Preparation data acquisition method and system for herbal coffee drinks and storage medium

Abstract

The invention provides a preparation data acquisition method for herbal coffee drinks, and belongs to the technical field of data processing, and the method specifically comprises the following steps: on the basis of herbal types needing quality inspection processing and associated herbal type data of corresponding drink types, when it is determined that preparation data acquisition processing does not need to be carried out by adopting a preset scheme, determining that the preparation data acquisition processing does not need to be carried out by adopting the preset scheme; based on the sales data of the related beverage types of the herbal types in different stores and the herbal type data obtained by carrying out preparation data acquisition processing by adopting a preset scheme, determining an acquisition matching store of the preparation data of the herbal types, so as to acquire an acquisition result of the preparation data of the related beverage types of the herbal types in the matching store, according to the method for acquiring and analyzing preparation data of different stores, the identification processing efficiency of the herbal types with quality defect risks is improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a method, system and storage medium for collecting data on the preparation of herbal coffee beverages. Background Technology

[0002] "Herbal Coffee" is a new type of health drink that creatively blends traditional Chinese herbal medicine (herbal) with modern coffee. It is not just about adding herbal flavor to coffee, but also an innovative product based on the theory of "medicine and food sharing the same origin," aiming to satisfy both taste enjoyment and health needs.

[0003] However, at the same time, for example, in the case of jujube and goji berry eye-brightening coffee, jujubes, goji berries, and chrysanthemums are added to coffee. The bitterness of the coffee is softened by the natural sweetness of the jujubes, while also carrying the fragrance of goji berries and chrysanthemums. If the proportion of jujubes, goji berries, and chrysanthemums added is off or there are quality defects, the overall quality defect problem will be quite serious. Therefore, how to identify and process the types of herbs suspected of having quality defects based on feedback data, and collect and process preparation data in a targeted manner to accurately identify the types of herbs with a high risk of quality defects, and improve the targeting and efficiency of quality inspection, has become an urgent technical problem to be solved.

[0004] Therefore, there is an urgent need for a data acquisition method, system, and storage medium for the preparation of herbal coffee beverages. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for collecting data on the preparation of herbal coffee beverages, which includes: S1 uses the analysis results of the preparation data of herbal coffee products to determine the sales data of different beverage types. Based on the sales data, evaluation data and the herbal types associated with the beverage types, the herbal types that need to be quality inspected are determined. Based on the herbal types that need to be quality inspected and the associated herbal type data of the corresponding beverage types, if it is determined that the preparation data collection and processing does not need to be carried out using the preset scheme, proceed to the next step. S2 determines the matching stores for collecting preparation data of the herbal type based on the sales data of the associated beverage types of the herbal type in different stores and the herbal type data collected and processed using a preset scheme. S3 uses the collection results of preparation data of the herbal type-related beverage types in the matched stores to determine the herbal type-related beverage types and the method for collecting and analyzing preparation data in different stores.

[0006] The beneficial effects of this invention are as follows: Based on the data of the herbal types that require quality inspection and the corresponding beverage types associated with them, it is determined whether a preset scheme should be used for data collection and processing. This ensures that the herbal types with a large number of associated herbal types in the corresponding beverage types that require quality inspection are selected, improving the efficiency of identifying and processing the quality defects of the aforementioned herbal types, while also ensuring the reliability of the quality control of herbal coffee beverages.

[0007] By collecting preparation data of herbal beverage types from matching stores, the associated beverage types of herbal beverage types are identified. The method of collecting and analyzing preparation data from different stores enables the evaluation of the reliability of identifying and handling issues caused by operational problems in sales data of herbal beverage types based on the number of matching stores and the proportional deviation of associated beverage types of herbal beverage types in the preparation process. This also lays the foundation for determining further improvement strategies for the collection and analysis method based on the reliability of the identification and handling, thereby improving the efficiency of identifying and handling preparation defects.

[0008] Furthermore, the analysis results of the preparation data are determined based on the order data of the herbal coffee products.

[0009] Furthermore, the sales data for the beverage type includes the sales volume of the beverage type in different stores.

[0010] Furthermore, the herbal type associated with the beverage type is the herbal type required by the beverage type.

