CN117852819A - Intelligent dispatching and optimizing method and system for bulk cargo wharf - Google Patents
Intelligent dispatching and optimizing method and system for bulk cargo wharf Download PDFInfo
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Abstract
The invention relates to the field of cargo management, and discloses a method and a system for intelligent dispatching and optimizing of a bulk cargo wharf, wherein the method comprises the following steps of S1: dividing a bulk cargo wharf into a plurality of target areas according to grids; s2: collecting storage information in a target area in real time; s3: analyzing and processing the acquired warehouse information, acquiring cargo type, distribution, proportion and site redundancy value data of each target area, judging the rationality of a dispatching plan according to the cargo data, judging whether the dispatching plan is reasonable according to the cargo stocking data, and judging whether the warehouse use of the target area is reasonable according to the redundancy value; s4: marking and early warning a target area with unreasonable use of the judging warehouse, generating a recommendation index for a reasonable plan of the judging cargo scheduling plan, matching the recommendation index with cargoes to be put in and put out, and recommending the cargoes to be corresponding to the corresponding target area to be stored or put out according to the matching result.
Description
Technical Field
The invention relates to the field of cargo management, in particular to a method and a system for intelligent dispatching and optimizing of a bulk cargo wharf.
Background
The intelligent dispatching and optimizing of the bulk cargo wharf refers to the process of optimizing management of the bulk cargo wharf, improving the working efficiency, reducing the management cost, ensuring the safe storage of cargoes and the like;
the existing goods management of the bulk goods terminal can only store the goods in sequence according to a preset mode, but the goods arrangement mode and process in the bulk goods terminal are recorded and managed by personnel, and the goods in-out schedule is manually arranged according to experience, so that the goods scheduling schedule of different personnel can not be uniform in efficiency, uniform standards can not be formed, and the effective utilization rate of space and the production efficiency of the bulk goods terminal are affected; meanwhile, the space utilization of stored goods lacks corresponding analysis and evaluation, and cannot be modified in a targeted manner.
Disclosure of Invention
The invention aims to provide a method and a system for intelligent dispatching and optimizing of bulk cargo wharf, which solve the technical problems.
The aim of the invention can be achieved by the following technical scheme:
a bulk cargo wharf intelligent scheduling and optimizing method comprises the following steps:
s1: dividing a bulk cargo wharf into a plurality of target areas according to grids;
s2: at least one group of three-dimensional scanners and monitoring cameras are arranged in each target area, and storage information in the target areas is collected in real time;
s3: analyzing and processing the acquired warehouse information, acquiring cargo type, distribution, proportion and site redundancy value data of each target area, judging the rationality of a dispatching plan according to the cargo data, judging whether the dispatching plan is reasonable according to the cargo stocking data, and judging whether the warehouse use of the target area is reasonable according to the site redundancy value;
s4: marking and early warning a target area with unreasonable use of the judging warehouse, generating a recommendation index for a reasonable plan of the judging cargo scheduling plan, matching the recommendation index with cargoes to be put in and put out, and recommending the cargoes to be corresponding to the corresponding target area to be stored or put out according to the matching result.
As a further technical solution, the warehouse information includes: the type, distribution and occupied volume of the stored goods.
As a further technical solution, the process of obtaining the site redundancy value is:
by the formula:
calculating to obtain field redundancy valueWherein P is 0 For the standard volume of the target area, when P 0 ＝P i When the target area is judged to have no empty space; i is the i-th cargo; i epsilon [1, n ]]，R i For the volume occupied, delta, of each distribution area of the i-th cargo i For the number of distribution areas in the i-th cargo, n is the total number of cargo categories.
As a further technical scheme, the process of judging whether the target area is used reasonably according to the acquired site redundancy value is as follows:
scanning a stacked goods stack in a target area based on a three-dimensional scanner, and establishing a three-dimensional model according to scanned data;
acquiring the actual residual volume P 'of the target area based on the three-dimensional model' th ；
By the formula:calculating to obtain a deviation coefficient E i ；
Wherein alpha is i Selecting and determining the reference coefficient according to historical experience data;
the deviation coefficient E to be obtained i And a preset threshold E 0 The comparison is carried out,
if E i ≤E 0 Judging that the target area is reasonable in storage use;
if E i >E 0 And judging that the storage of the target area is unreasonable.
