CN116777241A - Goods data analysis method and system based on scp supply chain and electronic equipment - Google Patents
Goods data analysis method and system based on scp supply chain and electronic equipment Download PDFInfo
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Abstract
The invention relates to a scp supply chain technology, and discloses a method, a system and electronic equipment for analyzing goods data based on a scp supply chain, wherein the method comprises the following steps: acquiring goods data corresponding to the goods, and performing cluster analysis on the goods data to obtain the types of the goods; analyzing commodity indexes and structure indexes according to the types and the data of the goods, and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes; generating a supply chain plan according to the inventory plan and the sales plan, and performing risk assessment on the supply chain plan to obtain an assessment result; and adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan, and managing the goods according to the standard supply chain plan. In addition, the present invention relates to blockchain technology, where inventory plans and sales plans may be stored at nodes of the blockchain. The invention can improve the accuracy of the analysis of the goods data and reduce the risk when constructing the supply chain plan, thereby improving the efficiency of the goods data management.
Description
Technical Field
The invention relates to the technical field of scp supply chains, in particular to a method, a system and electronic equipment for analyzing commodity data based on a scp supply chain.
Background
The development and application of the internet technology accumulate mass goods data information for social progress, and along with the generation of the mass goods data information, people can know and perceive things in various fields deeply, and information sources are enriched, but the burden is brought to the performance of processing goods data by a computer, so that the management efficiency of the goods data is reduced. Therefore, in order to improve the efficiency of the management of the goods data, a coordination relationship between the goods data needs to be formed by constructing the scp supply chain plan, so as to improve the information sharing level, reduce the inventory total of the goods in the whole supply chain, reduce the cost and improve the operation performance of the whole supply chain.
At present, due to the enhancement of the uncertainty of market demands, the influence and risk of the uncertainty of demands are weakened as much as possible by the combined parties, so that some problems exist in the analysis of the goods data, for example, the relevance of the goods data is low, the classification is inaccurate, and the accuracy of the goods analysis is low; secondly, a certain risk exists in the process of constructing a supply chain plan corresponding to the goods, so that the efficiency in the process of managing the goods data is lower. Therefore, how to improve the accuracy of the analysis of the goods data and reduce the risk when constructing the supply chain plan, so as to improve the efficiency of the management of the goods data is a problem to be solved urgently.
Disclosure of Invention
The invention provides a method, a system and electronic equipment for analyzing goods data based on a scp supply chain, which mainly aim to solve the problems of how to improve the accuracy of goods data analysis and reduce the risk in constructing a supply chain plan so as to improve the efficiency of goods data management.
In order to achieve the above object, the invention provides a method for analyzing commodity data based on scp supply chain, comprising:
acquiring goods data corresponding to goods, and performing cluster analysis on the goods data to obtain the types of the goods;
analyzing commodity indexes and structure indexes according to the commodity types and the commodity data, and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes;
generating a supply chain plan according to the inventory plan and the sales plan, and performing risk assessment on the supply chain plan to obtain an assessment result;
and adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan, and managing the goods according to the standard supply chain plan.
Optionally, the performing cluster analysis on the goods data to obtain the goods category includes:
Carrying out data cleaning on the goods data to obtain cleaning data;
performing data conversion on the cleaning data to obtain conversion data;
extracting the characteristics of the conversion data to obtain conversion characteristics;
and classifying the conversion characteristics by using a preset cluster analysis algorithm to obtain the types of the goods.
Optionally, the classifying the conversion feature by using a preset cluster analysis algorithm to obtain the goods category includes:
generating a feature matrix according to the conversion features, and calculating a clustering center according to the feature matrix;
the feature matrix is expressed as:
wherein X represents the feature matrix, X 11 Representing the conversion characteristics corresponding to the 1 st row and the 1 st column, x 1j Representing the conversion characteristics corresponding to the 1 st row and the j th column, x i1 Representing the conversion characteristics corresponding to the ith row and the 1 st column, x ij Representing conversion characteristics corresponding to the ith row and the jth column;
taking the conversion features except the clustering center in the conversion features as the features to be selected, and calculating a distance matrix between the features to be selected and the clustering center;
calculating a distance matrix between the feature to be selected and the clustering center by using the following formula:
D b ＝‖x′ b -y‖
wherein D is b Representing a distance matrix, x 'corresponding to the b-th candidate feature' b Representing the b-th feature to be selected, and y represents the clustering center;
updating the feature matrix by using the distance matrix to obtain an updated matrix;
updating the feature matrix by using the following formula to obtain an updated matrix:
X′ b ＝[(D b ) 2/(α-1) ] -1
wherein X 'is' b Representing an update matrix corresponding to the b-th candidate feature, D b Representing a distance matrix corresponding to the b-th feature to be selected, wherein alpha represents a preset calculation parameter;
performing type division on the update matrix and the feature matrix by using the cluster analysis algorithm to obtain the types of goods;
the cluster analysis algorithm is expressed as:
wherein I represents the type of the goods, X' b Representing an update matrix corresponding to the b-th candidate feature, D b And (3) representing a distance matrix corresponding to the B-th feature to be selected, wherein B represents the total number of the features to be selected.