[0011] Furthermore, the method for determining the type of herb requiring quality inspection is as follows: Based on the sales data of the beverage type, determine the sales volume of the beverage type in different stores; Based on the sales volume of the beverage types in different stores, the types of beverages of interest are determined. Based on the quality feedback data of the beverage types of interest in different stores and the associated herbal types, the herbal types that need to be inspected are determined.

[0012] Furthermore, the method for determining the data collection and analysis of the preparation data for the herbal-type related beverages in different stores is as follows: Based on the collection results of the preparation data of the herbal type of related beverages in the matching stores, the single cup addition ratio data of the related risk beverage type is determined, and the beverages of the related risk beverage type whose addition ratio is not within the standard range are regarded as proportion deviation beverages. Based on the proportion deviation beverage data collected from the matched stores, the proportion of shipments of beverages with proportion deviation in the matched stores is determined in the associated risk beverage type of the herbal type. Based on the data collected from the matched stores of the herbal type and the proportion of beverage shipments in different matched stores, the associated beverage types of the herbal type are determined, and the data collection and analysis method for the preparation data of different stores is used.

[0013] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for collecting data for the preparation of a herbal coffee beverage when running the computer program.

[0014] Thirdly, the present invention provides a computer storage medium storing a computer program, which, when executed in a computer, causes the computer to execute the aforementioned method for collecting data on the preparation of a herbal coffee beverage.

[0015] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of a data collection method for the preparation of a herbal coffee beverage; Figure 2 This is a flowchart illustrating the method for determining the types of herbs that require quality inspection. Figure 3 This is a flowchart that determines whether a preset scheme is needed for data acquisition and processing. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0020] Example 1 like Figure 1 As shown, this application provides a method for collecting data on the preparation of herbal coffee beverages, specifically including: S1 uses the analysis results of the preparation data of herbal coffee products to determine the sales data of different beverage types. Based on the sales data, evaluation data and the herbal types associated with the beverage types, the herbal types that need to be quality inspected are determined. Based on the herbal types that need to be quality inspected and the associated herbal type data of the corresponding beverage types, if it is determined that the preparation data collection and processing does not need to be carried out using the preset scheme, proceed to the next step. S2 determines the matching stores for collecting preparation data of the herbal type based on the sales data of the associated beverage types of the herbal type in different stores and the herbal type data collected and processed using a preset scheme. S3 uses the collection results of preparation data of the herbal type-related beverage types in the matched stores to determine the herbal type-related beverage types and the method for collecting and analyzing preparation data in different stores.

[0021] Furthermore, the analysis results of the preparation data are determined based on the order data of the herbal coffee products.

[0022] Furthermore, the sales data for the beverage type includes the sales volume of the beverage type in different stores.

[0023] Furthermore, the herbal type associated with the beverage type is the herbal type required by the beverage type.

[0024] Specifically, such as Figure 2 As shown, the method for determining the type of herb requiring quality inspection is as follows: Based on the sales data of the beverage type, determine the sales volume of the beverage type in different stores; Based on the sales volume of the beverage types in different stores, the types of beverages of interest are determined. Based on the quality feedback data of the beverage types of interest in different stores and the associated herbal types, the herbal types that need to be inspected are determined.

[0025] It is understood that the beverage types of interest are those whose average sales volume in different stores is greater than a preset sales volume threshold.

[0026] It should be noted that, based on quality feedback data of the beverage types of concern in different stores and related herbal types, the types of herbal products requiring quality inspection were determined, specifically including: Based on the quality feedback data of the beverage types of interest in different stores, stores with a negative review rate of less than the average negative review rate of different stores are identified and identified as stores with quality defects. Based on the data of stores with quality defects and the associated herbal types, the types of herbs that need to be inspected are determined.

[0027] It is understandable that if the number of stores with quality defects in the beverage type in question does not meet the requirements, there is a risk that the sales volume of some stores may be abnormal due to the quality defects of the associated herbal type. Therefore, the associated herbal type of the beverage type in question will be considered as the herbal type that needs to be inspected.

[0028] Specifically, the beverage type corresponding to the herbal type that needs quality inspection is a beverage type whose associated herbal type contains the herbal type that needs quality inspection.

