CN116823190A - Cashier intelligent management device and cashier intelligent management method - Google Patents
Cashier intelligent management device and cashier intelligent management method Download PDFInfo
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Abstract
The invention provides a cashing intelligent management device and method, which belongs to the technical field of data management and specifically comprises the following steps: determining a private cashing probability evaluation value of cashing staff through the historical private cashing times of the cashing staff, the historical private cashing frequency and the latest historical private cashing time; the method comprises the steps of determining an analysis period through a private cashing analysis frequency, determining cashing evaluation amounts of cashing personnel through the number of the shopping personnel corresponding to the analysis period, cashing time and settlement interval time of different shopping personnel, and determining a private cashing analysis sequence of the cashing personnel by combining a private cashing probability evaluation value of the cashing personnel, so that the efficiency and the accuracy of the private cashing analysis of the cashing personnel are improved.
Description
Technical Field
The invention belongs to the technical field of data management, and particularly relates to a cashing intelligent management device and method.
Background
In business occasions such as supermarkets or canteens, cash register processing is generally realized by arranging a plurality of cash register devices, but in actual production, private cash register situations exist for part of cash register personnel, so that the benefits of enterprises are greatly damaged, and a plurality of identification methods for carrying out private cash register of the cash register personnel are provided in the prior art.
In order to realize the identification of private cashing of cashing staff, n stores with highest potential private cashing probability are selected as first target stores in an invention patent CN116011822A (a store cashing management method, device, computer equipment and storage medium), and private cashing tool identification is performed on the first target stores by using a private cashing tool identification model so as to judge whether the corresponding stores have private cashing conditions or not, but the following technical problems exist:
in single store often there are many receipts silver-colored device, even if there is only single receipts silver-colored device, also there are a plurality of receipts silver-colored personnel generally, and the private receipts silver-colored condition often gets in touch with receipts silver-colored personnel simultaneously, consequently if can not carry out the analysis according to receipts silver-colored personnel's potential private probability, then can not in time accurate realization to the discernment of private receipts silver-colored condition.
The analysis sequence of cashier is determined without considering the historical private cashier and the checkout number of the cashier, specifically, when the cashier with the private cashier exists in the history, the probability of private cashier is obviously larger than that of the cashier without the private cashier, and meanwhile, when the checkout number of the cashier is more, the analysis complexity is higher, so that if the factors cannot be combined, the analysis and the determination of the private cashier cannot be realized timely.
Aiming at the technical problems, the invention provides a cashing intelligent management device and a cashing intelligent management method.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the invention, a cashier intelligent management method is provided.
The intelligent cashier management method is characterized by comprising the following steps of:
s11, acquiring the number of cash registers and the number of cashiers in a store, and determining private cashier analysis frequency of the store by combining the number of shoppers and commodity sales of the store;
s12, checking the inventory commodity of the store based on the private cashing analysis frequency to obtain commodity checking data, and entering the next step when the private cashing condition is determined to exist by combining cashing data of a cash register;
s13, determining a private cashing probability evaluation value of the cashing personnel according to the historical private cashing times, the historical private cashing frequency and the latest historical private cashing time of the cashing personnel, judging whether the private cashing probability evaluation value of the cashing personnel is larger than a preset probability, if so, setting the analysis priority of the cashing personnel as a first priority, and determining a private cashing analysis sequence according to the private cashing probability evaluation value of the cashing personnel, otherwise, entering the next step;
S14, determining an analysis period through the private cashing analysis frequency, setting the analysis priority of the cashing personnel as a second priority, determining the cashing evaluation quantity of the cashing personnel through the number of the shopping personnel corresponding to the analysis period, cashing time and settlement interval time of different shopping personnel, and determining the private cashing analysis sequence of the cashing personnel by combining the private cashing probability evaluation value of the cashing personnel.
The method comprises the following steps that the number of shoppers and commodity sales in a store are determined according to cash register records of the store, and specifically according to cash register records of the store in a preset period.
The further technical scheme is that the preset period is determined according to the daily average people flow of the store and the commodity number of the store, wherein the larger the daily average people flow of the store is, the more the commodity number of the store is, the shorter the preset period is.
The further technical scheme is that the private cashing situation exists by combining cashing data of the cash register, and the method specifically comprises the following steps:
and determining commodity sales volume data of the store according to the commodity inventory data, determining commodity sales record data of the store according to the cashing data of the cash register, and determining whether private cashing exists according to the commodity sales volume data of the store and the commodity sales record data of the store.
The further technical scheme is that the value range of the private cashing probability evaluation value of the cashing staff is between 0 and 1, wherein when the preset probability is determined according to the number of the cashing staff and the distribution condition of the private cashing probability evaluation value of the cashing staff, specifically, the analysis priority of the cashing staff is that the number of the staff with the first priority is not more than half of the number of the cashing staff.
The further technical scheme is that when the analysis priority of the cashier is the second priority, the method for determining the private cashier analysis sequence of the cashier is as follows:
and carrying out the determination of the private cashing analysis sequence of the cashing personnel by combining the private cashing probability evaluation value of the cashing personnel.