As a further technical scheme, the process of generating the recommended index for the target area with reasonable storage use is as follows:
will satisfy E i ≤e 0 Uniformly marking the target area mark of (2) to be yellow;
coefficient of deviation E of target area to be marked yellow i Arranged from large to small and sequentially counted as q j ；
Target area to be represented as yellowArranged from small to large and sequentially counted as c j ；
By the formula: t (T) j ＝q j *β 1 +c j *β 2 Calculating an acquisition recommendation index, wherein beta 1 、β 2 And the ratio coefficient is determined according to historical experience data and experimental data.
As a further technical solution, the process of matching the recommendation index with the goods to be put in storage includes:
acquiring the types and the quantity of goods to be put in storage;
by the formula:calculating and obtaining the expected occupied volume of the goods to be put in storage; r is R k *δ k The method comprises the steps of (1) presetting the volume of the k-th goods to be put in storage;
will satisfy P Into (I) ＜P’ th The yellow target areas of the conditions are integrated into an area set;
calculating a matching value of a target region in the region set;
by the formula:
obtaining a matching value delta j 。
As a further technical scheme, the process of recommending goods to be put in storage to enter corresponding target areas for storage according to the matching result comprises the following steps:
delta obtained by calculation j And a preset threshold delta 0 A comparison is made with respect to the number of the cells,
if delta j ＞δ 0 Recommending the target area to enter the target area;
otherwise, not recommending the goods to be put in storage to enter the target area.
An intelligent dispatching and optimizing system for bulk cargo wharf, comprising:
the planning module is used for dividing the bulk cargo wharf into a plurality of target areas according to grids;
the information acquisition module is used for arranging at least one group of monitoring cameras in each target area and acquiring storage information in the target area in real time;
the analysis module is used for analyzing and processing the acquired warehousing information, acquiring a site redundancy value of each target area, and judging whether the warehousing of the target area is reasonable or not according to the site redundancy value;
the early warning module marks and early warns the target area with unreasonable warehouse usage;
the recommending module is used for generating recommending indexes for judging reasonable target areas for storage, matching the recommending indexes with the goods to be stored, and recommending the goods to be stored into the corresponding target areas for storage according to matching results.
The invention has the beneficial effects that:
(1) According to the invention, through grid division of the bulk cargo wharf, cargo management can be conveniently carried out on each area, real-time storage information is obtained based on the monitoring cameras installed in each target area, the storage information comprises the types, distribution conditions and occupied volumes of stored cargos, and then the existing bulk cargo wharf conditions are analyzed, so that the condition of unreasonable storage use conditions in which target areas are rapidly judged, early warning is timely and rapidly carried out, management staff can be informed of rearranging and storing the cargos in the areas, meanwhile, reasonable target areas for storage use are judged to generate recommended indexes, the recommended indexes are matched with the cargos to be stored according to the recommended indexes, the cargos to be stored are recommended to enter the corresponding target areas according to the matching results, and the storage efficiency of the new cargos to be stored is improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a process step diagram of the present invention;
fig. 2 is a system block diagram 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.
Referring to fig. 1-2, the invention discloses a method for intelligent dispatching and optimizing of bulk cargo wharf, which comprises the following steps:
s1: dividing a bulk cargo wharf into a plurality of target areas according to grids;
s2: at least one group of three-dimensional scanners and monitoring cameras are arranged in each target area, and storage information in the target areas is collected in real time;
s3: analyzing and processing the acquired warehouse information, acquiring cargo type, distribution, proportion and site redundancy value data of each target area, judging the rationality of a dispatching plan according to the cargo data, judging whether the dispatching plan is reasonable according to the cargo stocking data, and judging whether the warehouse use of the target area is reasonable according to the site redundancy value;
s4: marking and early warning a target area with unreasonable use of the judging warehouse, generating a recommendation index for a reasonable plan of the judging cargo scheduling plan, matching the recommendation index with cargoes to be put in and put out, and recommending the cargoes to be corresponding to the corresponding target area to be stored or put out according to the matching result.
According to the technical scheme, the bulk cargo wharf is subjected to grid division, so that cargo management can be conveniently carried out on each area, real-time storage information is acquired based on the monitoring cameras installed in each target area, the storage information comprises the types, distribution conditions and occupied volumes of stored cargos, the existing bulk cargo wharf conditions are analyzed, therefore, the storage use conditions or scheduling production plans in which target areas are unreasonable are rapidly judged, early warning can be timely and rapidly carried out, an operation machine can be informed to rearrange and store cargos in the areas, meanwhile, reasonable target areas for storage use are judged to generate recommended indexes, the cargos to be stored are matched according to the recommended indexes, the cargos to be stored are recommended to enter the corresponding target areas according to matching results, and the storage efficiency of the newly-stored cargos is improved.
