CN116596575A - Fresh product cashier control system based on artificial intelligence - Google Patents
Fresh product cashier control system based on artificial intelligence Download PDFInfo
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Abstract
The application discloses a raw and fresh product cashing control system based on artificial intelligence, which particularly relates to the field of artificial intelligence, and comprises a raw and fresh product region dividing module, a raw and fresh product information acquisition module, a raw and fresh product information preprocessing module, a raw and fresh product information processing module, a raw and fresh product information analysis module, a raw and fresh product information assessment index, a raw and fresh product cashing early warning control module and a raw and fresh product information safety supervision module.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a fresh product cashing control system based on artificial intelligence.
Background
Artificial intelligence is a new technological discipline for researching, developing theory, method, technology and application systems for simulating, extending and expanding human intelligence, and the process of outputting final results by artificial intelligence is generally completed by pattern recognition, machine learning, technology mining and intelligent algorithms. The existing fresh product cashing system automatically measures the weight and the quantity of fresh products by using a sensor and a counter, adopts a computer identification technology after calculating the correct price, and identifies the fresh products by using a camera or scanning equipment, so that the workload of operators is reduced, and the sales efficiency and the management convenience of merchant products are improved.
However, when the cash register is actually used, the cash register has some defects, such as easy damage to fresh products, some errors exist between the actual quantity of the products sold and the actual quantity of the products received and the incoming goods and the cashed goods, but due to the problem of manual operation, the cash register process cannot be monitored and checked in real time, so that the merchant is difficult to find abnormal problems in time, and the merchant receives economic loss;
the variety of fresh products of merchants is various, meanwhile, the similar products have only slight gaps, the correct identification of profit and loss is carried out on the various products, decisions are timely made according to the information fed back by the corresponding products, and certain difficulty is brought to manual operation.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present application provides an artificial intelligence based system for controlling cashing of fresh products, which is used for solving the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present application provides the following technical solutions:
fresh product region dividing module: the method is used for acquiring information of the fresh products of the target merchants, dividing the information into monitoring subareas according to the types of the fresh products of the target merchants, and marking the product type monitoring subareas of the fresh product areas of the target merchants as 1 and 2 … … n in sequence.
Fresh product information acquisition module: the system is used for collecting data information corresponding to the fresh products of each product type monitoring subarea of the fresh product area of the target merchant;
the fresh product information acquisition module comprises a fresh product cashier information acquisition unit and a fresh product state information acquisition unit.
Fresh product information preprocessing module: the system is used for receiving the data information transmitted by the fresh product information acquisition module, eliminating abnormal values in the data, and analyzing the weight loss rate of each fresh product and the collection difference rate of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant.
Fresh product information processing module: the system is used for receiving the data information transmitted by the fresh product information preprocessing module, calculating the actual loss index of each fresh product according to the weight loss rate of each fresh product and the collection difference rate of each fresh product, and calculating the income security early warning index of each fresh product according to the price of each fresh product, the commodity price of each fresh product and the collection amount of each fresh product.
Fresh product information analysis module: the system is used for receiving the data information transmitted by the fresh product information processing module, and calculating the cash-on-sale safety assessment coefficient of the fresh product according to the actual loss index of each fresh product and the income safety early warning index of each fresh product.
Fresh product information evaluation index: and the system is used for acquiring the cashing safety evaluation coefficients of the fresh products of each product type monitoring subarea of the target merchant fresh product area, and carrying out corresponding treatment after comparison analysis.
The fresh product cashing early warning control module: the method is used for extracting the actual loss index of each fresh product, the income security early warning index of each fresh product and the commodity quantity of each fresh product in each product type monitoring subarea of the target merchant, calculating to obtain the actual loss change early warning index of each fresh product and the income security change early warning index of each fresh product, and carrying out corresponding management on the fresh products according to the comparison result.
Fresh product information safety supervision module: the method is used for setting data access rights and storing the cashing security evaluation coefficient of the fresh products, the actual loss change early warning index of each fresh product and the income security change early warning index of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant.
