Sports goods recovery management method based on artificial intelligence

Abstract

The invention belongs to the field of sporting goods management, relates to a data analysis technology, and aims to solve the problem that corresponding return requirements cannot be made according to the lending risk of sporting goods in the prior art, in particular to an artificial intelligence-based sporting goods recovery management method, which comprises the following steps of: performing lending data acquisition on the lended sporting goods; a management period is generated, a complementation priority sequence is obtained at the end moment of the management period and sent to a mobile phone terminal of a manager, and the manager recycles and purchases the sports goods according to the complementation priority sequence; according to the method, the shortage degree of the warehouse inventory of all sports goods of the same type in the management period can be evaluated, the sports goods are recycled and purchased on the basis of the complement priority sequence, the pertinence of the sports goods complement plan is improved, and it is guaranteed that all types of sports goods can be lended.

Description

Technical Field

[0001] The present invention belongs to the field of sports equipment management and relates to data analysis technology, and specifically is a sports equipment recycling management method based on artificial intelligence. Background Art

[0002] The recycling management method of sporting goods is mainly used to ensure the effective use of sporting goods and the rational allocation of resources; classify sporting goods and establish detailed files, including information such as the variety, quantity, usage status, construction records and usage specifications of sporting goods, which helps to track and manage the use of sporting goods.

[0003] The invention patent with announcement number CN108764499B discloses a sporting goods recycling management system based on the Internet of Things. The sporting goods recycling management system is equipped with fingerprint recognition devices in the lending cabinet and the robot body, and QR codes are set on the surface of the sporting goods. The sporting goods can be bound to the user's identity information, thereby more comprehensively improving the safety of the sporting goods; however, the system cannot formulate corresponding return requirements according to the lending risks of the sporting goods. The use of unified return requirements for all sporting goods is not conducive to the management of sporting goods lending, and the risk of sporting goods lending cannot be controlled.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a sports equipment recycling management method based on artificial intelligence, which is used to solve the problem that the existing technology cannot formulate corresponding return requirements according to the lending risk of sports equipment;

[0006] The technical problem to be solved by the present invention is: how to provide a sporting goods recycling management system based on the Internet of Things that can formulate corresponding return requirements according to the lending risks of sporting goods.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A sports goods recycling management method based on artificial intelligence, comprising the following steps:

[0009] Step 1: Collect lending data of the loaned sports equipment;

[0010] Step 2: Conduct loan management analysis on loaned sports equipment: Generate a management cycle, obtain a replenishment priority sequence at the end of the management cycle and send the replenishment priority sequence to the manager's mobile phone terminal, and the manager recycles and purchases the sports equipment according to the replenishment priority sequence;

[0011] Step 3: Conduct risk analysis on the lending of sports goods: mark the sports goods that have been lent and returned within the management cycle as analysis objects, obtain the defect data QX and maintenance data WH corresponding to the type of the analysis object, and perform numerical calculation to obtain the risk coefficient FX of the type;

[0012] Step 4: Use the risk factor FX to assess the lending risk of sports goods and mark the risk status of the type as normal lending or risk-controlled lending;

[0013] Step 5: Perform return evaluation analysis when the sports equipment is returned: Obtain the weight difference data ZC and heterodyne data WC of the sports equipment when the sports equipment is returned; The evaluation coefficient PG for returning sports goods is obtained, where k1 and k2 are both proportional coefficients, and k1>k2>1; t1 is the correction factor; and whether the sports goods meet the return requirements is determined by the evaluation coefficient PG.

[0014] Furthermore, in step one, the lending data of the lent sporting goods include type, weight data and appearance data, the weight data is the weight value weighed when the lent sporting goods are lent, and the appearance data is the appearance image taken when the lent sporting goods are lent.

[0015] Furthermore, in step two, the process of obtaining the replenishment priority sequence includes: classifying and counting the sports goods stored in the warehouse during the management period, marking the ratio of the number of sports goods of the same type stored in the warehouse to the total number of sports goods of this type held as the type's borrowing value, marking the maximum value of the type's borrowing value during the management period as the type's borrowing data JK, and arranging the types in descending order of the borrowing data JK to obtain the replenishment priority sequence.