The process of obtaining the site redundancy value is as follows:
by the formula:
calculating to obtain field redundancy valueWherein P is 0 For the standard volume of the target area, when P 0 ＝P i When the target area is judged to have no empty space; i is the i-th cargo; i epsilon [1, n ]]，R i For class i goods eachOccupied volume of distribution area delta i For the number of distribution areas in the i-th cargo, n is the total number of cargo categories.
Through the technical scheme, the method for acquiring the site redundancy value of the target area is provided, specifically, through a formulaCalculating to obtain a site redundancy value +.>Wherein P is 0 For the standard volume of the target area, when P 0 ＝P i When the target area is judged to have no empty space; i is the i-th cargo; i epsilon [1, n ]]，R i For the volume occupied, delta, of each distribution area of the i-th cargo i The number of the distribution areas in the i-th type of goods is n, and the total number of the types of the goods is n; obviously, the expected occupation condition and distribution condition of each dye in each target area can be rapidly calculated, and the storage service condition of each target area can be fed back according to the expected occupation condition and the distribution condition.
The process for judging whether the target area is reasonably used according to the acquired site redundancy value comprises the following steps:
scanning a stacked goods stack in a target area based on a three-dimensional scanner, and establishing a three-dimensional model according to scanned data;
acquiring the actual residual volume P 'of the target area based on the three-dimensional model' th ；
By the formula:calculating to obtain a deviation coefficient E i ；
Wherein alpha is i Selecting and determining the reference coefficient according to historical experience data;
the deviation coefficient E to be obtained i And a preset threshold E 0 The comparison is carried out,
if E i ≤E 0 Judging that the target area is reasonable in storage use;
if E i >E 0 And judging that the storage of the target area is unreasonable.
Through the technical scheme, the method for judging the storage condition of the target area is provided, specifically, a three-dimensional scanner is used for scanning a stack of goods stacked in the target area, and a three-dimensional model is built according to scanned data; acquiring the actual residual volume P 'of the target area based on the three-dimensional model' th The method comprises the steps of carrying out a first treatment on the surface of the By the formula:calculating to obtain a deviation coefficient E i The method comprises the steps of carrying out a first treatment on the surface of the Wherein alpha is i Selecting and determining the reference coefficient according to historical experience data; the deviation condition between the site redundant value and the actual residual volume at the position can be fed back quickly through the formula, and the smaller the deviation coefficient is, the more reasonable the target area is represented; and then the obtained deviation coefficient E i And a preset threshold E 0 Comparing, if E i ≤E 0 The target area storage actual condition is better and the use is reasonable; if E i >E 0 And judging that the actual storage condition of the target area is poor, and if the target area is unreasonable to use, the target area needs to be modified.
It should be noted that, the three-dimensional scanner is a scientific instrument for detecting and analyzing the shape and appearance data of an object or environment in the real world, the collected data are often used for performing three-dimensional reconstruction calculation, and a digital model of the actual object is created in the virtual world, which is not described in detail herein.
The process for generating the recommended index for judging the reasonable target area for storage comprises the following steps:
will satisfy E i ≤E 0 Uniformly marking the target area mark of (2) to be yellow;
coefficient of deviation E of target area to be marked yellow i Arranged from large to small and sequentially counted as q j ；
Target area to be represented as yellowArranged from small to large and sequentially counted as c j ；
By the formula: t (T) j ＝q j *β 1 +c j *β 2 Calculating an acquisition recommendation index, wherein beta 1 、β 2 And the ratio coefficient is determined according to historical experience data and experimental data.
Through the technical scheme, the method for recommending the reasonable-use target area is provided, particularly, the reasonable-use area is marked, so that the use condition of the existing warehouse can be conveniently and rapidly distinguished, and meanwhile, the deviation coefficient E of the yellow-marked target area is used i Arranged from large to small and sequentially counted as q j The method comprises the steps of carrying out a first treatment on the surface of the Target area to be represented as yellowArranged from small to large and sequentially counted as c j The method comprises the steps of carrying out a first treatment on the surface of the By the formula: t (T) j ＝q j *β 1 +c j *β 2 Calculating an acquisition recommendation index, wherein beta 1 、β 2 As a proportionality coefficient, selecting and determining according to historical experience data and experimental data; obviously, the deviation coefficient q can be combined by the above formula j And site redundancy value->Establishing a relation, synthesizing a target area with reasonable site redundancy value and deviation coefficient, forming recommendation, and obviously, q j 、c j The larger the sorting value is, the higher the recommended value is, and the recommendation can be performed for the next goods warehouse-in.