Preferably, the specific division mode of the fresh product area division module is as follows:
determining the types of fresh products of a merchant as target areas, dividing the target areas into product type monitoring subareas according to the product types, marking the product type monitoring subareas of the target merchant as 1 and 2 … … n in sequence, acquiring the fresh product commodity intake quantity of the product type monitoring subareas of the target merchant, and marking the fresh product intake quantity as follows respectively。
Preferably, the specific collection mode of the fresh product information collection module is as follows:
the fresh product cashier information acquisition unit: the method is used for setting a weight sensor in a cashing area, collecting the total commodity weight of each fresh product, the selling weight of each fresh product and the cashing weighing weight of each fresh product in each product type monitoring subarea of a target merchant fresh product area, and respectively marking the commodity weight, the selling weight and the cashing weighing weight as followsWhere i=1, 2 … … n, i is denoted as the i-th monitoring subregion number; collecting the total commodity amount of each fresh product, the selling amount of each fresh product and the cashing amount of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant through a data collecting point, and respectively marking asWhere i=1, 2 … … n, i is denoted as the i-th monitoring subregion number;
fresh product state information acquisition unit: the method is used for setting data acquisition points, acquiring the price of each fresh product, the commodity price of each fresh product and the collection amount of each fresh product in each product type monitoring subarea of the fresh product area of a target merchant, and marking the price and collection amount of each fresh product respectivelyIs thatWhere i=1, 2 … … n, i denotes the i-th monitoring subregion number.
Preferably, the weight loss rate calculation formula of each fresh product is as follows:
wherein Kz is expressed as weight loss rate of each fresh product,/-, and>fresh products sold weight, denoted by the ith product category monitoring sub-area,/for each fresh product>The weight of each fresh product, expressed as the ith each product category monitoring sub-area, is determined by the weight of each fresh product>Total fresh product intake weight of each fresh product, denoted as the ith each product category monitoring sub-area,/for each fresh product>A compensation factor expressed as a fresh product weight difference;
the calculation formula of the collection difference rate of each fresh product is as follows:
wherein Ks is expressed as the rate of difference in collection of each fresh product,/-, and>fresh products sales amount, denoted by the ith product category monitoring sub-area,/for each fresh product>The cash amount of each fresh product, which is expressed as the ith each product type monitoring subarea,/each fresh product>Total amount of fresh products, denoted as ith each product category monitoring sub-area,/for each fresh product>Expressed as a compensation factor for the payoff differential of the fresh product.
Preferably, the calculation formula of the actual loss index of each fresh product is as follows:
wherein alpha is expressed as an actual loss index of each fresh product, kz is expressed as a weight loss rate of each fresh product, ks is expressed as a collection difference rate of each fresh product, and->、/>Expressed as the weight loss rate of each fresh product and other influencing factors of the collection difference rate of each fresh product.
Preferably, the calculation formula of the income security pre-warning index of each fresh product is as follows:
wherein beta is expressed as the income security precaution index of each fresh product, < >>Fresh product price marking, which is denoted as the i-th product category monitoring sub-area,/for each fresh product>Fresh product price of each product, denoted as the ith product category monitoring sub-area,/for each product>The amount of money collected for each fresh product, which is indicated as the ith each product category monitoring sub-area,/for each fresh product>Total fresh product intake weight of each fresh product, denoted as the ith each product category monitoring sub-area,/for each fresh product>The weight of each fresh product, expressed as the ith each product category monitoring sub-area, is determined by the weight of each fresh product>、/>Other influencing factors expressed as the income security pre-warning index of each fresh product.
Preferably, the calculation formula of the cashing security evaluation coefficient of the fresh product is as follows:
wherein θ represents a cashing safety evaluation coefficient of the fresh products, α represents an actual loss index of each fresh product, and β represents a income safety early warning index of each fresh product;
the method comprises the following steps:。
preferably, the specific evaluation mode of the fresh product information evaluation index is as follows:
acquiring a raw product cashing safety evaluation coefficient of each product type monitoring subarea of a raw product area of a target merchant, comparing the raw product cashing safety evaluation coefficient with a preset raw product cashing safety evaluation coefficient, if the raw product cashing safety evaluation coefficient is smaller than the preset raw product cashing safety evaluation coefficient, indicating that abnormal conditions exist in raw product cashing of a certain product type monitoring subarea of the raw product area of the target merchant, and early warning monitoring personnel of the area, otherwise, indicating that the raw product cashing state of the product type monitoring subarea of the raw product area of the target merchant is safe.
