Intelligent store operation management system based on big data analysis

Abstract

The invention discloses an intelligent store operation management system based on big data analysis, and the system comprises a store information collection module which is used for carrying out the collection of store information, and obtaining the store information; the article information acquisition module is used for acquiring related article information in the store to obtain article information; the store personnel acquisition module is used for acquiring information of personnel in a store and acquiring the information of the personnel in the store; the recycling bin information collection module is used for collecting recycling bin information to obtain the recycling bin information; the comprehensive analysis module is used for processing the collected store information, article information, store personnel information and recycling bin information to generate related management information; and the information export module is used for sending out the generated related management information. According to the invention, more intelligent store operation management can be carried out.

Description

Technical Field

[0001] The present invention relates to the field of management systems, and in particular to an intelligent store operation and management system based on big data analysis. Background Art

[0002] Smart recycling store management is a comprehensive process, which involves multiple aspects, including personnel management, recycling bin management, data management, and customer service. Smart recycling store management is a complex and meticulous process, which requires the store management to continuously optimize and improve personnel management, recycling bin management, data management, and customer service. Only in this way can we ensure that smart recycling stores can operate efficiently and stably; In the process of store operation and management, the store operation management system will be used.

[0003] The existing store management systems mostly only manage the content of a single store, which requires the use of multiple systems in combination to achieve integrated store management, which has a certain impact on the use of store management systems. Therefore, an intelligent store management system based on big data analysis is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing store operation and management system only manages the content of a single store, resulting in the need to use a combination of multiple systems to achieve integrated store management, which has a certain impact on the use of the store operation and management system. An intelligent store operation and management system based on big data analysis is provided.

[0005] The present invention solves the above technical problems through the following technical solutions, which include: The store information collection module is used to collect store information and obtain store information; The item information collection module is used to collect relevant item information in the store and obtain item information; The store personnel collection module is used to collect the personnel information in the store and obtain the store personnel information; A recycling bin information collection module is used to collect recycling bin information and obtain recycling bin information; Comprehensive analysis module, used to process the collected store information, item information, store personnel information and recycling bin information to generate relevant management information; The information export module is used to send out the generated relevant management information.

[0006] Furthermore, the relevant management information includes store management information, and the store management information includes determining and changing the selected site; The process of obtaining store management information is as follows: Extract the acquired store information, which includes road information around the store, store warehouse information and store location information; Process the road information around the store to obtain the traffic convenience index; Process the store warehouse information and obtain the warehouse index; Process the store location information to obtain the store location index; Then, the traffic convenience index, warehouse index and store location index are comprehensively calculated and processed to obtain a comprehensive evaluation index; When the comprehensive evaluation index is greater than or equal to the preset value, the store management information is generated. At this time, the store management information is used to determine the site selection; When the comprehensive evaluation index is less than the preset value, store management information is generated. At this time, the store management information is to change the selection point.

[0007] Furthermore, the process of obtaining the traffic convenience index is as follows: extracting the acquired road information around the store, the road information around the store including the road width information and road congestion conditions of the three roads closest to the store; The road traffic jam condition is the number of traffic jams within a preset time period; Process the road width information and traffic jam conditions of a single road to obtain road assessment parameters of the single road; Calculate the road evaluation parameters of the three roads according to the preset first calculation rule to obtain the traffic convenience index; The process of obtaining the road evaluation parameters of a single road is as follows: extract the road width information and the road congestion condition, mark the road width information as H1, mark the road congestion condition as H2, assign a weight U1 to H1, a weight U2 to H2, U1+U2=1, U2>U1; The road evaluation parameter Hh of a single road is obtained through the formula H1*U1+H2*U2=Hh; The specific content of the first calculation rule is as follows: extract three road evaluation parameters and mark them as Hh1, Hh2, and Hh3 according to the distance from the store from far to near; The traffic convenience index Hj is obtained through the formula Hh1*α+Hh2*β+Hh3*γ=Hj, where α, β and γ are correction parameters, α∶β∶γ=2∶3∶5, α+β+γ=1; The process of obtaining the warehouse index is as follows: extract the acquired store warehouse information, which includes the number of warehouse fire protection facilities, warehouse capacity utilization rate and the number of abnormal stores around the warehouse; The warehouse capacity utilization rate is the ratio of the actual warehouse area to the marked total area. The abnormal stores around the warehouse are restaurants and other stores around the warehouse that use open flames. The warehouse index is obtained by calculating the number of warehouse fire protection facilities, the warehouse capacity utilization rate and the number of abnormal stores around the warehouse according to the second calculation rule; The specific content of the second calculation rule is as follows: the number of warehouse fire protection facilities is marked as V1, the warehouse capacity utilization rate is marked as V2, and the number of abnormal stores around the warehouse is marked as V3; First, score V1 on a scale of 1-100. The score of V1 is proportional to V1. The larger V1 is, the larger the score of V1 is. The warehouse capacity utilization rate is marked as V2 and scored. The score range is 1-100. The score of V2 is proportional to V2. The larger V2 is, the larger the score of V2 is. Score the number of abnormal stores V3 around the warehouse, with a score range of 1-100. The score size of V3 is inversely proportional to V3. The smaller V3 is, the larger the score of V3 is; Then calculate the sum of the scores of V1, V2 and V3 to get the warehouse index; The process of obtaining the store location index is as follows: extracting the obtained store location information, importing the store location information into an Internet database, retrieving the number of residential buildings and the number of other similar service businesses within a preset range of the store location from the Internet database, and calculating the number of residential buildings and the number of other similar service businesses within the preset range of the store location according to a third calculation rule to obtain the store location index; The specific content of the third calculation rule is as follows: extract the number of residential buildings and other similar service businesses within the preset range of the store location, mark the residential buildings within the preset range of the store location as D1, and mark the number of other similar service businesses as D2; Calculate the ratio of D1 and D2 to get the store location index; The specific process of comprehensively calculating and processing the traffic convenience index, warehouse index and store location index to obtain the comprehensive evaluation index is as follows: The transportation convenience index is marked as F1, the warehouse index is marked as F2, and the store location index is marked as F3; Assign a correction value G1 to F1, a correction value G2 to F2, and a correction value G3 to F3, G1+G2+G3=1, G1>G2>G3; Through the formula F1*G1+F2*G2+F3*G3=Ff, the comprehensive evaluation index Ff is obtained.