When the cashing evaluation amount of the cashing personnel is larger than the set evaluation amount, determining the private cashing analysis sequence of the cashing personnel according to the cashing evaluation amount of the cashing personnel;
and when the cashier evaluation value of the cashier is not greater than the set evaluation value, determining an analysis sequence value of the cashier through the cashier evaluation value of the cashier and the private cashier probability evaluation value, and determining a private cashier analysis sequence of the cashier according to the analysis sequence value of the cashier.
The second aspect of the present invention provides a cashier intelligent management device, which adopts the cashier intelligent management method, and is characterized in that the cashier intelligent management device specifically comprises:
a frequency determination module; a private cash analysis module;
the privacy cashing analysis module comprises a privacy cashing confirmation module, a probability evaluation module, an evaluation amount determination module and an analysis sequence determination module;
the frequency determining module is responsible for acquiring the number of cash registers and the number of cashiers of the store, and determining the private cashier analysis frequency of the store by combining the number of shopping personnel and commodity sales of the store;
the private cashing confirmation module is responsible for counting inventory commodities of the store based on the private cashing analysis frequency to obtain commodity counting data, and determining whether a private cashing condition exists according to cashing data of a cash register;
the probability evaluation module is responsible for determining a private cashing probability evaluation value of the cashing personnel through the historical private cashing times of the cashing personnel, the historical private cashing frequency and the latest historical private cashing time;
the evaluation determining module is responsible for giving the private cashing analysis frequency to determine an analysis period, setting the analysis priority of the cashing personnel as a second priority, and determining the cashing evaluation quantity of the cashing personnel according to the number of the shopping personnel, the cashing time and the settlement interval time of different shopping personnel corresponding to the analysis period;
The analysis sequence determination module is responsible for determining the private cashing analysis sequence of the cashing personnel through the cashing evaluation value of the cashing personnel and the private cashing probability evaluation value of the cashing personnel.
In a third aspect, the present application provides a computer apparatus comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and executing the intelligent cashing management method when the processor runs the computer program.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, where the computer program, when executed in a computer, causes the computer to execute a cashier intelligent management method as described above.
The application has the beneficial effects that:
the private cashing analysis frequency of the store is determined according to the number of cash registers, cashier, shopping personnel and commodity sales, so that the private cashing analysis frequency is determined from the angles of the shopping personnel and commodity sales, the cash registers and the cashier, and the analysis frequency of the store with higher analysis difficulty and the store with lower analysis difficulty is ensured, and the timeliness of analysis of the store with higher private cashing probability is also ensured.
The method has the advantages that the number of times of historical private cashing of cashing staff, the frequency of the historical private cashing and the time of the latest historical private cashing are used for determining the evaluation value of the probability of cashing staff, so that differentiation of cashing staff from data of the historical private cashing is realized, the timeliness of analysis of cashing staff with serious private cashing in history is guaranteed, and meanwhile the analysis efficiency is guaranteed.
The method has the advantages that the private cashing analysis sequence of cashing staff is determined through cashing evaluation values of cashing staff and private cashing probability evaluation values of cashing staff, the severity of historical private cashing of cashing staff is considered, meanwhile, the difference of the analysis difficulty of cashing staff is also considered, meanwhile, further identification of abnormal private cashing conditions is achieved through settlement interval time of different shopping staff, and differential analysis of different cashing staff is achieved.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a method of intelligently managing cashier;
FIG. 2 is a flow chart of a method of determining a frequency of a store's private cashier analysis;
FIG. 3 is a flow chart of a method of determining a privacy cashing probability assessment value for a cashier;
FIG. 4 is a flow chart of a method of determining a cashier assessment of a cashier;
fig. 5 is a frame diagram of a cashier intelligent management device;
fig. 6 is a frame diagram of a computer device.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
The applicant finds that the prior art scheme for monitoring and managing the private cashes lacks of an efficient processing method, particularly when the number of charging windows in schools and the like is large, the efficiency of monitoring and analyzing the private cashes is low, so that how to realize differentiated analysis and improve analysis efficiency become the technical problem to be solved urgently.
In order to solve the technical problems, the applicant provides the following technical scheme:
firstly, determining private cashing analysis frequency of a store according to data such as the number of cash registers, the number of cashiers, the number of shoppers and commodity sales, so as to realize the determination of the private cashing analysis frequency of a differentiated store, wherein the highest analysis frequency corresponding to the number of cash registers, the number of cashiers, the number of shoppers and commodity sales can be determined according to the analysis frequency of 2 days to 1 week according to the difference of the number of cash registers, the number of cashiers, the number of shoppers and commodity sales;
counting the commodity according to the private cashing analysis frequency, and entering the next step when the private cashing exists according to the counting result and the sales result;
Because whether different cashiers have the private cashier and other conditions are different, the analysis priority of the cashiers with the private cashier for nearly one month can be set to be the first priority, and other personnel are set to be the second priority, and other personnel have larger difference in cashier number and busyness, so that analysis is performed on low busyness and fewer cashiers preferentially, and analysis processing efficiency is improved.