Preferably, the calculation formula of the actual loss change early warning index of each fresh product is as follows:
wherein->The early warning index of the actual loss of each fresh product is expressed, and alpha is expressed as the actual loss index of each fresh product,/I>Expressed as the number of orders for fresh products.
Preferably, the calculation formula of the early warning index of the safety change of the income of each fresh product is as follows:
wherein->Expressed as the early warning index of the income security change of each fresh product, and beta expressed as the early warning index of the income security of each fresh product,/for>Expressed as the number of orders for fresh products.
The application has the technical effects and advantages that:
1. the application provides a raw fresh product cashing control system based on artificial intelligence, which is characterized in that raw fresh product cashing information and raw fresh product state information of each product type monitoring subarea of a target merchant raw fresh product area are collected, data are preprocessed and analyzed to obtain actual loss indexes of each raw fresh product and income security early warning indexes of each raw fresh product, the raw fresh product cashing security assessment coefficients are further analyzed and obtained, the raw fresh product cashing security assessment coefficients are compared with preset raw fresh product cashing security assessment coefficients, if the raw fresh product cashing security assessment coefficients are smaller than the preset raw fresh product cashing security assessment coefficients, abnormal conditions of raw fresh product cashing of a certain product type monitoring subarea of the target merchant raw fresh product area are indicated, monitoring personnel of the area are warned, and cashing process is monitored and checked in real time through an artificial intelligence technology, so that merchants can find abnormal problems in time, and economic losses are avoided;
2. according to the method, the actual loss index of each fresh product and the income security pre-warning index of each fresh product in each product type monitoring subarea of the target merchant are extracted, the actual loss change pre-warning index of each fresh product and the income security change pre-warning index of each fresh product are obtained through analysis, deep processing and mining of data are realized, accurate feedback of different types of fresh products is realized, decisions are made in time, intelligent and accurate management of the fresh products by merchants is realized, and shopping requirements of consumers are further met.
Drawings
FIG. 1 is a schematic diagram of a system module flow connection according to the present application.
Fig. 2 is a schematic diagram of a fresh product information acquisition module according to the present application.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the application provides a system for controlling cashing of fresh products based on artificial intelligence, which comprises a fresh product region dividing module, a fresh product information collecting module, a fresh product information preprocessing module, a fresh product information processing module, a fresh product information analyzing module, a fresh product information assessment index, a fresh product cashing early warning control module and a fresh product information safety supervision module.
The system comprises a fresh product region dividing module, a fresh product information collecting module, a fresh product information preprocessing module, a fresh product information analyzing module, a fresh product cashing early warning control module, a fresh product information evaluating index, a fresh product information safety supervision module and a fresh product cashing early warning control module.
The fresh product area dividing module is used for acquiring information of fresh products of target merchants, dividing the information into monitoring subareas according to the types of the fresh products of the target merchants, and marking the monitoring subareas of the fresh product areas of the target merchants as 1 and 2 … … n in sequence.
In one possible design, the specific division mode of the fresh product area division module is as follows:
determining the types of fresh products of a merchant as target areas, dividing the target areas into product type monitoring subareas according to the product types, marking the product type monitoring subareas of the target merchant as 1 and 2 … … n in sequence, acquiring the fresh product commodity intake quantity of the product type monitoring subareas of the target merchant, and marking the fresh product intake quantity as follows respectively。
The fresh product information acquisition module is used for acquiring data information corresponding to fresh products of each product type monitoring subarea of the fresh product area of the target merchant;
referring to fig. 2, the fresh product information acquisition module includes a fresh product cashier information acquisition unit and a fresh product status information acquisition unit.