[0008] Furthermore, the relevant management information includes item management information, and the process of obtaining the item management information is as follows: Extract the acquired item information, the item information includes the purchase frequency of the store materials and the consumption frequency of the store materials; Then extract the store personnel information, which includes the number of store business personnel; Import the store staff quantity information, the store material purchase frequency and the store material consumption frequency into the preset model library, establish the model, and construct a real-time staff and material consumption model; Then, the personnel and material consumption models of other stores are extracted from the preset big database; The real-time personnel and material consumption model is compared with the personnel and material consumption models of other stores to obtain comparison parameters. When the comparison parameters are less than the preset values, the item management information is generated.

[0009] Furthermore, the relevant management information includes personnel management information, and the personnel management information includes personnel scheduling information and personnel abnormality prompt information; The specific process of obtaining the personnel management information is as follows: extracting the obtained personnel information, the personnel information including personnel sign-in information; Then extract the obtained recycling bin information, the recycling bin information includes the current recycling status information of the recycling bin; The current recycling status information of the recycling bin includes whether the recycling volume meets the standard or not; When the current recycling status information of the recycling bin is that the recycling reaches the standard, the personnel dispatch information is generated; The personnel sign-in information is extracted and compared with the table of personnel who should sign in on the day to obtain the comparison result. If the comparison result shows that there are personnel who have not signed in, a personnel abnormality prompt message is generated.

[0010] Furthermore, the personnel dispatching information is used for store personnel dispatching, which is used for dispatching personnel to perform the task of collecting recycled items in the recycling bin; The specific process of store personnel scheduling based on personnel scheduling information is as follows: Extracting personnel information and recycling bin information, the personnel information also includes recycling personnel location information, and the recycling bin information also includes recycling bin location information; First, extract the location information of the recycling bin where the recycled items need to be collected, mark it as the initial location Z, then extract the location information of all recycling personnel, extract the three closest personnel from all the recycling personnel location information, and mark them as the initial allocation personnel; Then collect the relevant information of the initial assigned personnel, including the current number of tasks of the initial personnel, the location of the next task point of the initial personnel and the current load of the initial personnel's vehicle; The current number of tasks, the location of the next task point of the initial personnel, and the current load of the initial personnel's vehicle are processed to obtain the initial allocation score; Then the person with the minimum initial allocation score is selected as the final allocation person.

[0011] Furthermore, the process of obtaining the initial allocation score is as follows: extracting the current task quantity, the next task point position of the initial personnel and the current load of the vehicle of the initial personnel, measuring the distance between the next task point position of the initial personnel and the initial position Z, and obtaining the evaluation distance; The evaluation distance is marked as E1, the current task volume is marked as E2, and the current load of the initial personnel and vehicle is marked as E3; Assign a correction value Y1 to E1, a correction value Y2 to E2, and a correction value Y3 to E3; Y3<Y1<Y2, Y1+Y2+Y3=1; Through the formula E1*Y1+E2*Y2+E3*Y3=Ee, the initial allocation score Ee is obtained.

