Device startup control method and apparatus
By optimizing the generation of product category turnover data, the problem of low prediction accuracy in the control of fast food area equipment was solved, and accurate control of equipment startup was achieved, reducing resource waste and delayed startup.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MULTIPOINT LIFE (CHENGDU) TECH CO LTD
- Filing Date
- 2021-06-24
- Publication Date
- 2026-05-26
AI Technical Summary
In the control of equipment in the fast food area, existing technologies are unable to accurately predict the turnover of various types and levels of goods, leading to equipment control errors and delayed start-up, which cannot meet the timeliness requirements of convenience stores.
By detecting the operation authorization signal, the system obtains the initial predicted item turnover data, generates adjusted predicted item turnover data, combines information from processing and display equipment, optimizes the generation of category item turnover data, and finally controls the equipment to start based on the target category item turnover data.
It improves the accuracy of equipment startup, reduces waste of equipment resources, avoids accidental equipment startup, and meets the timeliness requirements of the convenience store's fast food area.
Smart Images
Figure CN115526646B_ABST
Abstract
Description
Technical Field
[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a device startup control method and apparatus. Background Technology
[0002] In recent years, a growing number of convenience stores have emerged. Compared to other retail methods, convenience stores have distinct characteristics. They prioritize providing convenience to consumers and pursue high efficiency in operation and management. In the modern business world, convenience stores are rapidly rising as a new favorite in the retail industry, expanding globally at an unprecedented pace. The quick-service section is a crucial source of profit for convenience stores, boasting a high profit margin. Almost all items in the quick-service section require processing; if processed items are not sold quickly, they are discarded, and the higher the discard rate, the lower the profit margin. Furthermore, untimely processing leads to missed sales periods, resulting in lost sales opportunities. Scientifically determining the processing volume of various items at different times to control the operation of the quick-service section's storage equipment can significantly improve equipment utilization and has excellent market prospects.
[0003] However, the following technical problems often arise in the process of controlling the equipment in the fast food area:
[0004] First, when controlling the equipment in the fast food area, the characteristics and demand of different categories of goods must be considered, as well as sales demand and customer flow changes at different times. Predicting the turnover of goods based on multiple categories and levels of factors has low accuracy and carries a significant risk of equipment control errors, such as accidental activation of equipment, leading to supply chain disruptions.
[0005] Secondly, the start-up and operation status control of the fast food area equipment is affected by a variety of factors. The weighting or adjustment of multiple factors and conditions affects the accuracy of the final prediction of the turnover of goods and cannot meet the timeliness requirements of the convenience store fast food area, thus resulting in delayed equipment start-up. Summary of the Invention
[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.
[0007] Some embodiments of this disclosure provide a device start-up control method and apparatus to solve one or more of the technical problems mentioned in the background section above.
[0008] In a first aspect, some embodiments of this disclosure provide a device startup control method, the method comprising: detecting whether an operation authorization signal is received from a target terminal device; in response to detecting the operation authorization signal, acquiring an initial predicted item turnover data set; determining to adjust the predicted item turnover data set; generating a processing data set; generating a category item turnover data set; generating a set of target category item turnover data sets; receiving device startup information input by a user from a front-end device; and controlling the startup of the device corresponding to the device startup information based on the set of target category item turnover data sets and the device startup information.
[0009] In some embodiments, generating a set of category item turnover data based on the processing data set includes:
[0010] For each processing data pair in the processing data pair set, the corresponding product category turnover dataset is generated using the following formula to obtain the product category turnover dataset set:
[0011] min(s1+kσ1,y1)+min(s2+kσ2,y2)+…+min(s n +kσ n y n ) = T,
[0012] Where T is the sum of the values of each element in the adjusted predicted item turnover set corresponding to the processing data set, s1, s2, ... s n Let σ1, σ2, ..., σn be the values of each element in the adjusted predicted item turnover set corresponding to this processing data set, and n be the number of elements in the adjusted predicted item turnover set corresponding to this processing data set. n Let y1, y2, ..., y be the standard deviation of the elements in the adjusted predicted item turnover set corresponding to this processing data set, where k is any integer and k is the weight parameter. n This refers to each element in the product turnover dataset corresponding to the processing data set;
[0013] For each category of goods turnover data set in the category of goods turnover data set, the data pair consisting of the category of goods turnover data set and the corresponding goods category is determined as the category of goods turnover data pair set, so as to obtain the category of goods turnover data pair set.