The foregoing aspects are described in detail with reference to specific data and specific embodiments, including method class embodiments, apparatus class embodiments, and medium class embodiments
In order to solve the above problems, according to one aspect of the present invention, as shown in fig. 1, there is provided an intelligent cashing management method according to one aspect of the present invention, which is characterized by comprising:
s11, acquiring the number of cash registers and the number of cashiers in a store, and determining private cashier analysis frequency of the store by combining the number of shoppers and commodity sales of the store;
in this embodiment, the determination of the private cashing analysis frequency of the store is realized through the commodity selling condition and the cashing setting condition of the store, so that different analysis modes are adopted for the analysis of different private cashing, and the safety and reliability of data are ensured.
Specifically, the number of shoppers and the commodity sales amount in the store are determined according to the cashier records of the cash registers in the store, and specifically, the cashier records of the cash registers in the store in a preset period.
In this embodiment, the data may be obtained through a cash register of a store, and generally, the value of the preset period is 1 day or 20 days or whole month, so that stability and correctness of the data are ensured.
In one possible embodiment, as shown in fig. 2, the method for determining the private cashing analysis frequency of the store in step S11 is as follows:
s21, acquiring the number of the shoppers and the commodity sales volume of the store, and determining the set analysis frequency of the private cashing analysis of the store according to the number of the shoppers and the commodity sales volume of the store;
the number of shoppers and the sales of goods in a store are generally determined by the number of shoppers and the sales of goods in a certain period of time, such as the last week or month.
Specifically, the determining of the set analysis frequency of the private cashing analysis of the store according to the number of shoppers and commodity sales amount of the store specifically includes:
Determining the shopper demand frequency of private cashing analysis of the store by the number of shoppers of the store;
determining commodity demand frequency through private cashing analysis of the store by commodity sales volume of the store;
and determining the set analysis frequency of the private cashing analysis of the store according to the commodity demand frequency of the private cashing analysis of the store and the shopper demand frequency of the private cashing analysis of the store.
In another possible embodiment, the determination of the set analysis frequency may be implemented in the form of a look-up table, as shown in table 1:
table 1 determination table of set analysis frequency of private cash analysis of store
S22, acquiring the number of cashiers in the store, determining the cashiers and general cashiers in complex analysis according to the cashier time in a preset period, determining the analysis complexity of the cashiers according to the number of the cashiers and the cashier time in the preset period, the general cashiers and the cashier time in the preset period, determining whether the set analysis frequency of the store meets the requirement according to the analysis complexity of the cashiers, if so, entering the next step, and if not, entering the step S24;
The preset period is determined according to the daily people flow rate of the store and the commodity number of the store, wherein the larger the daily people flow rate of the store is, the larger the commodity number of the store is, and the shorter the preset period is.
In the invention, the preset period can be selected to be between 1 and 2 weeks, and the preset period can be dynamically adjusted according to the actual data of the store.
S23, determining a complex analysis cash register and a general cash register according to cash register time of the store in a preset period, determining analysis complexity of the cash register according to the number of the complex analysis cash registers, cash register time of the store in the preset period, general cash register number and cash register time of the store in the preset period, determining whether set analysis frequency of the store meets requirements or not according to the analysis complexity of the cash register, if yes, determining the set analysis frequency of private cash analysis of the store to the private cash analysis frequency of the store, and if no, entering step S24;
s24, acquiring historical private cashing data of the store, determining the private cashing times and the number of private cashing staff in the history of the store according to the historical private cashing data of the store, determining the analysis frequency correction of the store according to the analysis complexity of the cash register and the analysis complexity of the cashing staff, and determining the private cashing analysis frequency of the store according to the analysis frequency correction of the store and the set analysis frequency of the store.
In this embodiment, the private cashing analysis frequency of the store is determined according to the number of cash registers, cashier, shopper and commodity sales, so that the private cashing analysis frequency is determined from the angles of the shopper and commodity sales, the cash registers and the cashier, and the store with higher analysis difficulty and the store with lower analysis difficulty are ensured to adopt different analysis frequencies, and the timeliness of the analysis of the store with higher private cashing probability is also ensured.
S12, checking the inventory commodity of the store based on the private cashing analysis frequency to obtain commodity checking data, and entering the next step when the private cashing condition is determined to exist by combining cashing data of a cash register;
it should be noted that, determining that a private cashing situation exists by combining cashing data of a cash register specifically includes:
and determining commodity sales volume data of the store according to the commodity inventory data, determining commodity sales record data of the store according to the cashing data of the cash register, and determining whether private cashing exists according to the commodity sales volume data of the store and the commodity sales record data of the store.
S13, determining a private cashing probability evaluation value of the cashing personnel according to the historical private cashing times, the historical private cashing frequency and the latest historical private cashing time of the cashing personnel, judging whether the private cashing probability evaluation value of the cashing personnel is larger than a preset probability, if so, setting the analysis priority of the cashing personnel as a first priority, and determining a private cashing analysis sequence according to the private cashing probability evaluation value of the cashing personnel, otherwise, entering the next step;
in the embodiment, by combining the historical private cashing situation of cashing personnel, the accurate screening of cashing personnel with private cashing situation is realized, so that the differentiated analysis sequence is realized, and the analysis efficiency is improved.