[0016] Furthermore, in step three, the process of acquiring defect data QX includes: marking the analysis object that has an operating defect after return as a defect object, and marking the ratio of the number of defect objects corresponding to the same type to the number of analysis objects as the defect data QX of the type; the process of acquiring maintenance data WH includes: summing and averaging the repair costs of all defect objects corresponding to the type to obtain the repair value, summing and averaging the recycling prices of all defect objects corresponding to the type to obtain the recycling value, and marking the ratio of the repair value to the recycling value as maintenance data WH.

[0017] Furthermore, in step four, the specific process of evaluating the risk of lending sporting goods includes: comparing the risk coefficient FX of the type with the preset risk threshold FXmax: if the risk coefficient FX is less than the risk threshold FXmax, it is determined that the overall lending risk of the type meets the requirements, and the risk status of the corresponding type is marked as normal lending; if the risk coefficient FX is greater than or equal to the risk threshold FXmax, it is determined that the overall lending risk of the type does not meet the requirements, and the risk status of the corresponding type is marked as risk-controlled lending.

[0018] Furthermore, in step five, the process of acquiring the weight difference data ZC includes: retrieving the weight data of the returned sports equipment when it was loaned out, marking the absolute value of the difference between the weight value of the returned sports equipment weighed when it is returned and the weight data when it was loaned out as the weight difference value, and marking the ratio of the weight difference value to the weight data as the weight difference data ZC; the process of acquiring the heterodyne data WC includes: photographing the returned sports equipment to obtain a returned image, amplifying the appearance image and the returned image into pixel grid images and performing grayscale transformation, and overlapping and comparing the appearance image with the returned image: marking the absolute value of the difference between the grayscale value of the same pixel grid in the appearance image and the grayscale value in the returned image as the gray difference value of the pixel grid, marking the pixel grid whose gray difference value is not less than the gray difference threshold as the difference grid, and marking the ratio of the number of difference grids to the total number of pixel grids in the returned image as the heterodyne data WC.

[0019] Furthermore, in step five, the process of determining the value of t1 includes: retrieving the risk status of the corresponding type of returned sports equipment, if the risk status is normal lending, then t1=1; if the risk status is risk-controlled lending, then 1.06≤t1≤1.08.

[0020] Furthermore, in step five, a determination is made as to whether the sports goods meet the return requirements by comparing the evaluation coefficient PG for returning the sports goods with a preset evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the sports goods meet the return requirements; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the sports goods do not meet the return requirements, and an immediate accountability signal is generated and sent to the mobile phone terminal of the manager.

[0021] The present invention has the following beneficial effects:

[0022] 1. Collect and record the equipment parameters of sports equipment when they are loaned out through loan data, and compare the loan data as standard data when returning them, so as to quickly analyze the degree of loss of sports equipment during the loan period, and then efficiently evaluate whether the sports equipment meets the return requirements;

[0023] 2. Through the loan management analysis, the shortage of the warehouse inventory of all the same types of sports goods within the management cycle can be evaluated, and the sports goods can be recycled and purchased based on the replenishment priority sequence, so as to improve the pertinence of the sports goods replenishment plan and ensure that all types of sports goods can meet the loan demand;

[0024] 3. The lending risk of different types of sporting goods can be evaluated through lending risk analysis and lending risk assessment. The risk coefficient can be obtained by comprehensive analysis and calculation based on multiple risk parameters of sporting goods in the management cycle. The risk status of the type can be marked by the risk coefficient to match the return requirements when the sporting goods are returned.