[0014] Secondly, some embodiments of this disclosure provide a device startup control apparatus, the apparatus comprising: a detection unit configured to detect whether an operation authorization signal is received from a target terminal device, wherein the operation authorization signal is a signal generated by a user performing a target operation on a target control; an acquisition unit configured to acquire an initial predicted item turnover data pair set in response to detecting the operation authorization signal, wherein the initial predicted item turnover data pair set includes a first number of initial predicted item turnover data pair sets, the initial predicted item turnover data pair set includes a second number of initial predicted item turnover data pairs, and the initial predicted item turnover data pairs include item category and initial predicted item turnover volume; and a determination unit configured to determine, based on the initial predicted item turnover data pair set, an adjustment mechanism for the device startup control. The system comprises: a set of predicted goods turnover data pairs; a first generation unit configured to generate a set of processing data pairs based on the adjusted predicted goods turnover data pairs, wherein the processing data pairs include a first number of processing data pairs and a second number of processing data pairs; a second generation unit configured to generate a set of category goods turnover data pairs based on the processing data pairs; a third generation unit configured to generate a set of target category goods turnover data pairs based on the category goods turnover data pairs; a receiving unit configured to receive device startup information input by a user from a target terminal device; and a control unit configured to control the startup of the device corresponding to the device startup information based on the set of target category goods turnover data pairs and the device startup information.
[0015] The various embodiments of this disclosure have the following beneficial effects: the equipment start-up control method of some embodiments of this disclosure can generate a set of target category item turnover data pairs, determine the turnover of target category items in a store within a given time period, thereby improving the accuracy of equipment start-up control and reducing equipment resource waste. Specifically, the inventors have found that the reason for the current low accuracy of equipment start-up control is that when controlling the equipment in the fast food area, the characteristics and demand of different categories of items must be considered, as well as the sales demand and customer flow changes at different time periods. The accuracy of predicting item turnover based on multiple types and levels of factors is low, and there is a significant risk of equipment control errors, which may result in the mis-control of equipment startup, leading to supply flow errors. Based on this, firstly, some embodiments of this disclosure obtain an initial predicted item turnover data pair set. The initial predicted item turnover data pair set includes a first number of initial predicted item turnover data pairs, and a second number of initial predicted item turnover data pairs, each including item category and initial predicted item turnover. Secondly, based on the initial set of predicted item turnover data pairs, an adjusted set of predicted item turnover data pairs is determined. Specifically, the adjusted set of predicted item turnover data pairs can be the item turnover volume determined after considering the increased flow of items in the convenience store's fast food section. Then, based on the adjusted set of predicted item turnover data pairs, a processing data set is generated. This processing data set includes a first set of processing data pairs and a second set of processing data pairs. The processing data set can be information related to item turnover volume generated based on the convenience store's processing and display equipment conditions. Thirdly, based on the processing data set, a set of category item turnover data pairs is generated. Specifically, the category item turnover data set can include information related to turnover volume data for different categories of items. Finally, based on the category item turnover data set, a set of target category item turnover data pairs is generated. The information included in the target category item turnover data set can be information related to the turnover volume of items in the convenience store at different time periods. Finally, the system receives device activation information input by the user from the target terminal device. Based on the target category item turnover data and the device activation information, it controls the activation of the device corresponding to the activation information. This method can determine the target category item turnover of the store within a given time period by considering the processing equipment, display equipment, item status, and characteristics of different time periods. This improves the accuracy of device activation control, avoids accidental device activation, and reduces waste of equipment resources. Attached Figure Description
[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.
[0017] Figure 1 This is an architecture diagram of an exemplary system to which some embodiments of this disclosure can be applied;
[0018] Figure 2 These are flowcharts of some embodiments of the device start-up control method according to this disclosure;
[0019] Figure 3 This is an example authorization prompt box;
[0020] Figure 4 This is a flowchart of some embodiments of the device start-up control device according to the present disclosure;
[0021] Figure 5 This is a schematic diagram of the structure of a terminal device suitable for implementing some embodiments of the present disclosure. Detailed Implementation
[0022] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.
[0023] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.
[0024] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.
[0025] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0026] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.
[0027] Figure 1An exemplary system architecture 100 is shown, to which embodiments of the device startup control method of this disclosure can be applied.
[0028] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.
[0029] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications, such as information processing applications, information generation applications, and data analysis applications, can be installed on terminal devices 101, 102, and 103.