Optionally, the analyzing the commodity index and the structural index according to the commodity kind and the commodity data includes:
counting the number of the goods corresponding to the goods types, and calculating the movable sales rate and the stock-out rate according to the number of the goods;
calculating the movable sales rate and the stock-out rate by using the following formula:
wherein c represents the dynamic sales rate, h represents the stock shortage rate, E represents the stock-out number in the number of goods, f represents the initial stock-in number in the number of goods, g represents the stock-in number in the number of goods, and E represents the stock-out registration number in the number of goods;
Counting sales data corresponding to the goods data, calculating a sold-out rate and a discount rate according to the sales data, and taking the dynamic sales rate, the backout rate, the sold-out rate and the discount rate as commodity indexes;
acquiring total sales and price of the goods in the goods data, and counting the goods sales of the goods data corresponding to the goods types;
calculating the proportion of the product structure according to the sales of the goods and the total sales, and dividing the price of the goods into sections to obtain price sections;
and counting the sales of the price segment in the price interval, calculating the ratio of the price segment according to the sales of the price segment and the total sales, and taking the structural ratio of the product class and the ratio of the price segment as structural indexes.
Optionally, the making an inventory plan and a sales plan according to the commodity index and the structural index includes:
determining replenishment time and replenishment quantity according to the commodity indexes, and counting the stock quantity corresponding to the commodity data according to the replenishment time to obtain a statistical stock;
generating an inventory plan according to the replenishment quantity and the statistical inventory, and determining a sales type and a sales time period according to the structural index;
And generating a sales plan according to the sales category, the statistical inventory and the sales time period.
Optionally, the risk assessment of the supply chain plan is performed to obtain an assessment result, including:
acquiring a using facility in the supply chain plan, acquiring a historical fault event of the using facility, classifying the historical fault event to obtain a fault type, and counting an event result weight of the historical fault event corresponding to the fault type;
calculating the occurrence probability of the historical fault event, and calculating economic risk according to the occurrence probability and the event result weight;
the economic risk is calculated using the following formula:
wherein R represents the economic risk, prob p Represents the occurrence probability of the historical fault event corresponding to the p-th fault type, N p Representing event result weights corresponding to the P-th fault type, wherein P represents the total number of the fault types;
extracting a vehicle fault type from the fault types, and calculating a vehicle accident rate corresponding to the vehicle accident type;
acquiring the number of transported goods and the path length in a logistics plan of the supply chain plan, and calculating transport risks according to the number of transported goods, the path length and the vehicle accident rate;
The transportation risk is calculated using the following formula:
G＝Ar·L·Q
wherein G represents the transportation risk, ar represents the vehicle accident rate, L represents the path length, and Q represents the transportation item number;
integrating the economic risk and the transportation risk to obtain a planned risk, and judging whether the planned risk is larger than a preset standard risk value;
when the plan risk is not greater than the standard risk value, determining that the risk assessment of the supply chain plan is passed;
and when the plan risk is greater than the standard risk value, judging that the risk assessment of the supply chain plan is not passed.
Optionally, the adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan includes:
when the evaluation result is that the risk evaluation of the supply chain plan passes, the supply chain plan is taken as a standard supply chain plan;
when the evaluation result is that the risk evaluation of the supply chain plan fails, an abnormal plan in the supply chain plan is extracted, and the abnormal plan is corrected to obtain a correction plan;
and updating the supply chain plan by using the correction plan to obtain a standard supply chain plan.
Optionally, the generating a supply chain plan according to the inventory plan and the sales plan includes:
determining a production plan according to the replenishment time and the replenishment quantity in the inventory plan, and setting a logistics plan corresponding to the goods;
and integrating the production plan, the logistics plan, the inventory plan and the sales plan to obtain a supply chain plan.