The process of matching the goods to be put in storage according to the recommended index comprises the following steps:
acquiring the types and the quantity of goods to be put in storage;
by the formula:calculating and obtaining the expected occupied volume of the goods to be put in storage;R k *δ k the method comprises the steps of (1) presetting the volume of the k-th goods to be put in storage;
will satisfy P Into (I) ＜P’ th The yellow target areas of the conditions are integrated into an area set;
calculating a matching value of a target region in the region set;
by the formula:
obtaining a matching value delta j 。
Through the technical scheme, the capacity required by the goods to be put in storage is budgeted through a formula, and then P is satisfied Into (I) ＜P’ th The yellow target area of the condition is integrated into an area set, can be used as an alternative target area for warehousing the goods at the time, and then is processed through a formula Calculating to obtain a matching value delta j And timely and accurately reflecting the matching degree between each target area in each area set and the goods to be put in storage.
Recommending goods to be put in storage to enter corresponding target areas according to the matching result, wherein the process comprises the following steps:
delta obtained by calculation j And a preset threshold delta 0 A comparison is made with respect to the number of the cells,
if delta j ＞δ 0 Recommending the target area to enter the target area;
otherwise, not recommending the goods to be put in storage to enter the target area.
Through the technical scheme, delta obtained by calculation is calculated j And a preset threshold delta 0 Comparing if delta j ＞δ 0 Recommending the target area to enter the target area; otherwise, not recommending the goods to be put in storage to enter the target areaTherefore, the target areas which do not meet the requirements can be screened out rapidly, and the matching accuracy of the goods warehouse-in is improved.
An intelligent dispatching and optimizing system for bulk cargo wharf, comprising:
the planning module is used for dividing the bulk cargo wharf into a plurality of target areas according to grids;
the information acquisition module is used for arranging at least one group of three-dimensional scanners and monitoring cameras in each target area and acquiring storage information in the target area in real time;
the analysis module is used for analyzing and processing the acquired warehousing information, acquiring a site redundancy value of each target area, and judging whether the warehousing use and the dispatching plan of the target area are reasonable according to the site redundancy value data;
the early warning module marks and early warns the target area with unreasonable warehouse usage; marking alternative plans with unreasonable scheduling plans;
and the recommending module is used for generating a recommending index for a plan which is judged to be reasonable in the goods scheduling plan, matching the recommending index with goods to be put in and put out, and recommending the goods to be corresponding to the matching result to enter a corresponding target area for storage or put out.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (8)
1. The intelligent dispatching and optimizing method for the bulk cargo wharf is characterized by comprising the following steps of:
s1: dividing a bulk cargo wharf into a plurality of target areas according to grids;
s2: at least one group of three-dimensional scanners and monitoring cameras are arranged in each target area, and storage information in the target areas is collected in real time;
s3: analyzing and processing the acquired warehouse information, acquiring cargo type, distribution, proportion and site redundancy value data of each target area, judging the rationality of a dispatching plan according to the cargo data, judging whether the dispatching plan is reasonable according to the cargo stocking data, and judging whether the warehouse use of the target area is reasonable according to the site redundancy value;
s4: marking and early warning a target area with unreasonable use of the judging warehouse, generating a recommendation index for a reasonable plan of the judging cargo scheduling plan, matching the recommendation index with cargoes to be put in and put out, and recommending the cargoes to be corresponding to the corresponding target area to be stored or put out according to the matching result.
2. The method of intelligent dispatch and optimization for bulk cargo terminals according to claim 1, wherein the warehouse information comprises: the type, distribution condition and occupied volume of goods are stored.
3. The method for intelligent dispatching and optimizing of bulk cargo terminals according to claim 1 or 2, wherein the process of obtaining the site redundancy value is as follows:
by the formula:
calculating to obtain field redundancy valueWherein P is 0 For the standard volume of the target area, when P 0 ＝P i When the target area is judged to have no empty space; i is the i-th cargo; i epsilon [1, n ]]，R i For the volume occupied, delta, of each distribution area of the i-th cargo i For the number of distribution areas in the i-th cargo, n is the total number of cargo categories.