[0029] Specifically, in one possible embodiment, the beverage types (herbal coffee series) are as follows, based on last month's sales data: Ginseng Energy Coffee (related herb: ginseng), Goji Berry Eye-Protecting Coffee (related herb: goji berry), Peppermint Cold Brew Coffee (related herb: peppermint), and Licorice Latte (related herb: licorice). Preset sales volume threshold: set to 150 cups / month / store.

[0030] Calculation and Determination: Average sales of Ginseng Energy Coffee: 110 cups -> Not met; Average sales of Goji Berry Eye-Protecting Coffee: 90 cups -> Not met; Average sales of Mint Cold Brew Coffee: 130 cups -> Not met; Average sales of Licorice Latte: 210 cups -> Far exceeds the threshold. These are determined to be "Drinks of Interest". Conclusion: The current "Drinks of Interest" type is Licorice Latte. This is a popular and well-regarded product.

[0031] Licorice lattes are popular for their unique sweet flavor and throat-soothing concept. The associated herb is licorice. Calculating average sales, the average negative review rate for licorice lattes across 100 stores nationwide is 1%. To identify stores with quality defects, we identified all stores with a negative review rate greater than 1%. Analysis revealed 35 stores with abnormally high negative review rates for licorice lattes, averaging around 2%. These 35 stores are considered "quality defect stores." If the proportion of quality defect stores exceeds 10%, they are classified as herb-based products requiring quality inspection.

[0032] Specifically, such as Figure 3 As shown, it has been determined that a preset scheme is not required for data acquisition and processing, specifically including: The beverage type corresponding to the herbal type that needs quality inspection is taken as the associated beverage type of the herbal type that needs quality inspection. Based on the associated herbal type data of the associated beverage types, determine the influencing beverage types among the associated beverage types; Based on the data affecting beverage types, determine whether a preset scheme is needed for data collection and processing.

[0033] It is understood that the beverage type that affects the beverage is a beverage type that has a preset number or more of associated herbal types, wherein the preset number is not less than 2.

[0034] Specifically, based on the data affecting beverage types, it is determined whether a preset scheme needs to be used for data collection and processing, including: When the number of beverage types affected does not meet the requirements, since there are many beverage types affected, a preset plan needs to be adopted for preparation data collection and processing. That is, the consumption of a single cup of the herbal type that needs to be quality inspected is determined in different stores during the preparation process of different related beverage types, so as to determine whether it is due to abnormal ratio or abnormal herbal type.

[0035] Background: In the previous stage, we identified licorice as the herb type that needs quality control. Step 1: Associate the problematic herb with all beverages that use it. Herb type that needs quality control: Licorice. Find associated beverage types: In the company's beverage menu, find all beverages whose recipes contain licorice.

[0036] Licorice Latte (core herb: licorice), Licorice and Tangerine Peel Iced Shaken Tea (herbs: licorice, tangerine peel), Herbal Throat-Soothing Special (herbs: licorice, Malva nut, honeysuckle). Conclusion: Related beverage types for licorice include: Licorice Latte, Licorice and Tangerine Peel Iced Shaken Tea, and Herbal Throat-Soothing Special.

[0037] Step 2: Determine the "Affecting Beverage Type" from the associated beverages. Definition: "Affecting Beverage Type" refers to beverages with at least 2 associated herbal types (i.e., beverages with high formula complexity). Licorice Latte: Only licorice is an associated herb -> Not an affecting beverage type. Licorice and Tangerine Peel Iced Shaken Tea: Both licorice and tangerine peel are associated herbs -> Yes, it is an affecting beverage type. Herbal Throat Soothing Special: Licorice, Malva nut, and honeysuckle are associated herbs -> Yes, it is an affecting beverage type.

[0038] Step 3: Based on the number of "affected beverage types," decide whether to initiate in-depth monitoring and determine whether the number of affected beverage types "meets the requirements." "Requirements" can be understood as "an acceptable range of simple problems." For example, if the company believes that if more than one complex-formula beverage is affected, further investigation is necessary. Currently, the identified number of "affected beverage types" is 2, which is greater than 1. Therefore, the conclusion is that the number of affected beverage types "does not meet the requirements" (i.e., the number is too high). Decision: A pre-set plan needs to be adopted for data collection and processing. Goal: In different stores, monitor the single-cup consumption of licorice during the preparation process for all related beverage types.