[0012] Furthermore, the relevant management information also includes recycling bin management information, and the recycling bin management information includes recycling bin addition information and recycling bin cancellation information; The specific process of obtaining the recycling bin management information is as follows: Extract the recycling bin information, including the setting time of the recycling bin, the number of registered users of the recycling bin, the number of valid users, the total recycling volume of the recycling bin, and the total profit information of the recycling bin; Calculate the ratio of the number of registered users of the recycling box to the length of time the recycling box is set up, and obtain the daily average number of registered users; The number of valid users refers to users who have recycled items at least twice after registration. The ratio of the number of valid users to the number of registered users of the recycling bin is calculated to obtain the percentage of valid users. Then calculate the ratio of the total recycling volume of the recycling bin to the setting time of the recycling bin to obtain the average daily recycling volume; When any three or more of the following conditions occur: the daily average registered user parameter is greater than or equal to the preset value, the effective user ratio is greater than or equal to the preset value, the daily average recycling volume is greater than or equal to the preset value, and the total profit information of the recycling bin is greater than or equal to the preset value, the recycling bin addition information is generated; When any three or more of the following conditions occur: the daily average registered user parameter is less than the preset value, the effective user ratio is less than the preset value, the daily average recycling volume is less than the preset value, and the total profit information of the recycling bin is less than the preset value, the recycling bin cancellation information is generated.

[0013] Compared with the prior art, the present invention has the following advantages: the intelligent store operation and management system based on big data analysis, the system conducts in-depth mining and comprehensive analysis of multi-dimensional information such as stores, items, personnel and recycling bins through a comprehensive analysis module, providing managers with a scientific and objective basis for decision-making.

[0014] The process of acquiring store management information takes into account multiple key factors such as transportation convenience, warehouse conditions and store location. Through quantitative evaluation and comprehensive calculation, the store location strategy can be accurately determined to improve the accuracy and efficiency of decision-making.

[0015] The process of acquiring item management information builds a real-time personnel and material consumption model and compares it with the models of other stores. This can promptly identify problems in material management, optimize material procurement and inventory management, and reduce operating costs.

[0016] The process of acquiring personnel management information can timely detect personnel anomalies and recycling task requirements by monitoring personnel check-ins and recycling box status, reasonably dispatch personnel resources, and improve operational efficiency.

[0017] The generation process of personnel scheduling information takes into account multiple factors such as the location of the recovery personnel, the amount of tasks, and the vehicle load. Through quantitative scoring and optimal selection, it achieves the optimal allocation of personnel resources and improves the execution efficiency and effectiveness of the recovery task.

[0018] The process of acquiring recycling bin management information provides a scientific basis for the addition and removal of recycling bins by evaluating the usage and economic benefits of recycling bins, thereby avoiding waste and idleness of resources.

[0019] The entire system is centered on big data analysis. By collecting, processing and analyzing large amounts of data, it provides a new perspective and method for store management, improves the scientificity and accuracy of management, promotes management innovation and service upgrades, and lays a solid foundation for the long-term development of stores.

[0020] It can also be customized and adjusted according to the actual conditions and needs of different stores. As the store business expands and management deepens, new data and analysis models can be continuously introduced to improve the functions and performance of the system, realizing a series of management processes such as store site selection, store personnel management, and recycling management and scheduling, making the system more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0022] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0023] like Figure 1 As shown, this embodiment provides a technical solution: an intelligent store operation and management system based on big data analysis, including: The store information collection module is used to collect store information and obtain store information; The item information collection module is used to collect relevant item information in the store and obtain item information; The store personnel collection module is used to collect the personnel information in the store and obtain the store personnel information; A recycling bin information collection module is used to collect recycling bin information and obtain recycling bin information; Comprehensive analysis module, used to process the collected store information, item information, store personnel information and recycling bin information to generate relevant management information; The information export module is used to send out the generated relevant management information.

[0024] The relevant management information includes store management information, and the store management information includes determining and changing the selected site; The process of obtaining store management information is as follows: Extract the acquired store information, which includes road information around the store, store warehouse information and store location information; Process the road information around the store to obtain the traffic convenience index; Process the store warehouse information and obtain the warehouse index; Process the store location information to obtain the store location index; Then, the traffic convenience index, warehouse index and store location index are comprehensively calculated and processed to obtain a comprehensive evaluation index; When the comprehensive evaluation index is greater than or equal to the preset value, the store management information is generated. At this time, the store management information is used to determine the site selection; When the comprehensive evaluation index is less than the preset value, store management information is generated, and the store management information is to change the selected location; The above process improves the efficiency of store location decision-making. By automatically processing road information, warehouse information and location information around the store, the system can quickly generate a comprehensive evaluation index, thereby greatly shortening the store location decision-making cycle.

[0025] Managers can make decisions quickly based on the comprehensive evaluation index, avoiding the tedious data analysis and manual judgment in the traditional site selection process and improving decision-making efficiency.