In order to solve the above problems, the present invention also provides a system for analyzing commodity data based on a scp supply chain, the system comprising:
the clustering analysis module is used for acquiring goods data corresponding to goods, and performing clustering analysis on the goods data to obtain the types of the goods;
the plan making module is used for analyzing commodity indexes and structure indexes according to the commodity types and the commodity data and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes;
the risk assessment module is used for generating a supply chain plan according to the inventory plan and the sales plan, and carrying out risk assessment on the supply chain plan to obtain an assessment result;
and the plan adjustment module is used for adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan and managing the goods according to the standard supply chain plan.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the scp supply chain based item data analysis method described above.
According to the embodiment of the invention, the relevance among the goods data can be improved by carrying out cluster analysis on the goods data, and the types of the goods can be accurately obtained, so that the accuracy of the goods analysis is improved; the commodity index and the structure index can be accurately analyzed through the commodity type and the commodity data; the inventory plan and the sales plan are formulated through commodity indexes and structural indexes, so that the analysis efficiency of the goods data can be improved, and the inventory plan and the sales plan are more accurate; the supply chain plan is accurately generated through the inventory plan and the sales plan, and risk assessment is carried out on the supply chain plan, so that the risk of the supply chain plan can be reduced; the standard supply chain plan is obtained by adjusting the supply chain plan, and the goods are managed according to the standard supply chain plan, so that the efficiency of goods data management can be improved. Therefore, the method, the system and the electronic equipment for analyzing the goods data based on the scp supply chain can solve the problems of how to improve the accuracy of the goods data analysis and reduce the risk when a supply chain plan is constructed, thereby improving the efficiency of the goods data management.
Drawings
FIG. 1 is a flow chart of a method for analyzing commodity data based on a scp supply chain according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of clustering analysis of goods data to obtain kinds of goods according to an embodiment of the present application;
FIG. 3 is a flow chart of an inventory plan and a sales plan according to commodity indexes and structural indexes according to an embodiment of the present application;
FIG. 4 is a functional block diagram of a system for analyzing commodity data based on a scp supply chain according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device for implementing the method for analyzing commodity data based on scp supply chain according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a commodity data analysis method based on a scp supply chain. The execution subject of the product data analysis method based on the scp supply chain includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the commodity data analysis method based on the scp supply chain may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for analyzing commodity data based on a scp supply chain according to an embodiment of the present invention is shown. In this embodiment, the method for analyzing commodity data based on scp supply chain includes:
s1, acquiring goods data corresponding to goods, and performing cluster analysis on the goods data to obtain the types of the goods.
In the embodiment of the invention, the goods data comprise quality data, sales data and the like corresponding to the goods, wherein the quality data comprise the names of the goods, the prices of the goods, the sizes of the goods and the like; the sales data includes inventory of goods, amount of incoming goods, amount of outgoing goods, and the like.
Referring to fig. 2, in the embodiment of the present invention, the performing cluster analysis on the item data to obtain the item category includes:
s21, carrying out data cleaning on the goods data to obtain cleaning data;
s22, performing data conversion on the cleaning data to obtain converted data;
s23, extracting features of the conversion data to obtain conversion features;
s24, classifying the conversion characteristics by using a preset cluster analysis algorithm to obtain the types of the goods.
In the embodiment of the invention, the data cleaning of the goods data refers to cleaning redundant, null and ambiguous data in the goods data to obtain cleaning data; because the data format, the data range and the like in the cleaning data may be inconsistent, the cleaning data need to be completely converted into numerical data, and the numerical data is subjected to unified standardized description, so that converted data are obtained; the feature extraction of the conversion data refers to convolution, maximum pooling and full connection processing of the conversion data by using a preset neural network to obtain conversion features.
In the embodiment of the present invention, the classifying the conversion feature by using a preset cluster analysis algorithm to obtain the article category includes:
generating a feature matrix according to the conversion features, and calculating a clustering center according to the feature matrix;
taking the conversion features except the clustering center in the conversion features as the features to be selected, and calculating a distance matrix between the features to be selected and the clustering center;
updating the feature matrix by using the distance matrix to obtain an updated matrix;
and performing type division on the update matrix and the feature matrix by using the cluster analysis algorithm to obtain the types of goods.
In the embodiment of the present invention, the feature matrix is expressed as:
wherein X represents the feature matrix, X 11 Representing the conversion characteristics corresponding to the 1 st row and the 1 st column, x 1j Representing the conversion characteristics corresponding to the 1 st row and the j th column, x i1 Representing the conversion characteristics corresponding to the ith row and the 1 st column, x ij Representing the conversion characteristics corresponding to the ith row and the jth column.
In the embodiment of the invention, the clustering center is calculated by using the following formula:
wherein y represents the cluster center, X represents the feature matrix, and X a Representing the a-th conversion feature, a representing the total number of said conversion features.