As shown in fig. 3, the method for determining the evaluation value of the private cashing probability of the cashier includes:
s31, acquiring the historical private cashing times of cashier, determining whether the analysis priority of the cashier is a first priority according to the historical private cashing times of the cashier, if so, determining the analysis priority of the cashier as the first priority, determining the private cashing probability evaluation value of the cashier according to the historical private cashing times of the cashier, and if not, entering the next step;
Specifically, determining whether the analysis priority of the cashier is the first priority according to the historical private cashier times of the cashier specifically includes:
when the historical private cashing times of the cashier are larger than the set times, determining that the analysis priority of the cashier is the first priority, wherein the set times are determined according to the average value of the historical private cashing times of all cashiers with the historical private cashing conditions.
S32, acquiring the historical private cashing times of cashier, judging whether the historical private cashing times of cashier are smaller than preset times, if so, entering step S33, and if not, entering step S35;
specifically, when the number of times of historical private cashing of cashier is small, various factors are needed to be combined to determine whether the cashier has a problem.
Specifically, when the number of times of historical private cashing of cashier is 0 or 1, the determination of the private cashing probability evaluation value of cashier is directly performed through the number of times of historical private cashing of cashier.
S33, determining the frequency of the historical private cashing of the cashier according to the number of the historical private cashing of the cashier and the accumulated working time of the cashier, combining the highest historical private cashing number of the cashier in a set period and the average value of the historical private cashing number of the cashier in the set period in the latest set time, determining the frequency evaluation quantity of the historical private cashing of the cashier, and judging whether the frequency evaluation quantity of the historical private cashing of the cashier meets the requirement, if yes, entering the next step, and if not, entering the step S35;
S34, acquiring the latest historical private cashing time of the cashier, determining the latest evaluation quantity of the historical private cashing of the cashier by combining the historical private cashing times of the cashier in the latest setting time and the historical private cashing times of the cashier in the latest setting period, judging whether the latest evaluation quantity of the historical private cashing of the cashier meets the requirement, if so, determining the private cashing probability evaluation value of the cashier according to the historical private cashing times of the cashier, and if not, entering the next step;
and S35, determining the private cashing probability evaluation value of the cashing personnel through the recent evaluation value of the historical private cashing of the cashing personnel, the frequency evaluation value of the historical private cashing of the cashing personnel.
It should be noted that, the value range of the private cashing probability evaluation value of the cashing staff is between 0 and 1, where when the preset probability is determined according to the number of cashing staff and the distribution situation of the private cashing probability evaluation value of the cashing staff, specifically, the analysis priority of the cashing staff is that the number of the staff with the first priority is not greater than half the number of the cashing staff.
S14, determining an analysis period through the private cashing analysis frequency, setting the analysis priority of the cashing personnel as a second priority, determining the cashing evaluation quantity of the cashing personnel through the number of the shopping personnel corresponding to the analysis period, cashing time and settlement interval time of different shopping personnel, and determining the private cashing analysis sequence of the cashing personnel by combining the private cashing probability evaluation value of the cashing personnel.
Specifically, as shown in fig. 4, the method for determining the cashier evaluation amount of the cashier includes:
s41, acquiring the number of the shoppers corresponding to the cashier in the analysis period, and determining cashier analysis complexity of the cashier according to the number of the shoppers corresponding to the cashier in the analysis period and the cashier time;
s42, acquiring the number of cash registers used by the cashier in the analysis period, determining whether the cashier analysis complexity of the cashier meets the requirement according to the number of cash registers used by the cashier in the analysis period, if so, entering a step S43, and if not, entering a step S44;
S43, determining abnormal checkout intervals through checkout intervals of different shoppers of the cashier, determining abnormal checkout evaluation amounts of the cashier according to the times of the abnormal checkout intervals and the time length of the abnormal checkout intervals, and combining the determination of the abnormal checkout evaluation amounts of the cashier, and judging whether the abnormal checkout evaluation amounts of the cashier meet requirements, if so, determining the cashier evaluation amounts of the cashier through cashier analysis complexity, and if not, entering the next step;
s44, determining a common cash register and other cash registers through the use time length of the cash registers used by the cashier in the analysis period, determining the cashier' S cashier evaluation quantity through the use time length and the number of the common cash registers of the cashier, the use time length and the number of other cash registers, combining the time length of the cashier corresponding to the number of the shopping personnel in unit time being greater than the preset personnel number and the determination of the other analysis complexity of the cashier, and determining the cashier evaluation quantity through the other analysis complexity, the cashier analysis complexity and the abnormal checkout evaluation quantity of the cashier.
Specifically, when the checkout intervals of different shoppers are not within the set time threshold, determining that the checkout interval of the cashier is an abnormal checkout interval, specifically determining a continuous checkout screening time period according to the monitoring image of the cashier, determining the abnormal checkout interval according to the checkout intervals of different shoppers in the continuous checkout screening time period, and considering the number of purchased goods of the shoppers before and after the abnormal checkout interval when the abnormal checkout interval is performed.