[0026] The system can accurately evaluate the advantages and disadvantages of different store locations and help managers rationally allocate resources such as funds, manpower and materials.

[0027] For stores with a higher comprehensive evaluation index, we can increase investment to improve the store's operational capabilities and market competitiveness; for stores with a lower evaluation index, we can consider optimization or adjustment to avoid wasting resources.

[0028] This process improves the intelligence level of the intelligent store operation and management system by introducing data analysis and comprehensive evaluation mechanisms.

[0029] The system can automatically adjust evaluation criteria and weights based on real-time data and market changes to ensure the accuracy and adaptability of site selection decisions.

[0030] Through comprehensive evaluation indexes, the system can identify potential operational risks, such as inconvenient transportation, insufficient warehouse capacity or poor location.

[0031] Managers can take timely measures based on the evaluation results, such as changing the site, optimizing the warehouse layout or adjusting the operation strategy, so as to reduce operating risks and improve the profitability of the store.

[0032] In summary, the application of the above process in the intelligent store management system has brought many benefits, including improving decision-making efficiency, optimizing resource allocation, enhancing the level of system intelligence, improving user experience, and reducing operating risks. These benefits help to improve the competitiveness and profitability of stores and lay a solid foundation for the long-term development of the enterprise.

[0033] The process of obtaining the traffic convenience index is as follows: extracting the acquired road information around the store, the road information around the store including the road width information and road congestion conditions of the three roads closest to the store; The road traffic jam condition is the number of traffic jams within a preset time period; Process the road width information and traffic jam conditions of a single road to obtain road assessment parameters of the single road; Calculate the road evaluation parameters of the three roads according to the preset first calculation rule to obtain the traffic convenience index; The process of obtaining the road evaluation parameters of a single road is as follows: extract the road width information and the road congestion condition, mark the road width information as H1, mark the road congestion condition as H2, assign a weight U1 to H1, a weight U2 to H2, U1+U2=1, U2>U1; The road evaluation parameter Hh of a single road is obtained through the formula H1*U1+H2*U2=Hh; The specific content of the first calculation rule is as follows: extract three road evaluation parameters and mark them as Hh1, Hh2, and Hh3 according to the distance from the store from far to near; The traffic convenience index Hj is obtained through the formula Hh1*α+Hh2*β+Hh3*γ=Hj, where α, β and γ are correction parameters, α∶β∶γ=2∶3∶5, α+β+γ=1; The process of obtaining the warehouse index is as follows: extract the acquired store warehouse information, which includes the number of warehouse fire protection facilities, warehouse capacity utilization rate and the number of abnormal stores around the warehouse; Warehouse capacity utilization is the ratio of the actual warehouse area to the marked total area; The warehouse index is obtained by calculating the number of warehouse fire protection facilities, the warehouse capacity utilization rate and the number of abnormal stores around the warehouse according to the second calculation rule; The specific content of the second calculation rule is as follows: the number of warehouse fire protection facilities is marked as V1, the warehouse capacity utilization rate is marked as V2, and the number of abnormal stores around the warehouse is marked as V3; First, score V1 on a scale of 1-100. The score of V1 is proportional to V1. The larger V1 is, the larger the score of V1 is. The warehouse capacity utilization rate is marked as V2 and scored. The score range is 1-100. The score of V2 is proportional to V2. The larger V2 is, the larger the score of V2 is. Score the number of abnormal stores V3 around the warehouse, with a score range of 1-100. The score size of V3 is inversely proportional to V3. The smaller V3 is, the larger the score of V3 is; Then calculate the sum of the scores of V1, V2 and V3 to get the warehouse index; The process of obtaining the store location index is as follows: extracting the obtained store location information, importing the store location information into an Internet database, retrieving the number of residential buildings and the number of other similar service businesses within a preset range of the store location from the Internet database, and calculating the number of residential buildings and the number of other similar service businesses within the preset range of the store location according to a third calculation rule to obtain the store location index; The specific content of the third calculation rule is as follows: extract the number of residential buildings and other similar service businesses within the preset range of the store location, mark the residential buildings within the preset range of the store location as D1, and mark the number of other similar service businesses as D2; Calculate the ratio of D1 and D2 to get the store location index; The specific process of comprehensively calculating and processing the traffic convenience index, warehouse index and store location index to obtain the comprehensive evaluation index is as follows: The transportation convenience index is marked as F1, the warehouse index is marked as F2, and the store location index is marked as F3; Assign a correction value G1 to F1, a correction value G2 to F2, and a correction value G3 to F3, G1+G2+G3=1, G1>G2>G3; Through the formula F1*G1+F2*G2+F3*G3=Ff, the comprehensive evaluation index Ff is obtained; The above process extracts and processes the road information, warehouse information and location information around the store in detail, and conducts a comprehensive evaluation through multiple dimensions and indicators, making the evaluation results more detailed and accurate.

