CN-121120006-B - Commercial coffee machine data supervision method and system and coffee shop operation management platform with system

CN121120006BCN 121120006 BCN121120006 BCN 121120006BCN-121120006-B

Abstract

The invention provides a data supervision method and system for a commercial coffee machine and a coffee shop operation management platform with the system, which can improve the data quality by acquiring operation data in real time and obtaining standard operation data through data filtering, standardization and other processes, judge the standard operation data as abnormal when the standard operation data is not in a preset range, otherwise, judge the standard operation data as normal, quickly and accurately identify whether the coffee machine has problems, facilitate timely finding out the abnormality and take measures, store the data based on the association of the standard operation data and the historical storage data in data type and value, enhance the availability and association of the data, enable the reference data related to the current standard operation data to be quickly found when the historical data is required to be fetched later, and when the coffee machine is in an abnormal state, fetch the associated reference historical storage data, determine a control instruction by combining a historical processing result and a self-adaptive correction mechanism, thereby improving the efficiency and accuracy of abnormality processing.

Inventors

CHEN YONGJIE
CHEN CHAOCHAO
ZHOU XIANGBO

Assignees

鸿小咖咖啡(深圳)有限公司

Dates

Publication Date: 20260508
Application Date: 20250818

Claims (9)

1. A method of data supervision for a commercial coffee machine, comprising: S1, acquiring and processing operation data of a commercial coffee machine in real time to obtain standard operation data; S2, comparing the standard operation data with a preset normal parameter range, and judging whether the commercial coffee machine is in a normal operation state or not; S3, storing the standard operation data based on the data association between the standard operation data and the historical storage data; S4, when the commercial coffee machine is in an abnormal operation state, calling reference history storage data associated with the standard operation data, and determining a control instruction for the commercial coffee machine based on a history processing result of the reference history storage data and combining an adaptive correction mechanism, wherein the control instruction comprises the following steps: performing association analysis on the standard operation data and the reference historical storage data based on the parameter dimension, the scene dimension and the causal dimension to obtain parameter association, scene association and causal association, obtaining the proportion of the abnormal data type in the standard operation data in the parameter dimension, the scene dimension and the causal dimension, and obtaining the abnormal association of the standard operation data and the reference historical storage data by combining the parameter association, the scene association and the causal association; Acquiring a history control instruction corresponding to the reference history storage data, establishing an effect scoring model based on the solution efficiency and the secondary failure rate as effect indexes, and inputting the history control instruction and the history effect index value into the effect scoring model to obtain the effect score of the history control instruction of the reference history storage data; determining a historical processing result duty ratio and an adaptive correction mechanism duty ratio based on the abnormal relevance and the effect score; Based on historical experience and real-time feedback, establishing a parameter self-adaptive correction mechanism; based on the history effect corresponding to the history control instruction, constructing an anomaly type-processing scheme-correction coefficient mapping library, and based on the anomaly type-processing scheme-correction coefficient mapping library, constructing a strategy self-adaptive correction mechanism; Setting a sub-duty ratio of a parameter self-adaptive correction mechanism and a sub-duty ratio of a strategy self-adaptive correction mechanism based on the effect score, and fusing the parameter self-adaptive correction mechanism and the strategy self-adaptive correction mechanism based on the sub-duty ratio to obtain the self-adaptive correction mechanism; and correcting the historical control instruction based on the historical processing result duty ratio and the self-adaptive correction mechanism duty ratio to obtain the control instruction of the commercial coffee machine.
2. A method of data supervision of a commercial coffee machine according to claim 1, characterized by further comprising: generating early warning information based on abnormal data when the commercial coffee machine is in an abnormal operation state; And sending the early warning information to a mobile terminal of the manager.
3. The method for supervising data of a commercial coffee machine according to claim 1, wherein in S1, operation data of the commercial coffee machine is collected and processed in real time to obtain standard operation data, and the method comprises: acquiring operation data in real time based on various sensors built in the commercial coffee machine to obtain initial operation data; and carrying out data filtering and data standardization on the initial operation data to obtain standard operation data.
4. The method for supervising data of a commercial coffee machine according to claim 1, wherein in S2, comparing the standard operation data with a preset normal parameter range, and determining whether the commercial coffee machine is in a normal operation state comprises: judging whether the standard operation data is in a preset normal parameter range or not; If yes, determining that the commercial coffee machine is in a normal running state; Otherwise, determining that the commercial coffee machine is in an abnormal operation state.
5. The method according to claim 1, wherein in S3, storing the standard operation data based on the data association between the standard operation data and the history storage data, comprises: Extracting the data type and the numerical value in the standard operation data, determining a basic tag of the standard operation data based on the data type and the numerical value, and generating a depth tag of the standard operation data based on space-time association and causal association in the standard operation data; Performing data base association on standard operation data and historical storage data based on the basic tag to obtain a first association degree, performing data depth association on the standard operation data and the historical storage data based on the deep tag to obtain a second association degree, determining a first weight of the first association degree and a second weight of the second association degree based on a call search keyword of a latest historical period, and calculating real-time association coefficients of the standard operation data and the historical storage data based on the first association degree, the first weight, the second association degree and the second weight; acquiring target storage data corresponding to the highest real-time correlation coefficient of standard operation data from historical storage data, and storing the standard operation data into a target storage area corresponding to the target storage data; Determining that the standard operation data is abnormal data or normal data, increasing the cold data area weight of the target storage area when the standard operation data is normal data, increasing the hot data area weight of the target storage area when the standard operation data is abnormal data, Determining a target base region weight of the target storage region based on the base region weight of the target storage region in combination with the cold data region weight or the hot data region weight and the calling frequency of the target storage region in the latest history period; when the weight of the target basic area is greater than a preset weight range, storing all storage data of the target storage area into a higher-level high-speed memory area, when the weight of the target basic area is within the preset weight range, keeping the target storage area unchanged, and when the weight of the target basic area is less than the preset weight range, storing all storage data of the target storage area into a lower-level high-speed memory area.
6. The method according to claim 5, wherein the determining the target base area weight of the target storage area is based on the base area weight of the target storage area in combination with the cold data area weight or the hot data area weight and the call frequency of the target storage area in the recent history period, specifically: calculating to obtain the target basic area weight of the target storage area according to the following formula; Wherein, the A target base region weight representing a target storage region, The base region weight is represented as a function of the base region weight, Representing the weight of the hot data region, Representing the weight of the cold data region, Representing the frequency of the reference difference, Representing the call frequency of the target storage area during the last history period, Represents the historical call frequency of the target storage area when determining the base area weight.
7. The method of claim 1, wherein modifying the historical control command based on the historical processing result duty cycle and the adaptive modification mechanism duty cycle to obtain the control command for the commercial coffee machine comprises: determining acceptance of the historical control instruction based on the duty ratio of the historical processing result, and determining acceptance content of the historical control instruction based on the acceptance; correcting the non-accepted content of the historical control instruction based on the self-adaptive correction mechanism duty ratio to obtain corrected content; based on the accepted content and the corrected content, control instructions for the commercial coffee machine are obtained.
8. A commercial coffee machine data supervision system for use in a commercial coffee machine data supervision method according to any one of claims 1 to 7, comprising: The acquisition processing module is used for acquiring and processing the operation data of the commercial coffee machine in real time to obtain standard operation data; The state judging module is used for comparing the standard operation data with a preset normal parameter range and judging whether the commercial coffee machine is in a normal operation state or not; The data storage module is used for storing the standard operation data based on the data association between the standard operation data and the historical storage data; the instruction determining module is used for calling the reference history storage data associated with the standard operation data when the commercial coffee machine is in an abnormal operation state, and determining a control instruction for the commercial coffee machine based on a history processing result of the reference history storage data and combining an adaptive correction mechanism.
9. A coffee shop operations management platform with a commercial coffee machine data administration system comprising the commercial coffee machine data administration system of claim 8.