In the embodiment of the invention, the distance matrix between the feature to be selected and the clustering center is calculated by using the following formula:
D b ＝‖x′ b -y‖
wherein D is b Representing a distance matrix, x 'corresponding to the b-th candidate feature' b Representing the b-th candidate feature, and y represents the cluster center.
In the embodiment of the invention, the feature matrix is updated by using the following formula to obtain an updated matrix:
X′ b ＝[(D b ) 2/(α-1) ] -1
wherein X 'is' b Representing an update matrix corresponding to the b-th candidate feature, D b And (3) representing a distance matrix corresponding to the b-th feature to be selected, wherein alpha represents a preset calculation parameter.
In the embodiment of the invention, the cluster analysis algorithm is expressed as:
wherein I represents the type of the goods, X' b Representing an update matrix corresponding to the b-th candidate feature, D b And (3) representing a distance matrix corresponding to the B-th feature to be selected, wherein B represents the total number of the features to be selected.
According to the embodiment of the invention, the goods data can be classified into the goods types such as washing and protecting types, food types and clothing types, and the goods data are classified, so that the goods data are more regular, the goods types can be accurately obtained, and the efficiency of analyzing the goods data is improved.
S2, analyzing commodity indexes and structure indexes according to the commodity types and the commodity data, and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes.
In the embodiment of the present invention, the analyzing the commodity index and the structural index according to the commodity type and the commodity data includes:
counting the number of the goods corresponding to the goods types, and calculating the movable sales rate and the stock-out rate according to the number of the goods;
counting sales data corresponding to the goods data, calculating a sold-out rate and a discount rate according to the sales data, and taking the dynamic sales rate, the backout rate, the sold-out rate and the discount rate as commodity indexes;
acquiring total sales and price of the goods in the goods data, and counting the goods sales of the goods data corresponding to the goods types;
calculating the proportion of the product structure according to the sales of the goods and the total sales, and dividing the price of the goods into sections to obtain price sections;
and counting the sales of the price segment in the price interval, calculating the ratio of the price segment according to the sales of the price segment and the total sales, and taking the structural ratio of the product class and the ratio of the price segment as structural indexes.
In the embodiment of the invention, the number of the goods can be counted in a grouping analysis mode, wherein the number of the goods comprises the stock number, the stock quantity, the sales number and the like.
In the embodiment of the invention, the dynamic sales rate and the stock-out rate are calculated by using the following formulas:
wherein c represents the movable sales rate, h represents the stock shortage rate, E represents the stock-out number in the number of goods, f represents the initial stock-in number in the number of goods, g represents the stock-in number in the number of goods, and E represents the stock-out registration number in the number of goods.
In the embodiment of the invention, the movable sales rate refers to the effective utilization rate of the commodity inventory in a period of time; the stock-out rate refers to the lack proportion of commodities in a period of time; counting sales data corresponding to the goods data by adopting a time series analysis method; calculating the sold-out rate according to the sales data, namely obtaining the sales quantity, the inventory quantity and the shipment quantity in the sales data, and calculating the ratio of the sales quantity to the inventory quantity and the shipment quantity to obtain the sold-out rate, wherein the sold-out rate is the sales speed of the goods; calculating the discount rate according to the sales data refers to obtaining the commodity sales amount and the retail price in the sales data, and calculating the ratio of the commodity sales amount to the retail price, namely the discount rate.
In the embodiment of the invention, the sales of the goods can be counted by adopting a comprehensive index method, wherein the sales of the goods refer to sales corresponding to the types of the goods in a period of time, for example, sales corresponding to washing and protecting goods in one week; calculating the ratio of the sales of the goods to the total sales, namely the ratio of the goods structure; the price of the goods can be divided into intervals according to a preset price range, for example, the price of the goods can be divided into four price intervals, and the first price interval is less than 500; the second price range is more than 500 and less than 2000; the third lattice interval is more than 2000 and less than 5000; the fourth price interval is more than 5000 price intervals; and calculating the ratio of the sales volume of the price segment corresponding to the price segment in a period of time to the total sales volume, namely the ratio of the price segment.
Referring to fig. 3, in the embodiment of the present invention, the making of an inventory plan and a sales plan according to the commodity index and the structural index includes:
s31, determining the replenishment time and the replenishment quantity according to the commodity indexes, and counting the stock quantity corresponding to the commodity data according to the replenishment time to obtain a statistical stock;
S32, generating an inventory plan according to the replenishment quantity and the statistical inventory, and determining the sales type and the sales time period according to the structural index;
s33, generating a sales plan according to the sales category, the statistical inventory and the sales time period.