[0030] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various terminal devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed on the terminal devices listed above. They can be implemented as multiple software programs or software modules (e.g., used to provide initial predicted item turnover data sets as input), or as a single software program or software module. No specific limitations are imposed here.
[0031] Server 105 can be a server that provides various services, such as a server that stores the initial predicted item turnover data pairs input by terminal devices 101, 102, and 103. The server can process the received initial predicted item turnover data pairs and feed back the processing results (such as the set of target category item turnover data pairs) to the terminal devices.
[0032] It should be noted that the device startup control method provided in this embodiment can be executed by the server 105 or by the terminal device.
[0033] It should be noted that the server 105 can also directly store the initial predicted item turnover data set locally. The server 105 can directly extract the local initial predicted item turnover data set and process it to obtain the target category item turnover data set. In this case, the exemplary system architecture 100 may not include the terminal devices 101, 102, 103 and the network 104.
[0034] It should also be noted that device startup control applications may also be installed on terminal devices 101, 102, and 103. In this case, the processing method may also be executed by terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may not include server 105 and network 104.
[0035] It should be noted that server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide device startup control services), or as a single software program or software module. No specific limitations are made here.
[0036] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.
[0037] Continue to refer to Figure 2 The flowchart 200 illustrates some embodiments of a device startup control method according to the present disclosure. The device startup control method includes the following steps:
[0038] Step 201: Detect whether an operation authorization signal has been received from the target terminal device.
[0039] In some embodiments, the executor of the device startup control method (e.g. Figure 1 The server (as shown) checks whether it has received an operation authorization signal from the target terminal device. The operation authorization signal is a signal generated when the user performs a target operation on the target control. The target terminal device can be a terminal device logged into with the corresponding user account. The terminal device can be a "mobile phone" or a "computer". The target control can be contained in an authorization prompt box. The authorization prompt box can be displayed on the target terminal device. The target control can be a "confirm button".
[0040] Step 202: In response to the detection of an operation authorization signal, obtain the initial predicted item turnover data set.
[0041] In some embodiments, the executor of the device startup control method (e.g. Figure 1The server (shown) responds to the detection of an operation authorization signal by acquiring an initial predicted item turnover data set. The operation authorization signal can be a signal generated by the user corresponding to the initial predicted item turnover data set performing a target operation on the target control. The target control can be contained in an authorization prompt box. The authorization prompt box can be displayed on the target terminal device. The target terminal device can be a terminal device logged into with the account corresponding to the user. The terminal device can be a "mobile phone" or a "computer". The target operation can be a "click operation" or a "swipe operation". The target control can be a "confirm button".
[0042] As an example, the above authorization prompt can be as follows: Figure 3 As shown. The authorization prompt box may include: a prompt information display section 301 and a control 302. The prompt information display section 301 can be used to display a prompt message. The prompt message may be "Allow acquisition of the initial predicted item turnover data set?". The control 302 may be a "Confirm" button or a "Cancel" button.
[0043] Specifically, the initial predicted item turnover data set includes a first number of initial predicted item turnover data sets. The initial predicted item turnover data set includes a second number of initial predicted item turnover data pairs, each including item category and initial predicted item turnover volume. Optionally, the first number is the number of item categories, and the second number is the number of time periods corresponding to the initial predicted item turnover volume. Specifically, the time periods corresponding to the initial predicted item turnover volume can be the number of working hours in the convenience store's fast food section. Specifically, the time periods corresponding to the initial predicted item turnover volume can be 3 PM or 8 PM. The second number can be 5.
[0044] Step 203: Based on the initial set of predicted goods turnover data pairs, determine the set of adjusted predicted goods turnover data pairs.
[0045] In some embodiments, the aforementioned executing entity determines an adjusted set of predicted goods turnover data pairs based on an initial set of predicted goods turnover data pairs.
[0046] Optionally, for each initial predicted item turnover data pair in the initial predicted item turnover data pair set, a third number is added to the initial predicted item turnover value in the initial predicted item turnover data pair set to generate an adjusted predicted item turnover set, thus obtaining an adjusted predicted item turnover set.
[0047] For each adjusted predicted item turnover set in the adjusted predicted item turnover set set, a data pair consisting of this adjusted predicted item turnover set and the data of the corresponding item category is determined as the adjusted predicted item turnover data pair set, thus obtaining the adjusted predicted item turnover data pair set set. Specifically, the adjusted predicted item turnover data pair set set can be the item turnover volume determined after considering the increase in the item turnover of the convenience store's fast food section.
