data of store in a warehouse status data and O&M equipment of storing in a warehouse is collected.
It is connected by communication interfaces such as RS232, RS485, RS422 with storage sensor and storage O&M equipment, is collected
The device status data of storage status data and O&M equipment of storing in a warehouse that sensor of storing in a warehouse acquires.
In step S104, established by the latticed forms such as NB-Iot, 3G/4G network, Wifi, Ethernet and server
Storage status data and device status data are uploaded onto the server, while realizing server by things-internet gateway by connection
Long-range control to sensor and equipment.
The long-range control includes but is not limited to the frequency acquisition of sensor, the opening and closing of equipment, gear of equipment etc..
Referring to Fig.2, for the building of intelligent storage model provided in an embodiment of the present invention and the process of training method, including with
Lower step：
In step s 201, periodically the storage status data of the current storage sensor of acquisition, O&M equipment of storing in a warehouse are set
Expert's operation data of standby status data and corresponding storage O&M equipment arrives the training data storage as training data
Server database.
In step S202, by the storage status data stored in server database, device status data and corresponding
The expert's operation data for O&M equipment of storing in a warehouse is established with intelligent storage model as the feature data types of input layer
The relationship mapped one by one.
In step S203, deep learning classifier is constructed, by multilayer hidden layer neuron by input layer and output layer
It connects, the neuronal quantity of each hidden layer is set, the number of the target type of output layer is set, and initialize each nerve
The linear relationship coefficient and bias value of member.
In step S204, data set is docked with classifier, using storage status data, device status data as input
Value, using equipment operation data as target output type, is arranged the frequency of training of every group of data.
Periodically newest storage status data, device status data and equipment expert are grasped by a background program
Make data as training dataset and carrys out training smart storage model.
Referring to Fig. 3, which depict the O&M processes of the storage method provided by the invention based on artificial intelligence, specifically such as
Under：
Step S301, the storage status data of real-time collecting storage sensor and the device status data for O&M equipment of storing in a warehouse, and
The storage status data and device status data are inputted into intelligent storage model.
Step S302, according to the intelligent storage model, the storage O&M equipment and storage O&M called needed for calculating
The concrete operations of equipment instruct.
Step S303, according to calculated result, the concrete operations that the corresponding storage O&M equipment of control completes corresponding to it refer to
It enables.
The intelligent storage model is by first constructing deep neural network machine learning model, then by periodically
The expert's operation data for passing current storage status data and corresponding storage O&M equipment is trained as training dataset
It is established.
As the embodiment of the present invention, before step 301, intelligent storage model can also be loaded by server
Memory is quickly calculated with realizing.
The embodiment of the present invention provides a kind of storage O&M method based on artificial intelligence, can be according to warehouse actual conditions
And all data index of storage sensor, automatically control storage O&M equipment, timely prevents the generation of abnormal problem,
Realize the unmanned warehousing management intervened.A large amount of human costs and management cost, precise manipulation facility can be saved using the present invention
Equipment improves efficiency, and reduces energy consumption, and prevention in time and processing abnormal problem extend storage time, eliminate unnecessary loss.
Construct the deep neural network machine learning model of intelligent storage management；By collected storage status data and storage expert
Training smart storage model is come as training dataset to the operation of equipment；To be stored in a warehouse status data, equipment state number in real time
According to input intelligent storage model, equipment operation mode is calculated；Server is long-range to control corresponding bin storage and transportation dimension according to calculated result
Equipment completes corresponding operating.
It is that the present invention provides a kind of warehousing system based on artificial intelligence, which includes refering to Fig. 4：
Data collection module 41, for the storage status data of real-time collecting storage sensor and the equipment shape for O&M equipment of storing in a warehouse
State data, and the storage status data and device status data are inputted into intelligent storage model；
Intelligence computation module 42, storage O&M equipment and storehouse for being called needed for according to the intelligent storage model, calculating
The concrete operations instruction of equipment is tieed up in storage and transportation；
Intelligent control module 43, for according to calculated result, control corresponding storage O&M equipment complete it is specific corresponding to it
Operational order,
The intelligent storage model is worked as by first constructing deep neural network machine learning model, then by periodically uploading
Expert's operation data of preceding storage status data and corresponding storage O&M equipment is built as training dataset to train
Vertical.