In the embodiment of the invention, the out-of-stock time is determined according to the commodity index, an out-of-stock period is preset, and the out-of-stock time is ensured to be within the range of the out-of-stock period, for example, the out-of-stock time corresponding to the commodity is set to be not more than one week, so that the replenishment time is obtained; counting a plurality of inventory numbers corresponding to the types of goods before the replenishment time, namely counting the inventory, and judging whether the goods corresponding to other types of goods need replenishment or not, so as to determine the replenishment number and the replenishment type; and making an inventory plan such as warehouse-out and warehouse-in time, inventory reserve, inventory cleaning and the like according to the replenishment quantity and the statistical inventory.
In the embodiment of the invention, according to the structural index, which sales time period has the highest sales and the sales types with the highest sales and the lowest sales are analyzed, and the sales types with the highest sales and the lowest sales can be sold together or the sales types with the lowest sales can be promoted and the like in the time period with the highest sales, so that a sales plan is formed.
According to the embodiment of the invention, the commodity index and the structural index can be accurately analyzed according to the commodity type and the commodity data, so that an inventory plan and a sales plan can be accurately formulated, and the efficiency of commodity data analysis is improved.
S3, generating a supply chain plan according to the inventory plan and the sales plan, and performing risk assessment on the supply chain plan to obtain an assessment result.
In an embodiment of the present invention, the generating a supply chain plan according to the inventory plan and the sales plan includes:
determining a production plan according to the replenishment time and the replenishment quantity in the inventory plan, and setting a logistics plan corresponding to the goods;
and integrating the production plan, the logistics plan, the inventory plan and the sales plan to obtain a supply chain plan.
In the embodiment of the invention, the goods demand is formed according to the goods supplementing time and the goods supplementing quantity in the inventory plan, and the scheduling of goods purchase, production, delivery and the like, namely the production plan is carried out according to the goods demand; after the execution of the production plan is completed, the goods transportation is required, and at the moment, logistics plans such as dispatching vehicles, planning logistics routes and the like are required to be set; and connecting the production plan, the logistics plan, the inventory plan and the sales plan according to the time sequence, namely, a supply chain plan.
In an embodiment of the present invention, performing risk assessment on the supply chain plan to obtain an assessment result includes:
acquiring a using facility in the supply chain plan, acquiring a historical fault event of the using facility, classifying the historical fault event to obtain a fault type, and counting an event result weight of the historical fault event corresponding to the fault type;
calculating the occurrence probability of the historical fault event, and calculating economic risk according to the occurrence probability and the event result weight;
extracting a vehicle fault type from the fault types, and calculating a vehicle accident rate corresponding to the vehicle accident type;
acquiring the number of transported goods and the path length in a logistics plan of the supply chain plan, and calculating transport risks according to the number of transported goods, the path length and the vehicle accident rate;
integrating the economic risk and the transportation risk to obtain a planned risk, and judging whether the planned risk is larger than a preset standard risk value;
when the plan risk is not greater than the standard risk value, determining that the risk assessment of the supply chain plan is passed;
and when the plan risk is greater than the standard risk value, judging that the risk assessment of the supply chain plan is not passed.
In the embodiment of the invention, the using facility refers to equipment required to be used in the supply chain plan; the historical fault event refers to a fault that occurred while the device was in use; the historical fault events can be classified by adopting a K neighbor method; and calculating the ratio of the historical fault event in all events to obtain the occurrence probability.
In the embodiment of the invention, the economic risk is calculated by using the following formula:
wherein R represents the economic risk, prob p Represents the occurrence probability of the historical fault event corresponding to the p-th fault type, N p Indicating the event outcome weight corresponding to the p-th fault type,p represents the total number of fault types.
In the embodiment of the invention, the vehicle accidents corresponding to the vehicle accident types are counted to obtain the number of the vehicle accidents, and the proportion of the number of the vehicle accidents in the historical fault events is calculated to obtain the vehicle accident rate; the number of transported goods refers to the number of goods to be transported in the logistics plan; the path length refers to the distance of transport.
In the embodiment of the invention, the transportation risk is calculated by using the following formula:
F＝Ar·L·Q
wherein G represents the transportation risk, ar represents the vehicle accident rate, L represents the path length, and Q represents the transportation item number.
In the embodiment of the invention, integrating the economic risk and the transportation risk refers to carrying out weight assignment on the economic risk and the transportation risk to obtain the economic weight and the transportation weight, and carrying out weighted addition calculation according to the economic weight, the transportation weight, the economic risk and the transportation risk to obtain the plan risk; the standard risk value refers to a preset highest risk value, when the plan risk is greater than the standard risk value, the plan risk is too high, namely risk assessment is not passed, and at the moment, the supply chain plan needs to be adjusted so that the plan risk is reduced; and when the plan risk is not greater than the standard risk value, the plan risk is within a standard range, namely the risk assessment is passed, and the management of the goods can be continued through the supply chain.