[0048] Step 204: Based on the adjusted set of predicted goods turnover data pairs, generate a set of processing data pairs.
[0049] In some embodiments, the executing entity generates a processing data set based on the adjusted predicted goods turnover data set. The processing data set includes a first number of processing data sets and a second number of processing data pairs.
[0050] Optionally, a set of equipment information, a set of maximum suggested production quantities, and a set of minimum suggested production quantities are determined. Specifically, the set of maximum suggested production quantities includes a first set of maximum suggested production quantities. The maximum suggested production quantity can be the minimum of the sum of adjusted predicted item turnover from the start of the corresponding time period to the second set of time periods for that item category, and the sum of adjusted predicted item turnover from the start of the corresponding time period to the end of the current prediction task. The minimum suggested production quantity can be the adjusted predicted item turnover from the corresponding time period to the next time period.
[0051] For each adjusted predicted item turnover data pair in the set of adjusted predicted item turnover data pairs, a processing data pair is generated based on the adjusted predicted item turnover data pair, the equipment information set, the maximum suggested production quantity set, and the minimum suggested production quantity set, to obtain a processing data pair set. Optionally, for each adjusted predicted item turnover data pair in the set of adjusted predicted item turnover data pairs, the display equipment occupancy index is determined based on the equipment information set and the maximum suggested production quantity set, to obtain a display equipment occupancy index set. Specifically, the display equipment can be equipment used for displaying items in a convenience store. The display equipment occupancy index can characterize the ability of the convenience store's fast food area to display items. Specifically, the unit capacity of the display equipment corresponding to the adjusted predicted item turnover data pair is determined, and the unit capacity of the display equipment corresponding to the adjusted predicted item turnover data pair can be the reciprocal of the number of items that the display equipment corresponding to the adjusted predicted item turnover data pair can display per unit time. The maximum suggested production quantity corresponding to the adjusted predicted item turnover data pair is divided by the unit capacity of the display equipment corresponding to the adjusted predicted item turnover data pair, and then multiplied by 100%. The resulting value is determined as the display equipment occupancy index for the adjusted predicted item turnover data pair, thus obtaining the display equipment occupancy index set.
[0052] For each adjusted predicted item turnover data pair in the adjusted predicted item turnover data pair set, in response to the display equipment occupancy index corresponding to the adjusted predicted item turnover data pair not being greater than "100%", the maximum suggested production quantity corresponding to the adjusted predicted item turnover data pair is determined as the first processing data, so as to obtain the first processing data pair set of the adjusted predicted item turnover data pair set.
[0053] In response to the adjusted forecast item turnover data pair having a corresponding display equipment occupancy rate greater than 100%, a backup occupancy rate for the display equipment is determined. Specifically, the backup occupancy rate is determined by dividing the minimum recommended production quantity corresponding to the adjusted forecast item turnover data pair by the unit capacity of the corresponding display equipment and multiplying by 100%. In response to the adjusted forecast item turnover data pair having a backup occupancy rate not greater than 100%, the minimum recommended production quantity corresponding to the adjusted forecast item turnover data pair is determined as the initial first processing data. The first cycle indicator is set to 0. The following processing volume calculation step one is executed to update the initial first processing data.
[0054] The first step is to add the unit capacity of the corresponding display equipment to the initial first processing data, based on the adjusted predicted item turnover data, to determine the updated first processing data, in response to the first cycle indicator being 0.
[0055] The second step is to divide the updated first processing data by the unit capacity of the display equipment corresponding to the adjusted predicted item turnover data pair and multiply it by 100% to determine the display equipment test occupancy index of the adjusted predicted item turnover data pair.
[0056] Third, in response to the adjustment of the predicted item turnover data pair and the display equipment test occupancy index not exceeding "100%", the updated first processing data is determined as the initial first processing data. In response to the adjustment of the predicted item turnover data pair and the display equipment test occupancy index exceeding "100%", the first cycle index is determined to be 1.
[0057] The initial first processing data is determined as the first processing data.