[0034] By quantifying various information and converting them into index form, this process provides data support for store location selection and operation management. When calculating each index, the process takes into account the weight distribution of different factors, such as the weight of road width and traffic congestion, the number of warehouse fire protection facilities, capacity utilization and the weight of the number of abnormal stores, etc. These weight distributions are based on actual conditions and the principle of importance, making the evaluation results more reasonable.

[0035] This process allows adjustment of correction parameters and weight distribution according to specific circumstances to suit the needs of different market environments and store types, thus improving the flexibility and applicability of the assessment.

[0036] By extracting, processing and calculating information, the process can automatically generate various indexes and comprehensive evaluation indexes, reducing the time and cost of manual operations and improving work efficiency.

[0037] The evaluation results can help managers identify potential business risks, such as inconvenient transportation, insufficient warehouse capacity or poor location, so that they can take timely measures to improve and optimize.

[0038] The relevant management information includes item management information, and the process of obtaining the item management information is as follows: Extract the acquired item information, the item information includes the purchase frequency of the store materials and the consumption frequency of the store materials; Then extract the store personnel information, which includes the number of store business personnel; Import the store staff quantity information, the store material purchase frequency and the store material consumption frequency into the preset model library, establish the model, and construct a real-time staff and material consumption model; Then, the personnel and material consumption models of other stores are extracted from the preset big database; Compare the real-time personnel and material consumption model with the personnel and material consumption models of other stores to obtain the comparison parameters. When the comparison parameters are less than the preset values, the item management information is generated. Through the generated item management information, we can understand whether the material consumption status in the store is normal, and then make timely adjustments to avoid unnecessary waste of store materials; The specific process of model building and generating item management information is as follows: Data import and establishment of real-time personnel and material consumption model; Data collection and preprocessing: Collect store staff quantity information from the store management system, including the number of employees in each position, shift schedule, etc.

[0039] Obtain the purchase frequency and consumption frequency data of store materials from the supply chain management system or inventory system, including material type, purchase time, purchase quantity, consumption time, consumption quantity, etc.

[0040] Clean and preprocess the collected data to remove duplicate, invalid or abnormal data to ensure data accuracy and consistency.

[0041] Import data into the model library: import the pre-processed store staff quantity information, purchase frequency and consumption frequency data into the preset model library.

[0042] The model library should support functions such as data import, storage, processing and analysis, providing a basis for subsequent model establishment.

[0043] Establish a real-time personnel and material consumption model: Use the algorithms and tools in the model library to train and analyze the imported data to build a real-time personnel and material consumption model.

[0044] The model should be able to reflect changes in the number of store personnel and material consumption in real time, and provide decision support for subsequent item management.

[0045] Other store personnel material consumption model extraction: Big database selection: select personnel and material consumption models of other stores that are similar or related to the current store from the preset big database.

[0046] The big database should contain personnel and material consumption data of multiple stores, as well as corresponding models and analysis results.

[0047] Model extraction: extracting the personnel and material consumption models of other stores that are similar or related to the current store from the big database.

[0048] The extracted model should contain information such as model structure, parameters, and performance so that it can be compared with the real-time personnel and material consumption model.

[0049] Model comparison and item management information generation: Model comparison: compare the real-time personnel and material consumption model with the personnel and material consumption models of other stores to obtain comparison parameters.

[0050] The comparison parameters may include the similarity of model structures, the differences in parameters, the accuracy of prediction results, etc.

[0051] Comparison parameter judgment: According to the preset comparison parameter threshold, the degree of difference between the real-time personnel and material consumption model and other store models is determined.

[0052] If the comparison parameter is less than the preset value, it means that there is a significant difference between the real-time personnel material consumption model and other store models, and further analysis of the reasons is needed.

[0053] Item management information is generated. When the comparison parameter is less than the preset value, the system automatically generates item management information.

[0054] Item management information can include material demand forecasts, inventory adjustment suggestions, staffing optimization, etc. to guide the store's item management and operational decisions.

[0055] Subsequent analysis and optimization: conduct subsequent analysis and verification of the generated item management information to ensure its accuracy and effectiveness.

[0056] Based on the analysis results, the real-time personnel and material consumption model is continuously optimized and improved to improve the accuracy and reliability of the model.

[0057] Through the above steps, real-time monitoring and prediction of store personnel’s material consumption can be achieved, and item management information can be generated based on the model comparison results with other stores, providing strong support for the store’s item management and operational decisions.