The check-out interval time is determined when the number of products purchased by the shopper is greater than the set check-out interval time. It is determined that it belongs to an abnormal checkout interval in which, in general, the case of a private cash deposit occurs substantially
Specifically, when the analysis priority of the cashier is the second priority, the method for determining the private cashier analysis sequence of the cashier is as follows:
when the cashing evaluation amount of the cashing personnel is larger than the set evaluation amount, determining the private cashing analysis sequence of the cashing personnel according to the cashing evaluation amount of the cashing personnel;
And when the cashier evaluation value of the cashier is not greater than the set evaluation value, determining an analysis sequence value of the cashier through the cashier evaluation value of the cashier and the private cashier probability evaluation value, and determining a private cashier analysis sequence of the cashier according to the analysis sequence value of the cashier.
In this embodiment, the determination of the private cashing analysis sequence of cashing staff is performed through cashing evaluation values of cashing staff and private cashing probability evaluation values of cashing staff, so that the severity of historical private cashing of cashing staff is considered, meanwhile, the difference of the analysis difficulty of cashing staff is also considered, meanwhile, further identification of abnormal private cashing conditions is realized through settlement interval time of different shopping staff, and differential analysis of different cashing staff is realized.
As shown in fig. 5, the present invention provides a cashing intelligent management device, which adopts the cashing intelligent management method, and is characterized in that the cashing intelligent management device specifically includes:
a frequency determination module; a private cash analysis module;
the privacy cashing analysis module comprises a privacy cashing confirmation module, a probability evaluation module, an evaluation amount determination module and an analysis sequence determination module;
The frequency determining module is responsible for acquiring the number of cash registers and the number of cashiers of the store, and determining the private cashier analysis frequency of the store by combining the number of shopping personnel and commodity sales of the store;
the private cashing confirmation module is responsible for counting inventory commodities of the store based on the private cashing analysis frequency to obtain commodity counting data, and determining whether a private cashing condition exists according to cashing data of a cash register;
the probability evaluation module is responsible for determining a private cashing probability evaluation value of the cashing personnel through the historical private cashing times of the cashing personnel, the historical private cashing frequency and the latest historical private cashing time;
the evaluation determining module is responsible for giving the private cashing analysis frequency to determine an analysis period, setting the analysis priority of the cashing personnel as a second priority, and determining the cashing evaluation quantity of the cashing personnel according to the number of the shopping personnel, the cashing time and the settlement interval time of different shopping personnel corresponding to the analysis period;
the analysis sequence determination module is responsible for determining the private cashing analysis sequence of the cashing personnel through the cashing evaluation value of the cashing personnel and the private cashing probability evaluation value of the cashing personnel.
In another aspect, the present invention provides a computer apparatus comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and executing the intelligent cashing management method when the processor runs the computer program.
The intelligent cashier management method specifically comprises the following steps:
acquiring the number of cash registers and the number of cashiers in a store, and determining the private cashier analysis frequency of the store by combining the number of shoppers and commodity sales of the store;
checking the inventory commodity of the store based on the private cashing analysis frequency to obtain commodity checking data, and entering the next step when the private cashing condition is determined to exist by combining cashing data of a cash register;
determining a private cashing probability evaluation value of the cashing personnel according to the historical private cashing times, the historical private cashing frequency and the latest historical private cashing time of the cashing personnel, judging whether the private cashing probability evaluation value of the cashing personnel is larger than a preset probability, if so, setting the analysis priority of the cashing personnel as a first priority, and determining a private cashing analysis sequence according to the private cashing probability evaluation value of the cashing personnel, otherwise, entering the next step;
Determining an analysis period through the private cashing analysis frequency, setting the analysis priority of the cashing personnel as a second priority, acquiring the number of the shopping personnel corresponding to the cashing personnel in the analysis period, and determining the cashing analysis complexity of the cashing personnel according to the number of the shopping personnel corresponding to the cashing personnel in the analysis period and the cashing time;
acquiring the number of cash registers used by the cashier in the analysis period, and entering a next step when the cashier's cashier analysis complexity meets the requirement according to the number of cash registers used by the cashier in the analysis period;
determining abnormal checkout intervals through checkout intervals of different shoppers of the cashier, and determining abnormal checkout evaluation amounts of the cashier according to the times of the abnormal checkout intervals and the time length of the abnormal checkout intervals and the cashier;
the method comprises the steps that the cash register used in the analysis period of the cashier is used for determining a common cash register and other cash registers, the cash register is used for determining the use time of the cash register and the number of the cash registers, the use time of the cash register and the number of the cash registers are used for determining the private cashier analysis sequence of the cashier by combining the time that the number of corresponding shopping personnel in unit time of the cashier is larger than the number of preset personnel and the determination of the other analysis complexity of the cashier, and the determination of the cashier is performed by the other analysis complexity of the cashier, the cashier evaluation and the abnormal checkout evaluation of the cashier.
In another aspect, an embodiment of the present application provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed in a computer, causes the computer to execute a cashing intelligent management method as described above.