In the embodiment of the invention, the risk can be estimated by carrying out risk assessment on the supply chain plan, so that the supply chain plan is adjusted in time, and the risk occurrence probability is reduced.
And S4, adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan, and managing the goods according to the standard supply chain plan.
In an embodiment of the present invention, the adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan includes:
when the evaluation result is that the risk evaluation of the supply chain plan passes, the supply chain plan is taken as a standard supply chain plan;
when the evaluation result is that the risk evaluation of the supply chain plan fails, an abnormal plan in the supply chain plan is extracted, and the abnormal plan is corrected to obtain a correction plan;
and updating the supply chain plan by using the correction plan to obtain a standard supply chain plan.
In the embodiment of the invention, when the evaluation result is that the risk evaluation of the supply chain plan passes, the supply chain plan can normally run, so that the supply chain plan is directly used as a standard supply chain plan; when the evaluation result is that the risk evaluation of the supply chain plan fails, a certain risk appears in the supply chain plan, and at the moment, a plurality of plans in the supply chain plan need to be subjected to risk investigation, and abnormal points in the supply chain plan, namely abnormal plans, are found, for example, the transportation risk in the supply chain plan is too high, the logistics plan is adjusted, so that the transportation risk is reduced, and a correction plan is obtained; and replacing the correction plan with an abnormal plan in the supply chain plan, so that the supply chain plan is updated, and a standard supply chain plan is obtained.
In the embodiment of the invention, the execution management of the goods according to the standard supply chain plan refers to the production management, the logistics management, the inventory management and the sales management of the goods according to the standard supply chain plan, so that the production, the supply and the sales integration of the goods are ensured, and the management efficiency of the goods and the goods data is improved.
According to the embodiment of the invention, the relevance among the goods data can be improved by carrying out cluster analysis on the goods data, and the types of the goods can be accurately obtained, so that the accuracy of the goods analysis is improved; the commodity index and the structure index can be accurately analyzed through the commodity type and the commodity data; the inventory plan and the sales plan are formulated through commodity indexes and structural indexes, so that the analysis efficiency of the goods data can be improved, and the inventory plan and the sales plan are more accurate; the supply chain plan is accurately generated through the inventory plan and the sales plan, and risk assessment is carried out on the supply chain plan, so that the risk of the supply chain plan can be reduced; the standard supply chain plan is obtained by adjusting the supply chain plan, and the goods are managed according to the standard supply chain plan, so that the efficiency of goods data management can be improved. Therefore, the invention provides the commodity data analysis method based on the scp supply chain, which can solve the problems of how to improve the accuracy of commodity data analysis, reduce the risk when constructing a supply chain plan and further improve the efficiency of commodity data management.
FIG. 4 is a functional block diagram of a system for analyzing commodity data based on a scp supply chain according to an embodiment of the present invention.
The scp supply chain-based item data analysis system 400 of the present invention may be installed in an electronic device. Depending on the functionality implemented, the scp supply chain based commodity data analysis system 400 may include a cluster analysis module 401, a planning module 402, a risk assessment module 403, and a plan adjustment module 404. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the cluster analysis module 401 is configured to obtain item data corresponding to an item, and perform cluster analysis on the item data to obtain an item type;
the plan making module 402 is configured to analyze commodity indexes and structural indexes according to the commodity types and the commodity data, and make an inventory plan and a sales plan according to the commodity indexes and the structural indexes;
the risk assessment module 403 is configured to generate a supply chain plan according to the inventory plan and the sales plan, and perform risk assessment on the supply chain plan to obtain an assessment result;
The plan adjustment module 404 is configured to adjust the supply chain plan according to the evaluation result, obtain a standard supply chain plan, and perform management on the goods according to the standard supply chain plan.
In detail, each module in the scp supply chain-based item data analysis system 400 in the embodiment of the present invention adopts the same technical means as the scp supply chain-based item data analysis method in the drawings, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a method for analyzing commodity data based on scp supply chain according to an embodiment of the present invention.
The electronic device 500 may include a processor 501, a memory 502, a communication bus 503, and a communication interface 504, and may also include a computer program stored in the memory 502 and executable on the processor 501, such as a scp supply chain based item data analysis program.
The processor 501 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 501 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 502 (e.g., executes a scp supply chain-based item data analysis program, etc.), and invokes data stored in the memory 502 to perform various functions of the electronic device and process data.