In one possible design, the specific collection mode of the fresh product information collection module is as follows:
the fresh product cashier information acquisition unit: the method is used for setting a weight sensor in a cashing area, collecting the total commodity weight of each fresh product, the selling weight of each fresh product and the cashing weighing weight of each fresh product in each product type monitoring subarea of a target merchant fresh product area, and respectively marking the commodity weight, the selling weight and the cashing weighing weight as followsWhere i=1, 2 … … n, i is denoted as the i-th monitoring subregion number; collecting the total commodity amount of each fresh product, the selling amount of each fresh product and the cashing amount of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant through a data collecting point, and respectively marking asWhere i=1, 2 … … n, i is denoted as the i-th monitoring subregion number;
fresh product state information acquisition unit: the method is used for setting data acquisition points, and acquiring the price of each fresh product, the commodity price of each fresh product and the collection amount of each fresh product in each product type monitoring subarea of the fresh product area of a target merchant, and is respectively marked asWhere i=1, 2 … … n, i denotes the i-th monitoring subregion number.
The fresh product information preprocessing module is used for receiving the data information transmitted by the fresh product information acquisition module, eliminating abnormal values in the data, and analyzing the weight loss rate of each fresh product and the collection difference rate of each fresh product of each product type monitoring subarea of the fresh product area of the target merchant.
In one possible design, the weight loss rate of each fresh product is calculated by the following formula:
wherein Kz is expressed as weight loss rate of each fresh product,/-, and>fresh products sold weight, denoted by the ith product category monitoring sub-area,/for each fresh product>The weight of each fresh product, expressed as the ith each product category monitoring sub-area, is determined by the weight of each fresh product>Total fresh product intake weight of each fresh product, denoted as the ith each product category monitoring sub-area,/for each fresh product>A compensation factor expressed as a fresh product weight difference;
in the embodiment, by comparing Kz with 1%, if Kz is greater than 1%, it is indicated that the weight difference of a product type monitoring subarea of a fresh product area of a target merchant is abnormal, and the area manager should be immediately notified to check the sales condition of the fresh product;
the calculation formula of the collection difference rate of each fresh product is as follows:
wherein Ks is expressed as the rate of difference in collection of each fresh product,/-, and>fresh products sales amount, denoted by the ith product category monitoring sub-area,/for each fresh product>The cash amount of each fresh product, which is expressed as the ith each product type monitoring subarea,/each fresh product>Total amount of fresh products, denoted as ith each product category monitoring sub-area,/for each fresh product>A compensation factor expressed as a fresh product collection balance;
in this embodiment, by comparing Ks with 1%, if Ks is greater than 1%, it is indicated that there is a difference in collection of a product type monitoring sub-area in the fresh product area of the target merchant, and the area manager should be immediately notified to check the collection amount.
The fresh product information processing module is used for receiving the data information transmitted by the fresh product information preprocessing module, calculating to obtain the actual loss index of each fresh product through the weight loss rate of each fresh product and the collection difference rate of each fresh product, and calculating to obtain the income security early warning index of each fresh product through the price of each fresh product, the commodity price of each fresh product and the collection amount of each fresh product.
In one possible design, the calculation formula of the actual loss index of each fresh product is as follows:
wherein alpha is expressed as an actual loss index of each fresh product, kz is expressed as a weight loss rate of each fresh product, ks is expressed as a collection difference rate of each fresh product, and->、/>Expressed as the weight loss rate of each fresh product and other influencing factors of the collection difference rate of each fresh product.
The calculation formula of the income security early warning index of each fresh product is as follows:
wherein beta is expressed as the income security precaution index of each fresh product, < >>Fresh product price marking, which is denoted as the i-th product category monitoring sub-area,/for each fresh product>Fresh product price of each product, denoted as the ith product category monitoring sub-area,/for each product>The amount of money collected for each fresh product, which is indicated as the ith each product category monitoring sub-area,/for each fresh product>Total fresh product intake weight of each fresh product, denoted as the ith each product category monitoring sub-area,/for each fresh product>The weight of each fresh product, expressed as the ith each product category monitoring sub-area, is determined by the weight of each fresh product>、/>Other influencing factors expressed as the income security pre-warning index of each fresh product.