[0039] Execution: Quality inspectors use weight monitoring devices in stores and sales data to determine the actual amount of licorice used to make one cup of beverage. If the "licorice consumption per cup" in all stores is stable and meets the standard, then the problem is most likely with the quality of the licorice itself (e.g., this batch of licorice is not potent enough, even if the amount used is correct, the flavor is not good enough).

[0040] Specifically, the method for determining the matching stores for the preparation data of the herbal medicine type is as follows: Based on the sales data of the herbal beverage types associated with the herbal medicine type in different stores, determine the sales volume of the herbal beverage types associated with the herbal medicine type in the stores; Based on the herbal data collected and processed using a preset scheme in the stores, the herbal types collected and processed using the preset scheme in the stores are determined and used as the herbal types for quality inspection. Based on the quality inspection herbal type and the sales volume of related beverage types of herbal types in the stores, the stores for collecting and matching the preparation data of the herbal type are determined.

[0041] It is understandable that, based on the quality inspection herbal type and the sales volume of related herbal beverage types in the stores, the matching stores for collecting preparation data of the herbal type are determined, specifically including: Among the beverage types associated with the herbal medicine category, those beverage types whose number of stores with quality defects does not meet the requirements are considered as beverage types with associated risks. Based on the sales volume of herbal-type related beverages in the store, it is determined whether the store belongs to the quality defect store of the related risk beverage type. If so, the store is determined to be a matching store for the collection of preparation data of the herbal type. If not, it is determined whether the store is a matching store for the collection of preparation data of the herbal type based on the sales data of the related risk beverage type and the sales data of the related beverage type.

[0042] Specifically, based on the sales data of the associated risk beverage type and the sales data of the associated beverage type, it is determined whether the store is a matching store for the collection of preparation data for the herbal type, specifically including: Based on the proportion of the sales volume of the associated risk beverage type in the total sales volume of the associated beverage type in the store, the sales volume ratio is determined. It is then determined whether the sales volume ratio in the store is greater than a preset sales volume ratio threshold. If so, the store is determined to be the matching store for the preparation data of the herbal type. If not, the remaining matching stores are determined based on the sales data of the matching store.

[0043] It is understood that, based on the sales data of the collected and matched stores, the remaining stores to be collected and matched are determined, specifically including: Based on the sales data of the matched stores, the sales volume ratio of the herbal type to the associated risky beverage type in the matched stores is determined. When the sales volume ratio meets the requirements, the remaining stores are determined not to be matched stores. When the sales volume ratio does not meet the requirements, the matching factor of the store is determined based on the sales volume of the associated risky beverage type and the quality inspection herbal type in the store. When the matching factor of the store is greater than the preset matching factor threshold, the store is determined to be a matched store for the preparation data of the herbal type. If the matching factor of the store is not greater than the preset matching factor threshold, the store is determined not to be a matched store for the preparation data of the herbal type.

[0044] The core objective of this logic is: after deciding to initiate in-depth monitoring (i.e., sending people to stores to actually measure the amount of licorice used), which stores should be selected as monitoring targets? The selection criteria are: prioritize stores with the highest risk and the most representative data.

[0045] Example: Determining the matching stores for collecting "licorice" preparation data Background: Herbal type requiring quality inspection (quality inspection herb type): licorice; related beverage types: licorice latte, licorice and tangerine peel iced shaken tea, herbal throat-soothing special.

[0046] Associated risk beverage type (i.e., beverage type with insufficient number of stores with quality defects): In the initial analysis, licorice latte was identified as a "beverage type of concern," and its number of stores with quality defects (35 / 100) does not meet the requirement. Therefore, licorice latte is the current associated risk beverage type.

[0047] Step 1: Define the candidate store pool, which contains data for all 100 stores. All stores that have sold any of the "related beverage types" (licorice latte, licorice and tangerine peel iced shaken tea, herbal throat-soothing special) are potential candidate stores.

[0048] Step Two: First Round of Screening – Directly Identifying High-Risk Stores. Rule: Determine if the store belongs to the category of stores with quality defects related to the associated risk beverage type (licorice latte). If so, directly identify them as matching stores for data collection. Execution: We identified 35 stores with licorice latte sales significantly below the average level from the data. Conclusion: These 35 stores were directly identified as matching stores for data collection. We label them Group A.