The intelligent cashier management method specifically comprises the following steps:
acquiring the number of the shoppers and the commodity sales volume of the store, and determining the set analysis frequency of the private cashing analysis of the store according to the number of the shoppers and the commodity sales volume of the store;
acquiring the number of cashiers in the store, determining the cashiers in complex analysis and general cashiers in a preset period through the cashiers in the store, and determining the analysis complexity of the cashiers according to the number of the cashiers in complex analysis, the cashier in the preset period, the general cashiers and the cashier in the preset period;
determining complex analysis cash registers and general cash registers according to cash register time of the store in a preset period, and determining analysis complexity of the cash registers according to the number of the complex analysis cash registers, cash register time in the preset period, general cash register number and cash register time in the preset period;
Acquiring historical private cashing data of the store, determining the private cashing times and the number of private cashing staff in the history of the store according to the historical private cashing data of the store, determining the analysis frequency correction of the store by combining the analysis complexity of the cash register and the analysis complexity of the cashing staff, and determining the private cashing analysis frequency of the store according to the analysis frequency correction of the store and the set analysis frequency of the store;
checking the inventory commodity of the store based on the private cashing analysis frequency to obtain commodity checking data, and entering the next step when the private cashing condition is determined to exist by combining cashing data of a cash register;
determining a private cashing probability evaluation value of the cashing personnel according to the historical private cashing times, the historical private cashing frequency and the latest historical private cashing time of the cashing personnel, judging whether the private cashing probability evaluation value of the cashing personnel is larger than a preset probability, if so, setting the analysis priority of the cashing personnel as a first priority, and determining a private cashing analysis sequence according to the private cashing probability evaluation value of the cashing personnel, otherwise, entering the next step;
And determining an analysis period through the private cashing analysis frequency, setting the analysis priority of the cashing personnel as a second priority, determining the cashing evaluation quantity of the cashing personnel through the quantity of the cashing personnel, the cashing time and the settlement interval time of different shopping personnel corresponding to the analysis period, and determining the private cashing analysis sequence of the cashing personnel by combining the private cashing probability evaluation value of the cashing personnel.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (13)
1. The intelligent cashier management method is characterized by comprising the following steps of:
acquiring the number of cash registers and the number of cashiers in a store, and determining the private cashier analysis frequency of the store by combining the number of shoppers and commodity sales of the store;
checking the inventory commodity of the store based on the private cashing analysis frequency to obtain commodity checking data, and entering the next step when the private cashing condition is determined to exist by combining cashing data of a cash register;
determining a private cashing probability evaluation value of the cashing personnel according to the historical private cashing times, the historical private cashing frequency and the latest historical private cashing time of the cashing personnel, judging whether the private cashing probability evaluation value of the cashing personnel is larger than a preset probability, if so, setting the analysis priority of the cashing personnel as a first priority, and determining a private cashing analysis sequence according to the private cashing probability evaluation value of the cashing personnel, otherwise, entering the next step;
And determining an analysis period through the private cashing analysis frequency, setting the analysis priority of the cashing personnel as a second priority, determining the cashing evaluation quantity of the cashing personnel through the quantity of the cashing personnel, the cashing time and the settlement interval time of different shopping personnel corresponding to the analysis period, and determining the private cashing analysis sequence of the cashing personnel by combining the private cashing probability evaluation value of the cashing personnel.
2. The intelligent cashier management method according to claim 1, wherein the number of shoppers and the commodity sales amount of the store are determined according to cashier records of cash registers of the store, and particularly according to cashier records of cash registers of the store in a preset period.
3. The intelligent cashier management method as claimed in claim 1, wherein the method for determining the private cashier analysis frequency of the store is as follows:
s21, acquiring the number of the shoppers and the commodity sales volume of the store, and determining the set analysis frequency of the private cashing analysis of the store according to the number of the shoppers and the commodity sales volume of the store;
S22, acquiring the number of cashiers in the store, determining the cashiers and general cashiers in complex analysis according to the cashier time in a preset period, determining the analysis complexity of the cashiers according to the number of the cashiers and the cashier time in the preset period, the general cashiers and the cashier time in the preset period, determining whether the set analysis frequency of the store meets the requirement according to the analysis complexity of the cashiers, if so, entering the next step, and if not, entering the step S24;
s23, determining a complex analysis cash register and a general cash register according to cash register time of the store in a preset period, determining analysis complexity of the cash register according to the number of the complex analysis cash registers, cash register time of the store in the preset period, general cash register number and cash register time of the store in the preset period, determining whether set analysis frequency of the store meets requirements or not according to the analysis complexity of the cash register, if yes, determining the set analysis frequency of private cash analysis of the store to the private cash analysis frequency of the store, and if no, entering step S24;
S24, acquiring historical private cashing data of the store, determining the private cashing times and the number of private cashing staff in the history of the store according to the historical private cashing data of the store, determining the analysis frequency correction of the store according to the analysis complexity of the cash register and the analysis complexity of the cashing staff, and determining the private cashing analysis frequency of the store according to the analysis frequency correction of the store and the set analysis frequency of the store.