[0058] For each adjusted predicted item turnover data pair in the set of data pairs, based on the minimum suggested production quantity set and the maximum suggested production quantity set, the processing equipment occupancy index for that data pair is determined to obtain a set of processing equipment occupancy indicators. Specifically, the processing equipment can be the equipment used for processing items in a convenience store. The processing equipment occupancy index characterizes the ability of the convenience store's fast food area to process items. Specifically, the unit capacity of the processing equipment corresponding to the adjusted predicted item turnover data pair is determined. The unit capacity of the processing equipment corresponding to the adjusted predicted item turnover data pair can be the reciprocal of the number of items that the processing equipment can process per unit time. The value obtained by dividing the maximum suggested production quantity corresponding to the adjusted predicted item turnover data pair by the unit capacity of the processing equipment corresponding to the adjusted predicted item turnover data pair and multiplying it by 100% is determined as the processing equipment occupancy index for that data pair, thus obtaining a set of processing equipment occupancy indicators.
[0059] For each adjusted predicted item turnover data pair in the adjusted predicted item turnover data pair set, in response to the processing equipment occupancy index corresponding to the adjusted predicted item turnover data pair not being greater than "100%", the maximum suggested production number corresponding to the adjusted predicted item turnover data pair is determined as the second processing data, so as to obtain the second processing data pair set of the adjusted predicted item turnover data pair set.
[0060] In response to the adjusted forecast item turnover data pair having a corresponding processing equipment occupancy rate greater than 100%, the backup occupancy rate of the processing equipment for that adjusted forecast item turnover data pair is determined. Specifically, the backup occupancy rate of the processing equipment for that adjusted forecast item turnover data pair is determined by dividing the minimum recommended production quantity for that pair by the unit capacity of the processing equipment for that pair and multiplying by 100%. In response to the adjusted forecast item turnover data pair having a backup occupancy rate of less than 100%, the minimum recommended production quantity for that pair is determined as the initial second processing data. The second cycle indicator is set to 0. Step two of the processing volume calculation below is then executed to update the initial second processing data.
[0061] The first step is to add the unit capacity of the corresponding processing equipment to the initial second processing data, based on the adjusted predicted item turnover data, to determine the updated first processing data, in response to the second cycle indicator being 0.
[0062] The second step is to divide the updated second processing data by the unit capacity of the processing equipment corresponding to the adjusted predicted item turnover data pair and multiply it by 100% to determine the processing equipment test occupancy index of the adjusted predicted item turnover data pair.
[0063] Third, in response to the fact that the processing equipment test occupancy index of the adjusted predicted item turnover data pair is not greater than "100%", the updated second processing data is determined as the initial second processing data. In response to the fact that the processing equipment test occupancy index of the adjusted predicted item turnover data pair is greater than "100%", the second cycle index is determined to be 1.
[0064] The initial second processing data is determined as the second processing data.
[0065] Based on the first and second processing data sets of the adjusted predicted goods turnover data set, a further processing data set is generated. Specifically, the smaller value between the first and second processing data sets of the adjusted predicted goods turnover data set is determined as the further processing data set of the adjusted predicted goods turnover data set.
[0066] Step 205: Based on the processing data set, generate a category item turnover data set.
[0067] In some embodiments, the aforementioned executing entity generates a set of category item turnover data based on the set of processing data pairs.
[0068] Optionally, for each processing data pair in the processing data pair set, the following formula is used to generate the corresponding category item turnover dataset, so as to obtain the category item turnover dataset set:
[0069] min(s1+kσ1,y1)+min(s2+kσ2,y2)+…+min(s n +kσ n y n ) = T,
[0070] Where T represents the sum of the values of all elements in the adjusted predicted item turnover set corresponding to this processing data set. s1, s2, ... s n Let σ1, σ2, ..., σn be the value of each element in the adjusted predicted item turnover set corresponding to this processing data set. Let n be the number of elements in the adjusted predicted item turnover set corresponding to this processing data set. n Let y1, y2, ..., y be the standard deviation of the elements in the adjusted predicted item turnover set corresponding to this processing data set. k is any integer, and k is the weighting parameter. n This refers to each element in the product turnover dataset corresponding to this processing data set.
[0071] For each category of goods turnover data set in the category of goods turnover data set, the data pair consisting of the category of goods turnover data set and the corresponding goods category is determined as the category of goods turnover data pair set, so as to obtain the category of goods turnover data pair set.