[0049] Step 3: Second Round of Screening – Based on Sales Percentage. Now, we need to see if additional monitoring points are needed from the remaining 65 stores (i.e., stores with normal or good sales of licorice lattes). The rule is: calculate the sales percentage for each store (licorice latte sales / total sales of all licorice-containing drinks in that store). If the percentage is greater than the preset sales percentage threshold (e.g., 50%), it is also selected. Preset threshold: 50%. Execution: Among the remaining 65 stores, we found that: 10 stores, although their licorice latte sales met the threshold, had a sales percentage exceeding 50% of their total sales in the "licorice series drinks" category (e.g., these stores mainly rely on licorice lattes for volume sales, and other licorice drinks sell very little). Conclusion: These 10 stores are identified as matching stores for data collection. We label them Group B.

[0050] Step Four: Third Round of Screening – Evaluation and Supplementation. Check whether the data from the currently selected stores (45 stores) is representative enough. Rule: Calculate the sales volume percentage of the associated risk beverage type (licorice latte) in the total sales volume of the selected stores (Group A+B). If the requirement is met (e.g., the sales volume of licorice latte accounts for more than 70% of all licorice series beverages), it means that the sample can well reflect the core issue, and the screening stops. If the requirement is not met (e.g., the percentage is less than 70%), further screening is needed from the remaining stores using more refined "collection adaptation factors".

[0051] Calculate the total sales volume of all "licorice series drinks" in the 45 stores of Group A+B, let's call it S_total. Then calculate the sales volume of licorice lattes in these 45 stores, let's call it S_risk. Calculate the percentage: S_risk / S_total = 60%. Determine: 60% < 70% (preset requirement). Therefore, the sales volume percentage of the current sample "does not meet the requirement". Step 5: Final Screening – Using the Acquisition Fit Factor. Rules: From the remaining stores (100 - 45 = 55 stores remaining), calculate the acquisition fit factor for each store, using the absolute sales volume of licorice lattes and the total consumption of licorice in that store (the higher the consumption, the wider the impact). Simplified formula example: Acquisition Fit Factor = (Store Licorice Latte Sales / Average Licorice Latte Sales Across All Stores) * (Store Total Licorice Series Sales / Average Licorice Series Sales Across All Stores). Preset fit factor threshold: 1.0 (a factor greater than 1 indicates higher than average importance). We found that 5 stores had significantly higher factors than the others, with their factor calculation results between 1.2 and 1.8, all greater than the threshold of 1.0. These 5 stores are characterized by high sales of licorice lattes, and also good sales of other licorice drinks (such as licorice and tangerine peel iced shaken tea). They are loyal users and significant contributors to the "licorice" series. Conclusion: These 5 stores are identified as acquisition-matching stores. We label them Group C.

[0052] Final conclusion: Through a three-tiered screening mechanism, the following stores were ultimately selected to collect data (consumption per cup) on licorice: Group A (35 stores): The quality of licorice lattes is abnormally low, which is the core area of ​​the problem; Group B (10 stores): The sales of licorice lattes are normal, but it is the absolute mainstay of the licorice series in the store, and its data is crucial for comparative analysis; Group C (5 stores): The overall sales of licorice lattes and licorice series are very high, and they are the "benchmark stores" or "heavy-use stores" of the licorice series. Their data can reflect the usage of raw materials in a "successful" scenario. In total, there were 35 + 10 + 5 = 50 stores, so as to collect the most telling data with the highest efficiency, which can be used to finally determine whether it is "abnormal ratio" or "abnormal herbal quality".

[0053] Furthermore, the method for determining the data collection and analysis of the preparation data for the herbal-type related beverages in different stores is as follows: Based on the collection results of the preparation data of the herbal type of related beverages in the matching stores, the single cup addition ratio data of the related risk beverage type is determined, and the beverages of the related risk beverage type whose addition ratio is not within the standard range are regarded as proportion deviation beverages. Based on the proportion deviation beverage data collected from the matched stores, the proportion of shipments of beverages with proportion deviation in the matched stores is determined in the associated risk beverage type of the herbal type. Based on the data collected from the matched stores of the herbal type and the proportion of beverage shipments in different matched stores, the associated beverage types of the herbal type are determined, and the data collection and analysis method for the preparation data of different stores is used.