The fresh product information analysis module is used for receiving the data information transmitted by the fresh product information processing module, and calculating the cash-on-sale safety assessment coefficient of the fresh product through the actual loss index of each fresh product and the income safety early warning index of each fresh product.
In one possible design, the calculation formula of the cashing security evaluation coefficient of the fresh product is as follows:
wherein θ represents a cashing safety evaluation coefficient of the fresh products, α represents an actual loss index of each fresh product, and β represents a income safety early warning index of each fresh product;
the method comprises the following steps:。
the fresh product information evaluation index is used for acquiring the fresh product cashing security evaluation coefficients of all product category monitoring subareas of the fresh product area of the target merchant, and the corresponding processing is carried out after the comparison analysis.
In one possible design, the specific evaluation mode of the fresh product information evaluation index is as follows:
acquiring a raw product cashing safety evaluation coefficient of each product type monitoring subarea of a raw product area of a target merchant, comparing the raw product cashing safety evaluation coefficient with a preset raw product cashing safety evaluation coefficient, if the raw product cashing safety evaluation coefficient is smaller than the preset raw product cashing safety evaluation coefficient, indicating that abnormal conditions exist in raw product cashing of a certain product type monitoring subarea of the raw product area of the target merchant, and early warning monitoring personnel of the area, otherwise, indicating that the raw product cashing state of the product type monitoring subarea of the raw product area of the target merchant is safe.
The raw fresh product cashing early warning control module is used for extracting actual loss indexes of the raw fresh products, income safety early warning indexes of the raw fresh products and the commodity intake quantity of the raw fresh products in each product type monitoring subarea of the raw fresh product area of the target merchant, calculating to obtain actual loss change early warning indexes of the raw fresh products and income safety change early warning indexes of the raw fresh products, and managing the raw fresh products correspondingly according to comparison results.
In one possible design, the calculation formula of the actual loss variation early warning index of each fresh product is as follows:
wherein->The early warning index of the actual loss of each fresh product is expressed, and alpha is expressed as the actual loss index of each fresh product,/I>Expressed as fresh product stock quantity;
the calculation formula of the early warning index of the income security change of each fresh product is as follows:
wherein->Expressed as the early warning index of the income security change of each fresh product, and beta expressed as the early warning index of the income security of each fresh product,/for>Expressed as the number of orders for fresh products.
In the preferred technical scheme of the application, the fresh product cashing early warning control module further comprises:
comparing the actual loss change early-warning index of each fresh product with the standard actual loss change early-warning index of each fresh product, if the actual loss change early-warning index of each fresh product is larger than the standard actual loss change early-warning index of each fresh product, indicating that the price and the quantity of the fresh products in each product type monitoring subarea of the fresh product area of the target merchant are abnormal, and sending a loss early-warning instruction to a manager; comparing the early warning index of the income security change of each fresh product with the early warning index of the income security change of each fresh product, if the early warning index of the income security change of each fresh product is smaller than the early warning index of the income security change of each fresh product, the early warning index of the income security change of each fresh product indicates that the income abnormality risk exists in each product type monitoring subarea of the fresh product area of the target merchant, and a manager is informed to carry out abnormality monitoring on the area.
The fresh product information safety supervision module is used for setting data access authority and storing a fresh product cashing safety evaluation coefficient, an actual loss change early warning index and a income safety change early warning index of each fresh product of each product type monitoring subarea of a fresh product area of a target merchant.