[0072] The optional content in step 205 above, namely: "the technical formula for generating the category item turnover dataset corresponding to the processing data pair set using an optimized method," is an inventive point of this disclosure, solving the second technical problem mentioned in the background art: "The control of the start-up and operation status of the fast food area equipment is affected by a variety of factors. Setting or adjusting the weights of multiple factors and conditions affects the accuracy of the final predicted item turnover and cannot meet the timeliness requirements of the convenience store fast food area, thus leading to delayed equipment start-up." The factors affecting the equipment start-up control level are often as follows: numerous factors affect the accuracy of the category item turnover dataset used to generate and control the equipment start-up. If these factors are resolved, the start-up control level of the equipment can be improved. To achieve this effect, this disclosure introduces an optimized formula to generate the category item turnover dataset. First, the parameters in the optimized formula are determined. Then, for each processing data pair set in the processing data pair set set, the above formula is used to generate the category item turnover dataset corresponding to that processing data pair set. The processing data set contains various types of information such as equipment status and item status. For multi-factor optimization, it can generate a highly accurate category item turnover volume that takes into account multiple constraints. This generates a category item turnover volume dataset related to controlling equipment startup, thereby improving the startup control level of the equipment and avoiding equipment startup delays, thus solving technical problem two.
[0073] Step 206: Based on the set of product category turnover data pairs, generate a set of target product category turnover data pairs.
[0074] In some embodiments, the aforementioned executing entity generates a set of target category item turnover data pairs based on the category item turnover data pairs.
[0075] Optionally, for each category item turnover data pair in the category item turnover data pair set, generate a target category item turnover data set for that category item turnover data pair set, to obtain a set of target category item turnover data sets. For each category item turnover data pair in the category item turnover data pair set, use the following formula to generate the target category item turnover data set for that category item turnover data pair, to obtain the target category item turnover data set for that category item turnover data pair set: min(c1,m)+min(c2,m)+...+min(c n ,m)=f,
[0076] Where f is the sum of the values of each element in the adjusted predicted item turnover data set corresponding to the item turnover data set for this category, m is any integer, c1, c2, ... c nThis refers to each element in the target category's item turnover data set for this category of item turnover data pair.
[0077] For each target category item turnover data set in the set of target category item turnover data sets, the set of data pairs consisting of the target category item turnover data set and the corresponding item category is determined as the target category item turnover data pair set, so as to obtain the set of target category item turnover data pair sets.
[0078] Step 207: Receive device startup information input by the user from the front-end device.
[0079] In some embodiments, the execution entity receives device startup information input by the user from the front-end device. The device startup information is a signal generated by the user performing a target operation on the target control. The front-end device can be a terminal device logged into with the user's corresponding account. The front-end device can be a "mobile phone" or a "computer." The target control can be contained in an authorization prompt box. The authorization prompt box can be displayed on the front-end device. The target control can be a "confirm button."
[0080] Step 208: Based on the target category item turnover data, the collection of sets and the equipment startup information are used to control the startup of the equipment corresponding to the equipment startup information.
[0081] In some embodiments, the aforementioned executing entity controls the activation of the device corresponding to the device activation information based on the set of target category item turnover data and the device activation information. The corresponding display equipment can be activated based on the set of target category item turnover data to display the corresponding item categories within the set of target category item turnover data, thereby completing the sale of items in the convenience store's fast food section.
[0082] Figure 2 One embodiment provides the following advantages: It detects whether an operation authorization signal is received from the target terminal device; in response to detecting the operation authorization signal, it acquires an initial set of predicted item turnover data pairs; it determines and adjusts the set of predicted item turnover data pairs; it generates a set of processing data pairs; it generates a set of category item turnover data pairs; it generates a set of target category item turnover data pairs; it receives device startup information input by the user from the front-end device; and it controls the startup of the device corresponding to the device startup information based on the set of target category item turnover data pairs and the device startup information. This embodiment generates a set of target category item turnover data pairs to determine the target category item turnover volume of the store within a given time period, thereby improving the accuracy of controlling device startup and reducing equipment resource waste.
[0083] Further reference Figure 4 As an implementation of the above figures and methods, this disclosure provides some embodiments of a device start-up control device, which are similar to... Figure 2 Corresponding to the above-described method embodiments, the device can be specifically applied to various terminal devices.