[0054] Furthermore, based on the data collected from matching stores of the herbal type and the proportion of beverage shipments in different matching stores, the associated beverage types of the herbal type are determined. The method for collecting and analyzing preparation data from different stores specifically includes: Based on the proportion of beverage shipments with deviations in different matching stores, it is determined whether there are matching stores with shipment proportions greater than a preset shipment proportion threshold. If so, the associated beverage types of the herbal type are determined based on the data of the matching stores with shipment proportions greater than the preset shipment proportion threshold. The method for collecting and analyzing the preparation data of the herbal type in different stores is as follows: If not, if the number of matching stores with shipment proportions greater than the quality defect stores meets the requirements, the probability of quality risk of the herbal type is higher. Therefore, the method for collecting and analyzing the preparation data of the associated beverage types of the herbal type in different stores is to determine the consumption per cup during the preparation process of the herbal type in different associated beverage types in all stores, so as to determine whether the problem is due to abnormal proportions or abnormalities in the herbal type, so as to improve the identification and processing efficiency and determine as soon as possible whether quality inspection is required.

[0055] Furthermore, based on the collected and matched store data where the shipment volume ratio is greater than a preset shipment volume ratio threshold, the associated beverage types of the herbal type are determined. The method for collecting and analyzing preparation data from different stores specifically includes: Stores whose shipment volume ratio exceeds a preset shipment volume ratio threshold are identified as stores with abnormal shipments. The number of stores excluding stores with abnormal shipments is then determined. If the number meets the requirements, the process proceeds to the next step. If not, the large number of stores with abnormal shipments makes it impossible to determine the cause of the quality abnormality. Therefore, it is determined that there is no need to collect and analyze the preparation data of the herbal beverage type in other stores. Instead, the quality inspection of the herbal beverage type should be carried out according to a preset cycle, such as every three or six months.

[0056] Based on the proportion of stores with abnormal shipments and the average proportion of shipments from different stores with abnormal shipments, the identification and matching deviation factor of the herbal type is determined. When the identification and matching deviation factor is less than the preset deviation factor threshold, if the proportion of shipments is greater than the number of stores matching the quality defect stores, it is determined that the consumption per cup of the herbal type in different related beverage types will be determined in all stores. This will help determine whether the problem is due to an abnormal ratio or an abnormal herbal type, thereby improving the identification and processing efficiency and quickly determining whether quality inspection is required.

[0057] It is understandable that when the number of stores with low negative review rates in the associated beverage type meets the requirements, and the proportion of shipments from stores with low negative review rates is greater than that from stores with quality defects, then quality inspection is required.

[0058] Specifically, the negative review rate is calculated as follows: For the licorice latte, the negative review rate = (number of negative reviews about it in the past month) / (total number of cups sold). The definition of "low" is: stores with a negative review rate ranking in the bottom 20% of all stores are defined as "stores with a low negative review rate" (i.e., the 20% with the lowest negative review rates). The identification result: Out of 100 stores, the 20 stores with the lowest negative review rates were identified. We call them the "low negative review store group".

[0059] Step Two: Analyze the sales performance of the "low-negative-review store group" and compare the data: We reviewed the previous data, especially the "shipment volume ratio" (which can be understood as the proportion of "proportion deviation beverages" monitored). We observed that the quantity met the requirements: the "low-negative-review store group" consisted of 20 stores, forming a statistically significant group, which was not a coincidence. Examining the proportion of proportion deviation beverages in the licorice latte of these 20 "low-negative-review stores", we found that without exception, their shipment volume ratios were all significantly higher than those of the "quality defect stores" that we marked as having operational problems (i.e., the stores with high negative review rates found previously), for example, greater than 1%.

[0060] The logic revealed by this phenomenon is as follows: stores with quality defects (proportioning issues): the probability of excessively high negative review rates due to employee operational errors is low. When there are enough stores with low negative review rates, and these stores account for a higher proportion of shipments, this constitutes a strong chain of evidence pointing to the root cause of the problem: it is not a problem with the store's service or operation (that would cause negative reviews), but rather a common, hidden quality defect in the raw materials themselves. Therefore, it is determined that the raw materials should undergo quality inspection.