The memory 502 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 502 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 502 may also be an external storage device of the electronic device in other embodiments, for example, a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like. Further, the memory 502 may also include both internal storage units and external storage devices of the electronic device. The memory 502 may be used to store not only application software installed in an electronic device and various types of data, such as code of a commodity data analysis program based on a scp supply chain, but also temporarily store data that has been output or is to be output.
The communication bus 503 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory 502 and the at least one processor 501 etc.
The communication interface 504 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 illustrates only an electronic device having components, and it will be appreciated by those skilled in the art that the configuration illustrated in fig. 5 is not limiting of the electronic device 500 and may include fewer or more components than illustrated, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 501 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The scp supply chain based item data analysis program stored by the memory 502 in the electronic device 500 is a combination of instructions that, when executed in the processor 501, may implement:
acquiring goods data corresponding to goods, and performing cluster analysis on the goods data to obtain the types of the goods;
analyzing commodity indexes and structure indexes according to the commodity types and the commodity data, and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes;
Generating a supply chain plan according to the inventory plan and the sales plan, and performing risk assessment on the supply chain plan to obtain an assessment result;
and adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan, and managing the goods according to the standard supply chain plan.
In particular, the specific implementation method of the above instruction by the processor 501 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated with the electronic device 500 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
Acquiring goods data corresponding to goods, and performing cluster analysis on the goods data to obtain the types of the goods;
analyzing commodity indexes and structure indexes according to the commodity types and the commodity data, and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes;
generating a supply chain plan according to the inventory plan and the sales plan, and performing risk assessment on the supply chain plan to obtain an assessment result;
and adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan, and managing the goods according to the standard supply chain plan.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. A method for analyzing commodity data based on a scp supply chain, the method comprising:
Acquiring goods data corresponding to goods, and performing cluster analysis on the goods data to obtain the types of the goods;
analyzing commodity indexes and structure indexes according to the commodity types and the commodity data, and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes;
generating a supply chain plan according to the inventory plan and the sales plan, and performing risk assessment on the supply chain plan to obtain an assessment result;
and adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan, and managing the goods according to the standard supply chain plan.
2. The method for analyzing the item data based on the scp supply chain according to claim 1, wherein the performing cluster analysis on the item data to obtain the item category comprises:
carrying out data cleaning on the goods data to obtain cleaning data;
performing data conversion on the cleaning data to obtain conversion data;
extracting the characteristics of the conversion data to obtain conversion characteristics;
and classifying the conversion characteristics by using a preset cluster analysis algorithm to obtain the types of the goods.
3. The method for analyzing the commodity data based on the scp supply chain according to claim 2, wherein said classifying the transformation characteristics by using a preset cluster analysis algorithm to obtain the commodity category comprises:
Generating a feature matrix according to the conversion features, and calculating a clustering center according to the feature matrix;
the feature matrix is expressed as:
wherein X represents the feature matrix, X 11 Representing the conversion characteristics corresponding to the 1 st row and the 1 st column, x 1j Representing the conversion characteristics corresponding to the 1 st row and the j th column, x i1 Representing the conversion characteristics corresponding to the ith row and the 1 st column, x ij Representing conversion characteristics corresponding to the ith row and the jth column;
taking the conversion features except the clustering center in the conversion features as the features to be selected, and calculating a distance matrix between the features to be selected and the clustering center;
calculating a distance matrix between the feature to be selected and the clustering center by using the following formula:
D b ＝||x′ b -y||
wherein D is b Representing a distance matrix, x 'corresponding to the b-th candidate feature' b Representing the b-th feature to be selected, and y represents the clustering center;
updating the feature matrix by using the distance matrix to obtain an updated matrix;
updating the feature matrix by using the following formula to obtain an updated matrix:
X′ b ＝[(D b ) 2/(a-1) ] -1
wherein X 'is' b Representing an update matrix corresponding to the b-th candidate feature, D b Representing a distance matrix corresponding to the b-th feature to be selected, wherein alpha represents a preset calculation parameter;
performing type division on the update matrix and the feature matrix by using the cluster analysis algorithm to obtain the types of goods;
The cluster analysis algorithm is expressed as:
wherein U represents the goods category, X' b Representing an update matrix corresponding to the b-th candidate feature, D b And (3) representing a distance matrix corresponding to the B-th feature to be selected, wherein B represents the total number of the features to be selected.