[0058] The relevant management information includes personnel management information, which includes personnel scheduling information and personnel abnormality prompt information; The specific process of obtaining the personnel management information is as follows: extracting the obtained personnel information, the personnel information including personnel sign-in information; Then extract the obtained recycling bin information, the recycling bin information includes the current recycling status information of the recycling bin; The current recycling status information of the recycling bin includes whether the recycling volume meets the standard or not; When the current recycling status information of the recycling bin is that the recycling reaches the standard, the personnel dispatch information is generated; Extract the personnel sign-in information, compare the personnel sign-in information with the list of personnel who should sign in on the day, and obtain the comparison result. If the comparison result shows that there are personnel who have not signed in, a personnel abnormality prompt message is generated; The above process improves operational efficiency. By real-time monitoring of the current recycling status information of the recycling bin, the store can respond quickly to recycling needs. When the recycling volume reaches the standard, personnel scheduling information is generated in a timely manner to ensure the efficiency of material recycling and resource reuse.

[0059] The generation of personnel scheduling information helps to optimize the store’s human resource allocation, avoid idle or overly busy personnel, and thus improve overall operational efficiency.

[0060] Enhance management accuracy. By comparing personnel sign-in information with the list of personnel who should sign in on the same day, the system can accurately identify personnel who have not signed in and generate personnel abnormality prompt information.

[0061] This refined management helps stores identify problems in a timely manner, such as employee lateness and absenteeism, so that they can take corresponding measures to ensure the normal operation of the store.

[0062] The personnel dispatch information is used to dispatch store personnel to perform the task of collecting recycled items from the recycling bin; The specific process of store personnel scheduling based on personnel scheduling information is as follows: Extracting personnel information and recycling bin information, the personnel information also includes recycling personnel location information, and the recycling bin information also includes recycling bin location information; First, extract the location information of the recycling bin where the recycled items need to be collected, mark it as the initial location Z, then extract the location information of all recycling personnel, extract the three closest personnel from all the recycling personnel location information, and mark them as the initial allocation personnel; Then collect the relevant information of the initial assigned personnel, including the current number of tasks of the initial personnel, the location of the next task point of the initial personnel and the current load of the initial personnel's vehicle; The current number of tasks, the location of the next task point of the initial personnel, and the current load of the initial personnel's vehicle are processed to obtain the initial allocation score; Then the person with the minimum initial allocation score is selected as the final allocation person.

[0063] The process of obtaining the initial allocation score is as follows: extract the current task quantity, the next task point position of the initial personnel and the current load of the initial personnel vehicle, measure the distance between the next task point position of the initial personnel and the initial position Z, and obtain the evaluation distance; The evaluation distance is marked as E1, the current task volume is marked as E2, and the current load of the initial personnel and vehicle is marked as E3; Assign a correction value Y1 to E1, a correction value Y2 to E2, and a correction value Y3 to E3; Y3<Y1<Y2, Y1+Y2+Y3=1; The initial allocation score Ee is obtained through the formula E1*Y1+E2*Y2+E3*Y3=Ee; Through the above process, the efficiency of task execution is improved: by extracting the location information of the recycling bin and the recycling personnel, the recycling personnel closest to the recycling bin can be quickly determined, which reduces the personnel movement time and improves the efficiency of task execution.

[0064] At the same time, by considering factors such as the current number of tasks of the recycling personnel, the location of the next task point and the vehicle load, tasks can be allocated more reasonably, avoiding task accumulation or idle personnel, and further improving efficiency.

[0065] Assigning tasks based on the vehicle load of the recovery personnel can ensure the smooth completion of the recovery task and avoid multiple round trips or task failures due to insufficient vehicle load.

[0066] This refined resource allocation helps stores make better use of existing human and material resources and reduce resource waste.

[0067] By comprehensively considering the current task volume and the location of the next task point of the recycling personnel, more reasonable work tasks can be assigned to employees, reducing their work stress and dissatisfaction.

[0068] When employees feel that their workload is distributed reasonably, they are more likely to maintain a positive work attitude and improve work efficiency and satisfaction; Efficient execution of recycling tasks and reasonable resource allocation can ensure that stores complete the collection of items from recycling bins in a timely and accurate manner, thereby improving service quality.

[0069] The relevant management information also includes recycling bin management information, which includes recycling bin addition information and recycling bin cancellation information; The specific process of obtaining the recycling bin management information is as follows: Extract the recycling bin information, including the setting time of the recycling bin, the number of registered users of the recycling bin, the number of valid users, the total recycling volume of the recycling bin, and the total profit information of the recycling bin; Calculate the ratio of the number of registered users of the recycling box to the length of time the recycling box is set up, and obtain the daily average number of registered users; The number of valid users refers to users who have recycled items at least twice after registration. The ratio of the number of valid users to the number of registered users of the recycling bin is calculated to obtain the percentage of valid users. Then calculate the ratio of the total recycling volume of the recycling bin to the setting time of the recycling bin to obtain the average daily recycling volume; When any three or more of the following conditions occur: the daily average registered user parameter is greater than or equal to the preset value, the effective user ratio is greater than or equal to the preset value, the daily average recycling volume is greater than or equal to the preset value, and the total profit information of the recycling bin is greater than or equal to the preset value, the recycling bin addition information is generated; When any three or more of the following conditions occur: the daily average registered user parameter is less than the preset value, the effective user ratio is less than the preset value, the daily average recycling volume is less than the preset value, and the total profit information of the recycling bin is less than the preset value, the recycling bin cancellation information is generated; By comprehensively considering multiple dimensions such as the length of time the recycling bin has been set up, the number of registered users, the number of effective users, the total recycling volume and the total profit, we can more comprehensively evaluate the operating status of the recycling bin.