Description

Commercial coffee machine data supervision method and system and coffee shop operation management platform with system Technical Field The invention relates to the technical field of coffee machine data management, in particular to a commercial coffee machine data supervision method and system and a coffee shop operation management platform with the system. Background At present, commercial coffee machines are widely used in coffee shops, however, the monitoring of operation data, sales data and the like of the commercial coffee machines has a plurality of defects, and in the prior art, data collection is often not comprehensive enough, and various operation parameters of the coffee machines, such as water temperature, pressure, coffee bean allowance, milk foam amount and the like, are difficult to cover. Meanwhile, the data supervision is not timely enough, and the problems in the running process of the coffee machine cannot be found in real time; for example, when the water temperature of the coffee machine is abnormal, early warning cannot be timely performed, the produced coffee is bad in taste and the customer experience is affected, when the coffee bean allowance is insufficient, if the coffee bean allowance cannot be timely reminded to be supplemented, the coffee cannot be normally produced, sales loss is caused, equipment operation data, transaction data and maintenance records are mutually independent, unified analysis and decision making are difficult to realize, and the problems are that the operation management efficiency of a coffee shop is low and the normal operation and economic benefits of the coffee shop are affected. Disclosure of Invention The invention provides a data supervision method and system for a commercial coffee machine and a coffee shop operation management platform with the system, which are used for solving the problems in the background technology. A method of data supervision for a commercial coffee machine, comprising: S1, acquiring and processing operation data of a commercial coffee machine in real time to obtain standard operation data; S2, comparing the standard operation data with a preset normal parameter range, and judging whether the commercial coffee machine is in a normal operation state or not; S3, storing the standard operation data based on the data association between the standard operation data and the historical storage data; S4, when the commercial coffee machine is in an abnormal operation state, the reference history storage data related to the standard operation data is called, and based on a history processing result of the reference history storage data, a control instruction of the commercial coffee machine is determined by combining an adaptive correction mechanism. Preferably, the method further comprises: generating early warning information based on abnormal data when the commercial coffee machine is in an abnormal operation state; And sending the early warning information to a mobile terminal of the manager. Preferably, in the step S1, operation data of the commercial coffee machine is collected and processed in real time to obtain standard operation data, including: acquiring operation data in real time based on various sensors built in the commercial coffee machine to obtain initial operation data; and carrying out data filtering and data standardization on the initial operation data to obtain standard operation data. Preferably, in the step S2, comparing the standard operation data with a preset normal parameter range, and determining whether the commercial coffee machine is in a normal operation state includes: judging whether the standard operation data is in a preset normal parameter range or not; If yes, determining that the commercial coffee machine is in a normal running state; Otherwise, determining that the commercial coffee machine is in an abnormal operation state. Preferably, in the step S3, storing the standard operation data based on the data association between the standard operation data and the history storage data includes: Extracting the data type and the numerical value in the standard operation data, determining a basic tag of the standard operation data based on the data type and the numerical value, and generating a depth tag of the standard operation data based on space-time association and causal association in the standard operation data; Performing data base association on standard operation data and historical storage data based on the basic tag to obtain a first association degree, performing data depth association on the standard operation data and the historical storage data based on the deep tag to obtain a second association degree, determining a first weight of the first association degree and a second weight of the second association degree based on a call search keyword of a latest historical period, and calculating real-time association coefficients of the standard operation data and the historical storage