As the embodiment of the present invention, the system comprises：Training data collects module 31, works as periodically acquiring
The storage status data of preceding storage sensor, the device status data for O&M equipment of storing in a warehouse and corresponding storage O&M equipment
Expert's operation data is as training data, by training data storage to server database；
Data respective modules 32, storage status data, device status data and correspondence for will be stored in server database
Expert's operation data of storage O&M equipment built with the feature data types in intelligent storage model as input layer
The vertical relationship mapped one by one；
Module 33 is constructed, for constructing deep learning classifier, is connected input layer and output layer by multilayer hidden layer neuron
It picks up and, the neuronal quantity of each hidden layer is set, the number of the target type of output layer is set, and initializes each neuron
Linear relationship coefficient and bias value；
Setup module 34, for docking data set with classifier, using storage status data, device status data as input
Value, using equipment operation data as target output type, is arranged the frequency of training of every group of data.
As the embodiment of the present invention, the system comprises：Loading module 40, in being loaded into intelligent storage model
It deposits, is quickly calculated with realizing.
In an embodiment of the present invention, which is by first constructing deep neural network machine learning mould
Type, then the device status data by periodically uploading current storage status data, O&M equipment of storing in a warehouse and corresponding
Expert's operation data of storage O&M equipment, which is trained as training dataset, to be established.By collect storage status data and
Device status data, and it is inputted intelligent storage model；And according to the intelligent storage model, the storehouse of calling needed for calculating
The concrete operations instruction of equipment and O&M equipment of storing in a warehouse is tieed up in storage and transportation, and according to calculated result, it is complete to control corresponding storage O&M equipment
At the concrete operations instruction corresponding to it.In this way, just solving, existing storage O&M method efficiency is poor, warehouse cost is high
Problem.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of storage method based on artificial intelligence, which is characterized in that the described method comprises the following steps：
Real-time collecting is stored in a warehouse the storage status data of sensor and the device status data for O&M equipment of storing in a warehouse, and by the storage
Status data and device status data input intelligent storage model；
According to the intelligent storage model, the concrete operations of the storage O&M equipment and O&M equipment of storing in a warehouse called needed for calculating
Instruction；
According to calculated result, the corresponding storage O&M equipment of control completes the concrete operations instruction corresponding to it,
The intelligent storage model is worked as by first constructing deep neural network machine learning model, then by periodically uploading
Expert's operation data of preceding storage status data and corresponding storage O&M equipment is built as training dataset to train
Vertical.
2. the storage method according to claim 1 based on artificial intelligence, which is characterized in that store in a warehouse in the real-time collecting
The device status data of the storage status data of sensor and O&M equipment of storing in a warehouse, and by the storage status data and equipment shape
State data further include step before inputting the step of intelligent storage model：
According to the acquisition demand of storage status data, corresponding storage sensor is laid in warehouse；
According to the demand for control of storage O&M equipment, the storage O&M equipment of Electronic control formula is laid；
Things-internet gateway is set up, the gateway is connected by communication interface with storage sensor and storage O&M equipment, so as to
It is collected in the device status data of storage status data and O&M equipment of storing in a warehouse.
3. the storage method according to claim 1 based on artificial intelligence, which is characterized in that the intelligent storage model
Foundation includes the following steps：
Periodically the storage status data of the current storage sensor of acquisition, the device status data for O&M equipment of storing in a warehouse and right
The expert's operation data for the storage O&M equipment answered is as training data, by training data storage to server database；
By the storage status data stored in server database, the expert of device status data and corresponding storage O&M equipment
The relationship mapped one by one is established in operation data and intelligent storage model as the feature data types of input layer；
Deep learning classifier is constructed, is connected input layer and output layer by multilayer hidden layer neuron, is arranged each hidden
The neuronal quantity for hiding layer, is arranged the number of the target type of output layer, and initialize the linear relationship coefficient of each neuron
With bias value；
Data set is docked with classifier, using storage status data, device status data as input value, by equipment operation data
As target output type, the frequency of training of every group of data is set.