[0084] like Figure 4 As shown, some embodiments of the device start control device 400 include: a detection unit 401, an acquisition unit 402, a determination unit 403, a first generation unit 404, a second generation unit 405, a third generation unit 406, a receiving unit 407, and a control unit 408. The detection unit 401 is configured to detect whether an operation authorization signal has been received from a target terminal device. The operation authorization signal is a signal generated when a user performs a target operation on a target control. The acquisition unit 402 is configured to acquire an initial predicted item turnover data set in response to detecting the operation authorization signal. The initial predicted item turnover data set includes a first number of initial predicted item turnover data sets, a second number of initial predicted item turnover data sets, and each initial predicted item turnover data set includes an item category and an initial predicted item turnover volume. The determination unit 403 is configured to determine an adjustment to the predicted item turnover data set based on the initial predicted item turnover data set. The first generation unit 404 is configured to generate a set of processing data pairs based on the adjusted predicted item turnover data pairs, wherein the set of processing data pairs includes a first number of processing data pairs and a second number of processing data pairs. The second generation unit 405 is configured to generate a set of category item turnover data pairs based on the set of processing data pairs. The third generation unit 406 is configured to generate a set of target category item turnover data pairs based on the set of category item turnover data pairs. The receiving unit 407 is configured to receive device start information input by the user from the vending machine. The control unit 408 is configured to control the device start corresponding to the device start information based on the set of target category item turnover data pairs and the device start information.
[0085] It is understandable that the units described in the device 400 are related to the reference. Figure 2 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to device 400 and the units contained therein, and will not be repeated here.
[0086] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing a terminal device according to embodiments of the present disclosure. Figure 5The terminal device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.
[0087] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.
[0088] The following components are connected to I / O interface 505: storage section 506, including hard disks, etc.; and communication section 507, including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 507 performs communication processing via a network such as the Internet. Drive 508 is also connected to I / O interface 505 as needed. Removable media 509, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 508 as needed so that computer programs read from them can be installed into storage section 506 as needed.
[0089] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 507, and / or installed from removable medium 509. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined in the methods of this disclosure. It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0090] Computer program code for performing the operations of this disclosure can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0092] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features disclosed in this disclosure that have similar functions.
Claims
1. A device start-up control method, comprising: Detect whether an operation authorization signal has been received from the target terminal device, wherein the operation authorization signal is a signal generated by the user performing a target operation on the target control; In response to the detection of an operation authorization signal, an initial predicted item turnover data set is obtained, wherein the initial predicted item turnover data set includes a first number of initial predicted item turnover data sets, the initial predicted item turnover data set includes a second number of initial predicted item turnover data pairs, and the initial predicted item turnover data pairs include item category and initial predicted item turnover volume. Based on the initial set of predicted goods turnover data pairs, determine the set of adjusted predicted goods turnover data pairs. Based on the aforementioned adjusted predicted goods turnover data set, a processing data set is generated, including: Determine the equipment information set, the maximum recommended production quantity set, and the minimum recommended production quantity set; For each adjusted predicted item turnover data pair in the set of adjusted predicted item turnover data pairs, based on the adjusted predicted item turnover data pair, the set of equipment information, the set of maximum suggested production quantity, and the set of minimum suggested production quantity, a processing data pair is generated for the adjusted predicted item turnover data pair to obtain the processing data pair set set, wherein the processing data pair set set includes a first number of processing data pairs and the processing data pair set includes a second number of processing data pairs; Based on the aforementioned processing data set, a category item turnover data set is generated; Based on the set of circulation volume data pairs for the aforementioned product category, generate a set of circulation volume data pairs for the target product category. Receive device startup information input by the user from the front-end device; Based on the target category item turnover data, the set of collections and the device startup information are used to control the startup of the device corresponding to the device startup information.
2. The method according to claim 1, wherein, The target control is displayed in the authorization prompt box, which is displayed on the target terminal device.
3. The method according to claim 2, wherein, The first number is the number of item categories, and the second number is the number of time periods corresponding to the initial predicted item turnover.
4. The method according to claim 3, wherein, The step of determining the set of data pairs for adjusting the predicted goods turnover volume based on the initial set of predicted goods turnover volume data pairs includes: For each initial predicted item turnover data set in the initial predicted item turnover data set, add a third number to the initial predicted item turnover value in the initial predicted item turnover data set to generate an adjusted predicted item turnover set, so as to obtain the adjusted predicted item turnover set. For each set of adjusted predicted item turnover in the set of adjusted predicted item turnover sets, the data pair consisting of the adjusted predicted item turnover set and the data of the item category corresponding to the adjusted predicted item turnover set is determined as the adjusted predicted item turnover data pair set, so as to obtain the adjusted predicted item turnover data pair set set.