4. The intelligent cashing management method of claim 3, wherein the determining of the set analysis frequency of the private cashing analysis of the store is performed according to the number of shoppers and the sales volume of goods in the store, specifically comprises:
determining the shopper demand frequency of private cashing analysis of the store by the number of shoppers of the store;
determining commodity demand frequency through private cashing analysis of the store by commodity sales volume of the store;
and determining the set analysis frequency of the private cashing analysis of the store according to the commodity demand frequency of the private cashing analysis of the store and the shopper demand frequency of the private cashing analysis of the store.
5. The method for intelligently managing cashier according to claim 3, wherein the preset period is determined according to the average daily people flow rate of the store and the commodity number of the store, and the larger the average daily people flow rate of the store is, the larger the commodity number of the store is, the shorter the preset period is.
6. The intelligent cashier management method of claim 1, wherein determining that a private cashier condition exists in combination with cashier data of a cash register specifically comprises:
and determining commodity sales volume data of the store according to the commodity inventory data, determining commodity sales record data of the store according to the cashing data of the cash register, and determining whether private cashing exists according to the commodity sales volume data of the store and the commodity sales record data of the store.
7. The intelligent cashier management method as claimed in claim 1, wherein the method for determining the evaluation value of the private cashier probability of the cashier is as follows:
s31, acquiring the historical private cashing times of cashier, determining whether the analysis priority of the cashier is a first priority according to the historical private cashing times of the cashier, if so, determining the analysis priority of the cashier as the first priority, determining the private cashing probability evaluation value of the cashier according to the historical private cashing times of the cashier, and if not, entering the next step;
S32, acquiring the historical private cashing times of cashier, judging whether the historical private cashing times of cashier are smaller than preset times, if so, entering step S33, and if not, entering step S35;
s33, determining the frequency of the historical private cashing of the cashier according to the number of the historical private cashing of the cashier and the accumulated working time of the cashier, combining the highest historical private cashing number of the cashier in a set period and the average value of the historical private cashing number of the cashier in the set period in the latest set time, determining the frequency evaluation quantity of the historical private cashing of the cashier, and judging whether the frequency evaluation quantity of the historical private cashing of the cashier meets the requirement, if yes, entering the next step, and if not, entering the step S35;
s34, acquiring the latest historical private cashing time of the cashier, determining the latest evaluation quantity of the historical private cashing of the cashier by combining the historical private cashing times of the cashier in the latest setting time and the historical private cashing times of the cashier in the latest setting period, judging whether the latest evaluation quantity of the historical private cashing of the cashier meets the requirement, if so, determining the private cashing probability evaluation value of the cashier according to the historical private cashing times of the cashier, and if not, entering the next step;
And S35, determining the private cashing probability evaluation value of the cashing personnel through the recent evaluation value of the historical private cashing of the cashing personnel, the frequency evaluation value of the historical private cashing of the cashing personnel.
8. The intelligent cashier management method according to claim 1, wherein the value range of the private cashier probability evaluation value of the cashier is between 0 and 1, wherein when the preset probability is determined according to the number of the cashiers and the distribution condition of the private cashier probability evaluation value of the cashier, specifically, the analysis priority of the cashier is that the number of the first priority personnel is not more than half of the number of the cashier.
9. The method for intelligently managing cashier according to claim 7, wherein determining whether the analysis priority of the cashier is the first priority according to the historical private cashier times of the cashier specifically comprises:
when the historical private cashing times of the cashier are larger than the set times, determining that the analysis priority of the cashier is the first priority, wherein the set times are determined according to the average value of the historical private cashing times of all cashiers with the historical private cashing conditions.
10. The intelligent cashier management method of claim 1, wherein when the analysis priority of the cashier is the second priority, the method for determining the private cashier analysis order of the cashier is:
the cashing evaluation amount of the cashing personnel is determined, and the private cashing analysis sequence of the cashing personnel is determined by combining the private cashing probability evaluation value of the cashing personnel;
when the cashing evaluation amount of the cashing personnel is larger than the set evaluation amount, determining the private cashing analysis sequence of the cashing personnel according to the cashing evaluation amount of the cashing personnel;
and when the cashier evaluation value of the cashier is not greater than the set evaluation value, determining an analysis sequence value of the cashier through the cashier evaluation value of the cashier and the private cashier probability evaluation value, and determining a private cashier analysis sequence of the cashier according to the analysis sequence value of the cashier.
11. A cashier intelligent management device, adopting the cashier intelligent management method of any one of claims 1-10, characterized in that the cashier intelligent management device specifically comprises:
a frequency determination module; a private cash analysis module;
The privacy cashing analysis module comprises a privacy cashing confirmation module, a probability evaluation module, an evaluation amount determination module and an analysis sequence determination module;
the frequency determining module is responsible for acquiring the number of cash registers and the number of cashiers of the store, and determining the private cashier analysis frequency of the store by combining the number of shopping personnel and commodity sales of the store;
the private cashing confirmation module is responsible for counting inventory commodities of the store based on the private cashing analysis frequency to obtain commodity counting data, and determining whether a private cashing condition exists according to cashing data of a cash register;
the probability evaluation module is responsible for determining a private cashing probability evaluation value of the cashing personnel through the historical private cashing times of the cashing personnel, the historical private cashing frequency and the latest historical private cashing time;
the evaluation determining module is responsible for giving the private cashing analysis frequency to determine an analysis period, setting the analysis priority of the cashing personnel as a second priority, and determining the cashing evaluation quantity of the cashing personnel according to the number of the shopping personnel, the cashing time and the settlement interval time of different shopping personnel corresponding to the analysis period;
The analysis sequence determination module is responsible for determining the private cashing analysis sequence of the cashing personnel through the cashing evaluation value of the cashing personnel and the private cashing probability evaluation value of the cashing personnel.