[0061] Furthermore, if the identification and matching deviation factor is not less than the preset deviation factor threshold, the data acquisition and processing will continue in the identification and matching stores, and it will be periodically determined whether the data acquisition and processing of the herbal type in different associated beverage types needs to be carried out in other stores.

[0062] Step 1: Analyze the initial data collection to identify "drinks with proportion deviations." Standard range: The company stipulates that the standard licorice content for a licorice latte is 5 grams ± 0.5 grams (i.e., 4.5 grams - 5.5 grams is acceptable). Collection results: Through measurement results from the measuring device, among the licorice latte samples collected from 50 stores, it was found that: 40 stores had a content within the standard range, while 10 stores had a content exceeding the standard range (e.g., some only 3 grams, while others were as high as 7 grams). These were marked as "drinks with proportion deviations."

[0063] Step 2: Calculate the "proportion deviation of beverage shipments" for each store. Rules: For these 10 stores with deviations, we estimate the percentage of "deviated beverages" in their total licorice latte shipments. This estimation is based on quality inspection data. The procedure is as follows: Stores A1, A2, and A3: percentage as high as 80% (almost every cup is not up to standard); Store B1: percentage 15% (occasional errors); Stores C1, C2, C3, C4, C5, and C6: percentage between 5% and 10% (sporadic errors). Step 3: Post-Decision Data Collection and Analysis Methods (First-Level Decision) Preset shipment volume percentage threshold: 20%. Determine: Are there any matching stores with a shipment volume percentage > 20%? The answer is: Yes. Stores A1, A2, and A3 have a percentage (80%) > 20%. Decision: Therefore, proceed to the branch process—based on the data from these "abnormal shipment stores," determine the next step.

[0064] Step 4: Based on the in-depth decision-making of "abnormal shipment stores," determine the scope of abnormal stores. Abnormal shipment stores: A1, A2, A3 (3 stores in total). Total number of matching stores collected: 50. Judgment: 50 - 3 = 47. The remaining number of stores (47 stores) is much larger than the number of abnormal stores (3 stores). This number "meets the requirements" (indicating that the problem is concentrated in a few stores and is not a widespread phenomenon). Calculate the "Identification Matching Deviation Factor" and make the final decision. Factor composition: This factor combines the "prevalence of abnormal stores" and the "severity of deviation in abnormal stores." Simplified formula example: Identification Matching Deviation Factor = (Number of abnormal shipment stores / Total number of matching stores collected) * (Average shipment volume percentage of abnormal stores) Calculation: Abnormal store ratio: 3 / 50 = 0.06, average shipment volume ratio of abnormal stores: (80% + 80% + 80%) / 3 = 80%, identification and matching deviation factor = 0.06 * 0.8 = 0.048, preset deviation factor threshold: 0.1 (this threshold can be adjusted according to experience; a higher value indicates greater tolerance for local problems).

[0065] Final judgment: 0.048 < 0.1. Final decision: Because the identification matching deviation factor is less than the preset threshold, it indicates that although the problem is serious, it is very concentrated and not universal. At this time, if the number of stores whose shipment volume ratio is greater than the number of stores whose quality defects are matched meets the requirements, that is, if the number of stores whose shipment volume ratio is greater than the number of stores whose quality defects are matched is not less than 10, the monitoring scope will be expanded to eliminate the problem of raw material quality itself as soon as possible.

[0066] The final data collection and analysis method was determined as follows: the consumption per cup of herbal beverages was determined during the preparation process of different related beverage types in all stores.

[0067] Reasons and Actions: Since the problem is concentrated in only a very small number of stores (A1, A2, A3), the data reliability of the monitoring strategy is relatively high based on the number of stores whose shipment volume is greater than that of the stores with quality defects. The possibility that the "licorice" raw material itself has quality problems has increased. In order to confirm with the highest efficiency whether it is a "general raw material problem" or a "localized operational problem", the optimal strategy is to conduct a rapid survey of licorice usage in all stores of the company.