[0070] Making decisions based on these data can ensure that the addition or removal of recycling bins is more scientific and reasonable, and avoid blind expansion or excessive reduction.

[0071] By comprehensively considering multiple dimensions such as the length of time the recycling bin has been set up, the number of registered users, the number of effective users, the total recycling volume and the total profit, we can more comprehensively evaluate the operating status of the recycling bin.

[0072] Making decisions based on these data can ensure that the addition or removal of recycling bins is more scientific and reasonable, and avoid blind expansion or excessive reduction.

[0073] The generation of additional recycling bin information helps stores to promptly add recycling bins in areas with high demand to meet users’ recycling needs.

[0074] The generation of recycling bin cancellation information can help stores promptly identify and cancel poorly operated recycling bins to avoid waste of resources.

[0075] By automating the information acquisition and decision-making processes, manual intervention and decision-making time can be greatly reduced, improving operational efficiency.

[0076] At the same time, this data-based decision-making approach helps stores adapt to market changes more quickly and maintain competitive advantage.

[0077] Through a comprehensive assessment of the operating conditions of recycling bins, stores can promptly identify potential problems and risks and take appropriate measures to prevent and respond.

[0078] This helps reduce store operational risks and ensures the continued stable operation of the recycling bin business.

[0079] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0080] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0081] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An intelligent store management system based on big data analysis, characterized in that: include: The store information collection module is used to collect store information and obtain store information; The item information collection module is used to collect relevant item information in the store and obtain item information; The store personnel collection module is used to collect the personnel information in the store and obtain the store personnel information; A recycling bin information collection module is used to collect recycling bin information and obtain recycling bin information; Comprehensive analysis module, used to process the collected store information, item information, store personnel information and recycling bin information to generate relevant management information; The information export module is used to send out the generated relevant management information.

2. According to claim 1, an intelligent store management system based on big data analysis is characterized by: The relevant management information includes store management information, and the store management information includes determining and changing the selected site; The process of obtaining store management information is as follows: Extract the acquired store information, which includes road information around the store, store warehouse information and store location information; Process the road information around the store to obtain the traffic convenience index; Process the store warehouse information and obtain the warehouse index; Process the store location information to obtain the store location index; Then, the traffic convenience index, warehouse index and store location index are comprehensively calculated and processed to obtain a comprehensive evaluation index; When the comprehensive evaluation index is greater than or equal to the preset value, the store management information is generated. At this time, the store management information is used to determine the site selection; When the comprehensive evaluation index is less than the preset value, store management information is generated. At this time, the store management information is to change the selection point.

3. The intelligent store management system based on big data analysis according to claim 2 is characterized by: The process of obtaining the traffic convenience index is as follows: extracting the acquired road information around the store, the road information around the store including the road width information and road congestion conditions of the three roads closest to the store; The road traffic jam condition is the number of traffic jams within a preset time period; Process the road width information and traffic jam conditions of a single road to obtain road assessment parameters of the single road; Calculate the road evaluation parameters of the three roads according to the preset first calculation rule to obtain the traffic convenience index; The process of obtaining the warehouse index is as follows: extract the acquired store warehouse information, which includes the number of warehouse fire protection facilities, warehouse capacity utilization rate and the number of abnormal stores around the warehouse; Warehouse capacity utilization is the ratio of the actual warehouse area to the marked total area; The warehouse index is obtained by calculating the number of warehouse fire protection facilities, the warehouse capacity utilization rate and the number of abnormal stores around the warehouse according to the second calculation rule; The process of obtaining the store location index is as follows: extracting the obtained store location information, importing the store location information into an Internet database, retrieving the number of residential buildings and the number of other similar service businesses within a preset range of the store location from the Internet database, and calculating the number of residential buildings and the number of other similar service businesses within the preset range of the store location according to a third calculation rule to obtain the store location index; The specific process of comprehensively calculating and processing the traffic convenience index, warehouse index and store location index to obtain the comprehensive evaluation index is as follows: The transportation convenience index is marked as F1, the warehouse index is marked as F2, and the store location index is marked as F3; Assign a correction value G1 to F1, a correction value G2 to F2, and a correction value G3 to F3, G1+G2+G3=1, G1>G2>G3; Through the formula F1*G1+F2*G2+F3*G3=Ff, the comprehensive evaluation index Ff is obtained.