12. A computer apparatus, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when running the computer program, performs a cashier intelligent management method as defined in any one of claims 1-10.
13. A computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a cashier intelligent management method according to any of claims 1-10.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117857467A (en) * | 2024-03-08 | 2024-04-09 | 杭州青橄榄网络技术有限公司 | Data encryption method for user payment system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014089617A1 (en) * | 2012-12-10 | 2014-06-19 | Maze Retail Pty Ltd | Business system and method for customer relationship management |
| US20140214596A1 (en) * | 2013-01-29 | 2014-07-31 | Wal-Mart Stores, Inc. | Shopping process including monitored shopping cart basket weight |
| CN104616186A (en) * | 2014-11-05 | 2015-05-13 | 青岛海信智能商用系统有限公司 | Self-service bill collecting and loss prevention system, self-service checkout and loss prevention system and self-service checkout method |
| JP2015103203A (en) * | 2013-11-28 | 2015-06-04 | シャープ株式会社 | Terminal monitoring system, terminal monitoring method, and program |
| CN108764822A (en) * | 2018-05-04 | 2018-11-06 | 武汉科技大学 | A kind of Supermarket Management System cashier management subsystem |
| JP2019139795A (en) * | 2019-04-18 | 2019-08-22 | パナソニックＩｐマネジメント株式会社 | Shopping history management system and shopping history management method |
| CN114511372A (en) * | 2021-12-23 | 2022-05-17 | 上海群之脉信息科技有限公司 | Intelligent data processing method |
| JP2022154978A (en) * | 2021-03-30 | 2022-10-13 | Kddi株式会社 | Information processing apparatus |
| US20230089825A1 (en) * | 2020-03-05 | 2023-03-23 | Yair Cleper | Systems and methods for generating shopping checkout lists |
| CN116011822A (en) * | 2023-01-18 | 2023-04-25 | 广州市钱大妈信息科技有限公司 | Store cashing management method and device, computer equipment and storage medium |
-
2023
- 2023-08-25 CN CN202311081295.5A patent/CN116823190B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014089617A1 (en) * | 2012-12-10 | 2014-06-19 | Maze Retail Pty Ltd | Business system and method for customer relationship management |
| US20140214596A1 (en) * | 2013-01-29 | 2014-07-31 | Wal-Mart Stores, Inc. | Shopping process including monitored shopping cart basket weight |
| JP2015103203A (en) * | 2013-11-28 | 2015-06-04 | シャープ株式会社 | Terminal monitoring system, terminal monitoring method, and program |
| CN104616186A (en) * | 2014-11-05 | 2015-05-13 | 青岛海信智能商用系统有限公司 | Self-service bill collecting and loss prevention system, self-service checkout and loss prevention system and self-service checkout method |
| CN108764822A (en) * | 2018-05-04 | 2018-11-06 | 武汉科技大学 | A kind of Supermarket Management System cashier management subsystem |
| JP2019139795A (en) * | 2019-04-18 | 2019-08-22 | パナソニックＩｐマネジメント株式会社 | Shopping history management system and shopping history management method |
| US20230089825A1 (en) * | 2020-03-05 | 2023-03-23 | Yair Cleper | Systems and methods for generating shopping checkout lists |
| JP2022154978A (en) * | 2021-03-30 | 2022-10-13 | Kddi株式会社 | Information processing apparatus |
| CN114511372A (en) * | 2021-12-23 | 2022-05-17 | 上海群之脉信息科技有限公司 | Intelligent data processing method |
| CN116011822A (en) * | 2023-01-18 | 2023-04-25 | 广州市钱大妈信息科技有限公司 | Store cashing management method and device, computer equipment and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| ""从财务角度浅谈社区生鲜连锁超市内部监控方法"", 《质量与市场》, no. 07, pages 20 - 22 * |
| V. A. MYLNIKOV ET AL.: ""Modeling the Security System of the Cloud IoT Platform of an Smart Supermarket"", 《2022 WAVE ELECTRONICS AND ITS APPLICATION IN INFORMATION AND TELECOMMUNICATION SYSTEMS (WECONF)》, pages 21844850 - 1 * |
| 刘冉;宋丽丽;石磊鑫;: "基于RFID的超市自助收银系统", 物联网技术, no. 10, pages 94 - 95 * |
| 陈亮;杜敏;: ""大型超市收银作业IE改进与设计"", 《中小企业管理与科技(上旬刊)》, no. 12, pages 288 - 289 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117857467A (en) * | 2024-03-08 | 2024-04-09 | 杭州青橄榄网络技术有限公司 | Data encryption method for user payment system |
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