4. The method for intelligent dispatching and optimizing of bulk cargo wharf according to claim 3, wherein the process for judging whether the target area is used reasonably according to the obtained site redundancy value is as follows:
scanning a stacked goods stack in a target area based on a three-dimensional scanner, and establishing a three-dimensional model according to scanned data;
acquiring the actual residual volume P 'of the target area based on the three-dimensional model' th ；
By the formula:calculating to obtain a deviation coefficient E i ；
Wherein alpha is i Selecting and determining the reference coefficient according to historical experience data;
the deviation coefficient E to be obtained i And a preset threshold E 0 The comparison is carried out,
if E i ≤E 0 Judging that the target area is reasonable to use;
if E i ＞E 0 And judging that the target area is unreasonable to use.
5. The method for intelligent dispatching and optimizing of bulk cargo wharf according to claim 1 or 4, wherein the process of generating the recommendation index for the target area with reasonable judging use and dispatching plan is as follows:
will satisfy E i ≤E 0 Uniformly marking the target area mark of (2) to be yellow;
the deviation coefficient Ei of the target area marked as yellow is arranged from large to small and sequentially counted as q j ；
Target area to be represented as yellowArranged from small to large and sequentially counted as c j ；
By the formula: t (T) j ＝q j *β 1 +c j *β 2 Calculating an acquisition recommendation index, wherein beta 1 、β 2 And the ratio coefficient is determined according to historical experience data and experimental data.
6. The method of intelligent dispatching and optimizing of bulk cargo terminals according to claim 1, wherein the process of matching the recommended index with the cargo to be put in storage comprises the following steps:
acquiring the types and the quantity of goods to be put in storage;
by the formula:calculating and obtaining the expected occupied volume of the goods to be put in storage; r is R k *δ k The method comprises the steps of (1) presetting the volume of the k-th goods to be put in storage;
will satisfy P Into (I) ＜P’ th The yellow target areas of the conditions are integrated into an area set;
calculating a matching value of a target region in the region set;
by the formula:
obtaining a matching value delta j 。
7. The method for intelligent dispatching and optimizing of bulk cargo wharf according to claim 1, wherein the process of recommending the goods to be put in storage into the corresponding target area for storage according to the matching result is as follows:
delta obtained by calculation j And a preset threshold delta 0 A comparison is made with respect to the number of the cells,
if delta j ＞δ 0 Recommending the target area to enter the target area;
otherwise, not recommending the goods to be put in storage to enter the target area.
8. An intelligent dispatching and optimizing system of a bulk cargo wharf, which is suitable for the intelligent dispatching and optimizing method of the bulk cargo wharf according to any one of claims 1 to 7; characterized by comprising the following steps:
the planning module is used for dividing the bulk cargo wharf into a plurality of target areas according to grids;
the information acquisition module is used for arranging at least one group of three-dimensional scanners and monitoring cameras in each target area and acquiring storage information in the target area in real time;
the analysis module is used for analyzing and processing the acquired warehousing information, acquiring a site redundancy value of each target area, and judging whether the warehousing use and the dispatching plan of the target area are reasonable according to the site redundancy value data;
the early warning module marks and early warns the target area with unreasonable warehouse usage; marking alternative plans with unreasonable scheduling plans;
and the recommending module is used for generating a recommending index for a plan which is judged to be reasonable in the goods scheduling plan, matching the recommending index with goods to be put in and put out, and recommending the goods to be corresponding to the matching result to enter a corresponding target area for storage or put out.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116051004A (en) * | 2023-03-27 | 2023-05-02 | 深圳市宏大供应链服务有限公司 | Intelligent management method, system and medium based on big data |
| CN116415893A (en) * | 2023-04-11 | 2023-07-11 | 安徽路歌运输有限公司 | Logistics storage management method, system and storage medium |
| CN116843269A (en) * | 2023-09-01 | 2023-10-03 | 北京大学 | Intelligent unmanned retail warehouse system based on Internet of things |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116051004A (en) * | 2023-03-27 | 2023-05-02 | 深圳市宏大供应链服务有限公司 | Intelligent management method, system and medium based on big data |
| CN116415893A (en) * | 2023-04-11 | 2023-07-11 | 安徽路歌运输有限公司 | Logistics storage management method, system and storage medium |
| CN116843269A (en) * | 2023-09-01 | 2023-10-03 | 北京大学 | Intelligent unmanned retail warehouse system based on Internet of things |
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