4. The intelligent store management system based on big data analysis according to claim 1 is characterized by: The relevant management information includes item management information, and the process of obtaining the item management information is as follows: Extract the acquired item information, the item information includes the purchase frequency of the store materials and the consumption frequency of the store materials; Then extract the store personnel information, which includes the number of store business personnel; Import the store staff quantity information, the store material purchase frequency and the store material consumption frequency into the preset model library, establish the model, and construct a real-time staff and material consumption model; Then, the personnel and material consumption models of other stores are extracted from the preset big database; The real-time personnel and material consumption model is compared with the personnel and material consumption models of other stores to obtain comparison parameters. When the comparison parameters are less than the preset values, the item management information is generated.

5. The intelligent store operation and management system based on big data analysis according to claim 1 is characterized by: The relevant management information includes personnel management information, which includes personnel scheduling information and personnel abnormality prompt information; The specific process of obtaining the personnel management information is as follows: extracting the obtained personnel information, the personnel information including personnel sign-in information; Then extract the obtained recycling bin information, the recycling bin information includes the current recycling status information of the recycling bin; The current recycling status information of the recycling bin includes whether the recycling volume meets the standard or not; When the current recycling status information of the recycling bin is that the recycling reaches the standard, the personnel dispatch information is generated; The personnel sign-in information is extracted and compared with the table of personnel who should sign in on the day to obtain the comparison result. If the comparison result shows that there are personnel who have not signed in, a personnel abnormality prompt message is generated.

6. The intelligent store management system based on big data analysis according to claim 5 is characterized by: The personnel dispatch information is used to dispatch store personnel to perform the task of collecting recycled items from the recycling bin; The specific process of store personnel scheduling based on personnel scheduling information is as follows: Extracting personnel information and recycling bin information, the personnel information also includes recycling personnel location information, and the recycling bin information also includes recycling bin location information; First, extract the location information of the recycling bin where the recycled items need to be collected, mark it as the initial location Z, then extract the location information of all recycling personnel, extract the three closest personnel from all the recycling personnel location information, and mark them as the initial allocation personnel; Then collect the relevant information of the initial assigned personnel, including the current number of tasks of the initial personnel, the location of the next task point of the initial personnel and the current load of the initial personnel's vehicle; The current number of tasks, the location of the next task point of the initial personnel, and the current load of the initial personnel's vehicle are processed to obtain the initial allocation score; Then the person with the minimum initial allocation score is selected as the final allocation person.

7. The intelligent store management system based on big data analysis according to claim 6 is characterized by: The process of obtaining the initial allocation score is as follows: extract the current task quantity, the next task point position of the initial personnel and the current load of the initial personnel vehicle, measure the distance between the next task point position of the initial personnel and the initial position Z, and obtain the evaluation distance; The evaluation distance is marked as E1, the current task volume is marked as E2, and the current load of the initial personnel and vehicle is marked as E3; Assign a correction value Y1 to E1, a correction value Y2 to E2, and a correction value Y3 to E3; Y3<Y1<Y2, Y1+Y2+Y3=1; Through the formula E1*Y1+E2*Y2+E3*Y3=Ee, the initial allocation score Ee is obtained.

8. The intelligent store management system based on big data analysis according to claim 6 is characterized by: The relevant management information also includes recycling bin management information, which includes recycling bin addition information and recycling bin cancellation information; The specific process of obtaining the recycling bin management information is as follows: Extract the recycling bin information, including the setting time of the recycling bin, the number of registered users of the recycling bin, the number of valid users, the total recycling volume of the recycling bin, and the total profit information of the recycling bin; Calculate the ratio of the number of registered users of the recycling box to the length of time the recycling box is set up, and obtain the daily average number of registered users; The number of valid users refers to users who have recycled items at least twice after registration. The ratio of the number of valid users to the number of registered users of the recycling bin is calculated to obtain the percentage of valid users. Then calculate the ratio of the total recycling volume of the recycling bin to the setting time of the recycling bin to obtain the average daily recycling volume; When any three or more of the following conditions occur: the daily average registered user parameter is greater than or equal to the preset value, the effective user ratio is greater than or equal to the preset value, the daily average recycling volume is greater than or equal to the preset value, and the total profit information of the recycling bin is greater than or equal to the preset value, the recycling bin addition information is generated; When any three or more of the following conditions occur: the daily average registered user parameter is less than the preset value, the effective user ratio is less than the preset value, the daily average recycling volume is less than the preset value, and the total profit information of the recycling bin is less than the preset value, the recycling bin cancellation information is generated.

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