4. the storage method according to claim 1 based on artificial intelligence, which is characterized in that store in a warehouse in the real-time collecting
The device status data of the storage status data of sensor and O&M equipment of storing in a warehouse, and by the storage status data and equipment shape
State data further include step before inputting the step of intelligent storage model：
Intelligent storage model is loaded into memory, is quickly calculated with realizing.
5. a kind of warehousing system based on artificial intelligence, which is characterized in that the system comprises：
Data collection module, for the storage status data of real-time collecting storage sensor and the equipment state for O&M equipment of storing in a warehouse
Data, and the storage status data and device status data are inputted into intelligent storage model；
Intelligence computation module, storage O&M equipment and storage for being called needed for according to the intelligent storage model, calculating
The concrete operations of O&M equipment instruct；
Intelligent control module, for according to calculated result, the corresponding storage O&M equipment of control to complete the specific behaviour corresponding to it
It instructs,
The intelligent storage model is worked as by first constructing deep neural network machine learning model, then by periodically uploading
Expert's operation data of preceding storage status data and corresponding storage O&M equipment is built as training dataset to train
Vertical.
6. the warehousing system according to claim 5 based on artificial intelligence, which is characterized in that the system comprises：
Training data collects module, for periodically acquiring storage status data, the storage O&M of current storage sensor
Expert's operation data of the device status data of equipment and corresponding storage O&M equipment is as training data, by the trained number
Server database is arrived according to storage；
Data respective modules, storage status data, device status data for will be stored in server database and corresponding
Feature data types in the expert's operation data and intelligent storage model of O&M equipment of storing in a warehouse as input layer are established
The relationship mapped one by one；
Input layer is connect by building module for constructing deep learning classifier by multilayer hidden layer neuron with output layer
Get up, the neuronal quantity of each hidden layer is set, the number of the target type of output layer is set, and initializes each neuron
Linear relationship coefficient and bias value；
Setup module, for data set to be docked with classifier, the status data that will store in a warehouse, device status data as input value,
Using equipment operation data as target output type, the frequency of training of every group of data is set.
7. the warehousing system according to claim 5 based on artificial intelligence, which is characterized in that the system comprises：
Loading module is quickly calculated for intelligent storage model to be loaded into memory with realizing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810655109.7A CN108873697A (en) | 2018-06-23 | 2018-06-23 | A kind of storage method and system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810655109.7A CN108873697A (en) | 2018-06-23 | 2018-06-23 | A kind of storage method and system based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108873697A true CN108873697A (en) | 2018-11-23 |
Family
ID=64294399
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810655109.7A Pending CN108873697A (en) | 2018-06-23 | 2018-06-23 | A kind of storage method and system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108873697A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111831675A (en) * | 2020-07-07 | 2020-10-27 | 平安科技（深圳）有限公司 | Storage model training method and device, computer equipment and storage medium |
| CN117557397A (en) * | 2023-12-08 | 2024-02-13 | 广州德威生物科技有限公司 | Method and system for controlling disinfection based on intelligent AI monitoring of warehouse pests |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103453727A (en) * | 2013-09-13 | 2013-12-18 | 柳州职业技术学院 | Distributed refrigeration control system for storage refrigeration house and control method of distributed refrigeration control system |