5. The method according to claim 1, wherein, The process data set generated based on the adjusted predicted goods turnover data set, the equipment information set, the maximum suggested production quantity set, and the minimum suggested production quantity set includes: For each adjusted predicted item turnover data pair in the set of adjusted predicted item turnover data pairs, based on the set of equipment information and the set of maximum suggested production quantity, the display equipment occupancy index of the adjusted predicted item turnover data pair is determined to obtain the set of display equipment occupancy indexes. For each pair of adjusted predicted item turnover data in the set of adjusted predicted item turnover data, in response to the display equipment occupancy index corresponding to the adjusted predicted item turnover data pair not being greater than "100%", the maximum suggested production quantity corresponding to the adjusted predicted item turnover data pair is determined as the first processing data, so as to obtain the first processing data set of the set of adjusted predicted item turnover data. For each adjusted predicted item turnover data pair in the set of adjusted predicted item turnover data pairs, based on the minimum suggested production number set and the maximum suggested production number set, the processing equipment occupancy index of the adjusted predicted item turnover data pair is determined to obtain the processing equipment occupancy index set. For each adjusted predicted item turnover data pair in the adjusted predicted item turnover data pair set, in response to the processing equipment occupancy index corresponding to the adjusted predicted item turnover data pair not being greater than "100%", the maximum suggested production number corresponding to the adjusted predicted item turnover data pair is determined as the second processing data, so as to obtain the second processing data pair set of the adjusted predicted item turnover data pair set; Based on the first processing data set and the second processing data set of the adjusted and predicted goods turnover data set, the processing data set of the adjusted and predicted goods turnover data set is generated.
6. The method according to claim 5, wherein, The process of generating a set of product category turnover data based on the processing data set includes: For each processing data pair in the processing data pair set, generate the corresponding category item turnover data set to obtain the category item turnover data set set; For each category of goods turnover data set in the category of goods turnover data set, the data pair consisting of the category of goods turnover data set and the corresponding goods category is determined as the category of goods turnover data pair set, so as to obtain the category of goods turnover data pair set.
7. The method according to claim 6, wherein, The process of generating a set of target category item turnover data pairs based on the aforementioned category item turnover data pairs includes: For each category of goods turnover data pair in the set of category of goods turnover data pairs, generate a target category of goods turnover data set for that category of goods turnover data pair, so as to obtain a set of target category of goods turnover data sets. For each target category item turnover data set in the set of target category item turnover data sets, the set of data pairs consisting of the target category item turnover data set and the corresponding item category is determined as the target category item turnover data pair set, so as to obtain the set of target category item turnover data pair sets.
8. The method according to claim 7, wherein, The target category item turnover data set for generating the item turnover data set includes: For each item turnover data pair in the set of item turnover data pairs, the target item turnover data set for that item turnover data pair is generated using the following formula, thus obtaining the target item turnover data set for the set of item turnover data pairs: ， Where f is the sum of the values of all elements in the adjusted predicted item turnover volume set corresponding to the item turnover volume data set for this category, and m is any integer. , ,… This refers to each element in the target category's item turnover data set for this category of item turnover data pair.
9. A device for starting control, comprising: The detection unit is configured to detect whether an operation authorization signal is received from the target terminal device, wherein the operation authorization signal is a signal generated by the user performing a target operation on the target control; The acquisition unit is configured to acquire an initial predicted item turnover data pair set in response to detecting an operation authorization signal, wherein the initial predicted item turnover data pair set set includes a first number of initial predicted item turnover data pair sets, the initial predicted item turnover data pair set includes a second number of initial predicted item turnover data pairs, and the initial predicted item turnover data pairs include item category and initial predicted item turnover volume; The determining unit is configured to determine an adjusted set of predicted item turnover data pairs based on the initial set of predicted item turnover data pairs. The first generation unit is configured to generate a set of processing data pairs based on the adjusted predicted goods turnover data pairs, including: Determine the equipment information set, the maximum recommended production quantity set, and the minimum recommended production quantity set; For each adjusted predicted item turnover data pair in the set of adjusted predicted item turnover data pairs, based on the adjusted predicted item turnover data pair, the set of equipment information, the set of maximum suggested production quantity, and the set of minimum suggested production quantity, a processing data pair is generated for the adjusted predicted item turnover data pair to obtain the processing data pair set set, wherein the processing data pair set set includes a first number of processing data pairs and the processing data pair set includes a second number of processing data pairs; The second generation unit is configured to generate a set of category item turnover data based on the set of processing data pairs. The third generation unit is configured to generate a set of target category item turnover data pairs based on the set of turnover data pairs of the category items. The receiving unit is configured to receive device startup information input by the user from the front-end device; The control unit is configured to control the startup of the device corresponding to the device startup information based on the set of the target category item turnover data and the device startup information.