[0068] Example 2 Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for collecting data for the preparation of a herbal coffee beverage when running the computer program.

[0069] Example 3 Thirdly, the present invention provides a computer storage medium storing a computer program, which, when executed in a computer, causes the computer to execute the aforementioned method for collecting data on the preparation of a herbal coffee beverage.

[0070] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0071] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0072] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for collecting data on the preparation of a herbal coffee beverage, characterized in that, Specifically, it includes: Based on the analysis results of the preparation data of herbal coffee products, the sales data of different beverage types are determined. Based on the sales data, evaluation data and the herbal types associated with the beverage types, the herbal types that need to be quality inspected are determined. Based on the herbal types that need to be quality inspected and the associated herbal type data of the corresponding beverage types, if it is determined that the preparation data collection and processing does not need to be carried out using the preset scheme, proceed to the next step. Based on the sales data of the associated beverage types of the herbal medicine type in different stores and the herbal medicine type data collected and processed using a preset scheme, the matching stores for the collection of the preparation data of the herbal medicine type are determined. Based on the collection results of preparation data of the herbal type associated beverage types in the matched stores, the method for collecting and analyzing preparation data of the herbal type associated beverage types in different stores is used to determine the herbal type associated beverage types.

2. The data acquisition method for the preparation of herbal coffee beverage as described in claim 1, characterized in that, The sales data for the beverage type includes the sales volume of the beverage type in different stores.

3. The data acquisition method for the preparation of herbal coffee beverage as described in claim 1, characterized in that, The herbal type associated with the beverage type is the herbal type required by the beverage type.

4. The data acquisition method for the preparation of herbal coffee beverage as described in claim 1, characterized in that, Based on the sales data of the beverage type, determine the sales volume of the beverage type in different stores; Based on the sales volume of the beverage types in different stores, the types of beverages of interest are determined. Based on the quality feedback data of the beverage types of interest in different stores and the associated herbal types, the herbal types that need to be inspected are determined.

5. The data acquisition method for the preparation of herbal coffee beverage as described in claim 1, characterized in that, The beverage types of interest are those whose average sales volume across different stores exceeds a preset sales volume threshold.

6. The data acquisition method for the preparation of herbal coffee beverage as described in claim 5, characterized in that, Based on quality feedback data of the beverage types of interest in different stores and related herbal types, the types of herbal products that require quality inspection are determined, specifically including: Based on the quality feedback data of the beverage types of interest in different stores, stores with a negative review rate of less than the average negative review rate of different stores are identified and identified as stores with quality defects. Based on the data of stores with quality defects and the associated herbal types, the types of herbs that need to be inspected are determined.

7. The data acquisition method for the preparation of herbal coffee beverage as described in claim 1, characterized in that, It was determined that a pre-defined scheme was not required for data acquisition and processing, specifically including: The beverage type corresponding to the herbal type that needs quality inspection is taken as the associated beverage type of the herbal type that needs quality inspection. Based on the associated herbal type data of the associated beverage types, determine the influencing beverage types among the associated beverage types; Based on the data affecting beverage types, determine whether a preset scheme is needed for data collection and processing.

8. The data acquisition method for the preparation of herbal coffee beverage as described in claim 1, characterized in that, The method for determining the data collection and analysis of the preparation data for the aforementioned herbal-type related beverages in different stores is as follows: Based on the collection results of the preparation data of the herbal type of related beverages in the matching stores, the single cup addition ratio data of the related risk beverage type is determined, and the beverages of the related risk beverage type whose addition ratio is not within the standard range are regarded as proportion deviation beverages. Based on the proportion deviation beverage data collected from the matched stores, the proportion of shipments of beverages with proportion deviation in the matched stores is determined in the associated risk beverage type of the herbal type. Based on the data collected from the matched stores of the herbal type and the proportion of beverage shipments in different matched stores, the associated beverage types of the herbal type are determined, and the data collection and analysis method for the preparation data of different stores is used.

9. A computer system, comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes, when running the computer program, a data acquisition method for the preparation of a herbal coffee beverage as described in any one of claims 1-8.

10. A computer storage medium storing a computer program, which, when executed in a computer, causes the computer to perform a data acquisition method for the preparation of a herbal coffee beverage as described in any one of claims 1-8.

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