| CN107358388A (en) * | 2016-11-03 | 2017-11-17 | 厦门嵘拓物联科技有限公司 | A kind of WMS based on Internet of Things and the storage quality risk appraisal procedure based on the system |
| CN107948299A (en) * | 2017-12-05 | 2018-04-20 | 四川理工学院 | Application process and system of a kind of big data treatment technology in salt chemical engineering production |
| CN108090628A (en) * | 2018-01-16 | 2018-05-29 | 浙江大学 | A kind of grain feelings security detection and analysis method based on PSO-LSSVM algorithms |
-
2018
- 2018-06-23 CN CN201810655109.7A patent/CN108873697A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103453727A (en) * | 2013-09-13 | 2013-12-18 | 柳州职业技术学院 | Distributed refrigeration control system for storage refrigeration house and control method of distributed refrigeration control system |
| CN107358388A (en) * | 2016-11-03 | 2017-11-17 | 厦门嵘拓物联科技有限公司 | A kind of WMS based on Internet of Things and the storage quality risk appraisal procedure based on the system |
| CN107948299A (en) * | 2017-12-05 | 2018-04-20 | 四川理工学院 | Application process and system of a kind of big data treatment technology in salt chemical engineering production |
| CN108090628A (en) * | 2018-01-16 | 2018-05-29 | 浙江大学 | A kind of grain feelings security detection and analysis method based on PSO-LSSVM algorithms |
Non-Patent Citations (2)
| Title |
|---|
| 熊茂华 等: "《物联网技术与应用开发》", 31 August 2012, 西安电子科技大学出版社 * |
| 王红霞 等: "卷积神经网络在药品冷链监控系统中的应用", 《沈阳理工大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111831675A (en) * | 2020-07-07 | 2020-10-27 | 平安科技（深圳）有限公司 | Storage model training method and device, computer equipment and storage medium |
| CN117557397A (en) * | 2023-12-08 | 2024-02-13 | 广州德威生物科技有限公司 | Method and system for controlling disinfection based on intelligent AI monitoring of warehouse pests |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210059127A1 (en) | Cannabis growth methods and systems | |
| CN110440396B (en) | Cloud-side-end-coordinated global optimization energy-saving control method and system for central air conditioner | |
| KR102525632B1 (en) | Chinese medicinal material climate controlled storage system and control method therefor | |
| CN103576632B (en) | Pig growth environmental monitoring based on technology of Internet of things and control system and method | |
| CN107179732A (en) | A kind of aquaculture intelligent comprehensive monitoring system based on Internet of Things | |
| CN103235572A (en) | Intelligentized digital grain depot monitoring system | |
| KR20170084834A (en) | Cattle Shed Management System in mobile cloud environment by using IoT data | |
| CN102789222A (en) | Intelligent measurement and control system of greenhouse based on internet of things | |
| CN108873697A (en) | A kind of storage method and system based on artificial intelligence | |
| CN103196486A (en) | Monitoring system and monitoring method for tobacco storage based on wireless sensor network | |
| KR102131037B1 (en) | Next generation smart farm operating system | |
| CN112631356A (en) | Intelligent ventilation method and system for grain depot | |
| CN116500955B (en) | Zero-carbon type storage building internal environment control system and method | |
| CN111915094A (en) | Warehousing method and system based on artificial intelligence | |
| CN209248301U (en) | One kind automating managing and control system based on Internet of Things freezer | |
| CN111240261A (en) | Method and system for realizing intelligent ventilation of grain storage and preservation based on Internet of things equipment | |
| KR20200031966A (en) | A traditional sericulture factory system using containers | |
| Kantaros et al. | Setting up a digital twin assisted greenhouse architecture | |
| JP2016213589A (en) | Environment management system | |
| CN116456823A (en) | Vertical farming system and method | |
| CN117111660A (en) | Unattended intelligent granary system and method | |
| US20230117676A1 (en) | Methods and internet of things systems for maintaining and managing storage and distribution station of smart gas | |
| Bunluewong | Semi-Automated Mushroom Cultivation House using Internet of Things | |
| CN115755691A (en) | Tibetan monitoring and management system and construction method thereof | |
| CN107544342A (en) | Silo intelligent monitor system based on Temperature Humidity Sensor |
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 |