4. The scp supply chain-based item data analysis method according to claim 1, wherein the analyzing the item index and the structural index according to the item type and the item data comprises:
counting the number of the goods corresponding to the goods types, and calculating the movable sales rate and the stock-out rate according to the number of the goods;
calculating the movable sales rate and the stock-out rate by using the following formula:
wherein c represents the dynamic sales rate, h represents the stock shortage rate, E represents the stock-out number in the number of goods, f represents the initial stock-in number in the number of goods, g represents the stock-in number in the number of goods, and E represents the stock-out registration number in the number of goods;
counting sales data corresponding to the goods data, calculating a sold-out rate and a discount rate according to the sales data, and taking the dynamic sales rate, the backout rate, the sold-out rate and the discount rate as commodity indexes;
acquiring total sales and price of the goods in the goods data, and counting the goods sales of the goods data corresponding to the goods types;
Calculating the proportion of the product structure according to the sales of the goods and the total sales, and dividing the price of the goods into sections to obtain price sections;
and counting the sales of the price segment in the price interval, calculating the ratio of the price segment according to the sales of the price segment and the total sales, and taking the structural ratio of the product class and the ratio of the price segment as structural indexes.
5. The scp supply chain-based item data analysis method according to claim 1, wherein the making an inventory plan and a sales plan according to the commodity index and the structural index comprises:
determining replenishment time and replenishment quantity according to the commodity indexes, and counting the stock quantity corresponding to the commodity data according to the replenishment time to obtain a statistical stock;
generating an inventory plan according to the replenishment quantity and the statistical inventory, and determining a sales type and a sales time period according to the structural index;
and generating a sales plan according to the sales category, the statistical inventory and the sales time period.
6. The scp supply chain based item data analysis method according to claim 1, wherein performing risk assessment on the supply chain plan to obtain an assessment result comprises:
Acquiring a using facility in the supply chain plan, acquiring a historical fault event of the using facility, classifying the historical fault event to obtain a fault type, and counting an event result weight of the historical fault event corresponding to the fault type;
calculating the occurrence probability of the historical fault event, and calculating economic risk according to the occurrence probability and the event result weight;
the economic risk is calculated using the following formula:
wherein R represents the economic risk, prob p Represents the occurrence probability of the historical fault event corresponding to the p-th fault type, N p Representing event result weights corresponding to the P-th fault type, wherein P represents the total number of the fault types;
extracting a vehicle fault type from the fault types, and calculating a vehicle accident rate corresponding to the vehicle accident type;
acquiring the number of transported goods and the path length in a logistics plan of the supply chain plan, and calculating transport risks according to the number of transported goods, the path length and the vehicle accident rate;
the transportation risk is calculated using the following formula:
G＝Ar·L·Q
wherein G represents the transportation risk, ar represents the vehicle accident rate, L represents the path length, and Q represents the transportation item number;
Integrating the economic risk and the transportation risk to obtain a planned risk, and judging whether the planned risk is larger than a preset standard risk value;
when the plan risk is not greater than the standard risk value, determining that the risk assessment of the supply chain plan is passed;
and when the plan risk is greater than the standard risk value, judging that the risk assessment of the supply chain plan is not passed.
7. The scp supply chain based item data analysis method according to claim 1, wherein the adjusting the supply chain plan according to the evaluation result, to obtain a standard supply chain plan, comprises:
when the evaluation result is that the risk evaluation of the supply chain plan passes, the supply chain plan is taken as a standard supply chain plan;
when the evaluation result is that the risk evaluation of the supply chain plan fails, an abnormal plan in the supply chain plan is extracted, and the abnormal plan is corrected to obtain a correction plan;
and updating the supply chain plan by using the correction plan to obtain a standard supply chain plan.
8. The scp supply chain based item data analysis method according to claim 1, wherein the generating a supply chain plan from the inventory plan and the sales plan comprises:
Determining a production plan according to the replenishment time and the replenishment quantity in the inventory plan, and setting a logistics plan corresponding to the goods;
and integrating the production plan, the logistics plan, the inventory plan and the sales plan to obtain a supply chain plan.
9. A scp supply chain based item data analysis system, the system comprising:
the clustering analysis module is used for acquiring goods data corresponding to goods, and performing clustering analysis on the goods data to obtain the types of the goods;
the plan making module is used for analyzing commodity indexes and structure indexes according to the commodity types and the commodity data and making an inventory plan and a sales plan according to the commodity indexes and the structure indexes;
the risk assessment module is used for generating a supply chain plan according to the inventory plan and the sales plan, and carrying out risk assessment on the supply chain plan to obtain an assessment result;
and the plan adjustment module is used for adjusting the supply chain plan according to the evaluation result to obtain a standard supply chain plan and managing the goods according to the standard supply chain plan.
10. An electronic device, the electronic device comprising:
At least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the scp supply chain based item data analysis method according to any of claims 1 to 8.
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