In this embodiment, it needs to be specifically explained that, by collecting the cashing information of the fresh products and the state information of the fresh products in each product type monitoring sub-area of the target merchant, preprocessing the data, analyzing the preprocessed data to obtain the actual loss index of each fresh product and the income security early warning index of each fresh product, further analyzing to obtain the cashing security evaluation coefficient of each fresh product, comparing the cashing security evaluation coefficient of each fresh product with the preset cashing security evaluation coefficient of each fresh product, if the cashing security evaluation coefficient of each fresh product is smaller than the preset cashing security evaluation coefficient of each fresh product, indicating that the cashing of the fresh product in a certain product type monitoring sub-area of the target merchant has abnormal conditions, and early warning the monitoring personnel in the area, and monitoring and auditing the cashing process in real time by the artificial intelligence technology, so that the merchant can find abnormal problems in time, and avoid economic losses;
according to the method, the actual loss index of each fresh product and the income security pre-warning index of each fresh product in each product type monitoring subarea of the target merchant are extracted, the actual loss change pre-warning index of each fresh product and the income security change pre-warning index of each fresh product are obtained through analysis, deep processing and mining of data are realized, accurate feedback of different types of fresh products is realized, decisions are made in time, intelligent and accurate management of the fresh products by merchants is realized, and shopping requirements of consumers are further met.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (10)
1. An artificial intelligence based fresh product cashier control system, which is characterized by comprising:
fresh product region dividing module: the method comprises the steps of obtaining information of a target merchant fresh product, dividing the information into monitoring subareas according to the types of the target merchant fresh product, and marking the product type monitoring subareas of the target merchant fresh product area as 1 and 2 … … n in sequence;
fresh product information acquisition module: the system is used for collecting data information corresponding to the fresh products of each product type monitoring subarea of the fresh product area of the target merchant;
the fresh product information acquisition module comprises a fresh product cashier information acquisition unit and a fresh product state information acquisition unit;
fresh product information preprocessing module: is used for receiving the data information transmitted by the fresh product information acquisition module, eliminating the abnormal value in the data, analyzing the weight loss rate of each fresh product and the collection difference rate of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant;
fresh product information processing module: is used for receiving the data information transmitted by the fresh product information preprocessing module, calculating the actual loss index of each fresh product through the weight loss rate of each fresh product and the collection difference rate of each fresh product, calculating the income security early warning index of each fresh product according to the price of each fresh product, the commodity price of each fresh product and the collection amount of each fresh product;
fresh product information analysis module: the system comprises a data information processing module, a price information processing module and a price information processing module, wherein the data information is used for receiving data information transmitted by the price information processing module, and a price information processing module is used for receiving price information of the price information processing module and obtaining a price information processing module through calculation of the actual loss index of each price product and the price information processing module;
fresh product information evaluation index: the method comprises the steps of obtaining a cashier safety evaluation coefficient of the fresh product of each product type monitoring subarea of a target merchant, and carrying out corresponding treatment after comparison analysis;
the fresh product cashing early warning control module: the method comprises the steps of extracting actual loss indexes of fresh products, income security early warning indexes of the fresh products and the commodity quantity of the fresh products in each product type monitoring subarea of a target merchant, calculating to obtain actual loss change early warning indexes of the fresh products and income security change early warning indexes of the fresh products, and carrying out corresponding management on the fresh products according to comparison results;
fresh product information safety supervision module: the method is used for setting data access rights and storing the cashing security evaluation coefficient of the fresh products, the actual loss change early warning index of each fresh product and the income security change early warning index of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant.
2. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the specific division mode of the fresh product area division module is as follows:
determining the fresh product types of merchants as target areas, dividing the target areas into product type monitoring subareas according to the product types, and dividing the product types of the fresh product areas of the target merchantsThe monitoring subareas are marked as 1 and 2 … … n in sequence, the fresh product stock quantity of each product type monitoring subarea of the fresh product area of the target merchant is obtained, and the fresh product stock quantity is respectively marked as。
3. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the specific collection mode of the fresh product information collection module is as follows:
the fresh product cashier information acquisition unit: the method is used for setting a weight sensor in a cashing area, collecting the total commodity weight of each fresh product, the selling weight of each fresh product and the cashing weighing weight of each fresh product in each product type monitoring subarea of a target merchant fresh product area, and respectively marking the commodity weight, the selling weight and the cashing weighing weight as followsWhere i=1, 2 … … n, i is denoted as the i-th monitoring subregion number; collecting the total commodity amount of each fresh product, the selling amount of each fresh product and the cashing amount of each fresh product in each product type monitoring subarea of the fresh product area of the target merchant through a data collecting point, and marking the commodity amount and the cashing amount as +.>Where i=1, 2 … … n, i is denoted as the i-th monitoring subregion number;
fresh product state information acquisition unit: the method is used for setting data acquisition points, and acquiring the price of each fresh product, the commodity price of each fresh product and the collection amount of each fresh product in each product type monitoring subarea of the fresh product area of a target merchant, and is respectively marked asWhere i=1, 2 … … n, i denotes the i-th monitoring subregion number.
4. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the weight loss rate calculation formula of each fresh product is as follows:
wherein Kz is expressed as weight loss rate of each fresh product,/-, and>fresh products sold weight, denoted by the ith product category monitoring sub-area,/for each fresh product>The weight of each fresh product, expressed as the ith each product category monitoring sub-area, is determined by the weight of each fresh product>Total fresh product intake weight of each fresh product, denoted as the ith each product category monitoring sub-area,/for each fresh product>A compensation factor expressed as a fresh product weight difference;
the calculation formula of the collection difference rate of each fresh product is as follows:
wherein Ks is expressed as the rate of difference in collection of each fresh product,/-, and>fresh products sales amount, denoted by the ith product category monitoring sub-area,/for each fresh product>The cash amount of each fresh product, which is expressed as the ith each product type monitoring subarea,/each fresh product>The total commodity amount of each fresh product represented as the ith each product category monitoring sub-area,expressed as a compensation factor for the payoff differential of the fresh product.
5. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the calculation formula of the actual loss index of each fresh product is as follows:
wherein alpha is expressed as an actual loss index of each fresh product, kz is expressed as a weight loss rate of each fresh product, ks is expressed as a collection difference rate of each fresh product, and->、/>Expressed as the weight loss rate of each fresh product and other influencing factors of the collection difference rate of each fresh product.
6. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the calculation formula of the income security early warning index of each fresh product is as follows:
wherein beta is expressed as the income security precaution index of each fresh product, < >>Fresh product price marking, which is denoted as the i-th product category monitoring sub-area,/for each fresh product>Fresh product price of each product, denoted as the ith product category monitoring sub-area,/for each product>The amount of money collected for each fresh product, which is indicated as the ith each product category monitoring sub-area,/for each fresh product>Total fresh product intake weight of each fresh product, denoted as the ith each product category monitoring sub-area,/for each fresh product>The weight of each fresh product, expressed as the ith each product category monitoring sub-area, is determined by the weight of each fresh product>、/>Other influencing factors expressed as the income security pre-warning index of each fresh product.
7. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the calculation formula of the cashing safety evaluation coefficient of the fresh product is as follows:
wherein θ represents a cashing safety evaluation coefficient of the fresh products, α represents an actual loss index of each fresh product, and β represents a income safety early warning index of each fresh product;
the method comprises the following steps:。
8. the artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the specific evaluation mode of the fresh product information evaluation index is as follows:
acquiring a raw product cashing safety evaluation coefficient of each product type monitoring subarea of a raw product area of a target merchant, comparing the raw product cashing safety evaluation coefficient with a preset raw product cashing safety evaluation coefficient, if the raw product cashing safety evaluation coefficient is smaller than the preset raw product cashing safety evaluation coefficient, indicating that abnormal conditions exist in raw product cashing of a certain product type monitoring subarea of the raw product area of the target merchant, and early warning monitoring personnel of the area, otherwise, indicating that the raw product cashing state of the product type monitoring subarea of the raw product area of the target merchant is safe.
9. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the calculation formula of the actual loss change early warning index of each fresh product is as follows:
wherein->The early warning index of the actual loss of each fresh product is expressed, and alpha is expressed as the actual loss index of each fresh product,/I>Expressed as the number of orders for fresh products.
10. The artificial intelligence-based fresh product cashier control system according to claim 1, wherein: the calculation formula of the early warning index of the income security change of each fresh product is as follows:
wherein->Expressed as the early warning index of the income security change of each fresh product, and beta expressed as the early warning index of the income security of each fresh product,/for>Expressed as the number of orders for fresh products.
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