[0025] 4. Return assessment analysis can analyze the return status of sports equipment, correct the assessment coefficient of sports equipment through the marking results of risk status, increase the assessment coefficient of sports equipment loaned out for risk control, and increase the strictness of product inspection of corresponding sports equipment when returned, thereby reducing the overall risk of lending sports equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 This is a flow chart of a method according to Embodiment 1 of the present invention;

[0028] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1: Figure 1 As shown, a sports equipment recycling management method based on artificial intelligence includes the following steps:

[0031] Step 1: Collect lending data of loaned sports equipment: Classify sports equipment. The lending data of loaned sports equipment includes type, weight data and appearance data. The weight data is the weight value weighed when the loaned sports equipment is loaned, and the appearance data is the appearance image taken when the loaned sports equipment is loaned. Collect and record the equipment parameters when the sports equipment is loaned. When returning the sports equipment, compare the lending data as standard data to quickly analyze the degree of loss of the sports equipment during the loan period, and then efficiently evaluate whether the sports equipment meets the return requirements.

[0032] Step 2: Conduct loan management analysis on loaned sports goods: Generate a management cycle, classify and count the sports goods stored in the warehouse within the management cycle, mark the ratio of the number of sports goods of the same type stored in the warehouse to the total number of sports goods of the same type held as the type's borrowing value, mark the maximum value of the type's borrowing value within the management cycle as the type's borrowing data JK, arrange the types in descending order according to the borrowing data JK to obtain a replenishment priority sequence, send the replenishment priority sequence to the manager's mobile phone terminal at the end of the management cycle, and the manager recycles and purchases the sports goods according to the replenishment priority sequence; evaluate the shortage of all sports goods of the same type in the warehouse within the management cycle, recycle and purchase the sports goods based on the replenishment priority sequence, improve the pertinence of the sports goods replenishment plan, and ensure that all types of sports goods can meet the loan demand;

[0033] Step 3: Conduct risk analysis on the lending of sports equipment: mark the sports equipment that has been lent and returned within the management cycle as analysis objects, obtain defect data QX and maintenance data WH corresponding to the type of the analysis object, and the process of obtaining defect data QX includes: marking the analysis object with operation defects after return as defective object, and marking the ratio of the number of defective objects of the same type to the number of analysis objects as the defect data QX of the type; the process of obtaining maintenance data WH includes: summing up the maintenance costs of all defective objects corresponding to the type and taking the average to obtain the maintenance value, summing up the recovery prices of all defective objects corresponding to the type and taking the average to obtain the recovery value, and marking the ratio of the maintenance value to the recovery value as maintenance data WH; through the formula Get the risk factor FX of the type, where m1, m2 and m3 are all proportional coefficients, and m1>m2>m3>1;

[0034] Step 4: Evaluate the lending risk of sports goods: compare the risk coefficient FX of the type with the preset risk threshold FXmax: if the risk coefficient FX is less than the risk threshold FXmax, it is determined that the overall lending risk of the type meets the requirements, and the risk status of the corresponding type is marked as normal lending; if the risk coefficient FX is greater than or equal to the risk threshold FXmax, it is determined that the overall lending risk of the type does not meet the requirements, and the risk status of the corresponding type is marked as risk-controlled lending; evaluate the lending risks of different types of sports goods, combine multiple risk parameters of sports goods in the management cycle to conduct comprehensive analysis and calculation to obtain the risk coefficient, mark the risk status of the type by the risk coefficient, and match the return requirements when the sports goods are returned;

[0035] Step 5: Perform return evaluation and analysis when the sports equipment is returned: When the sports equipment is returned, obtain the weight difference data ZC and heterodyne data WC of the sports equipment. The process of obtaining the weight difference data ZC includes: retrieving the weight data of the returned sports equipment when it was loaned out, marking the absolute value of the difference between the weight value of the returned sports equipment weighed when it is returned and the weight data when it was loaned out as the weight difference value, and marking the ratio of the weight difference value to the weight data as the weight difference data ZC; the process of obtaining the heterodyne data WC includes: photographing the returned sports equipment to obtain a returned image, amplifying the appearance image and the returned image into pixel grid images and performing grayscale transformation, and overlapping and comparing the appearance image with the returned image: marking the absolute value of the difference between the grayscale value of the same pixel grid in the appearance image and the grayscale value in the returned image as the gray difference value of the pixel grid, marking the pixel grid whose gray difference value is not less than the gray difference threshold as the difference grid, and marking the ratio of the number of difference grids to the total number of pixel grids in the returned image as the heterodyne data WC.

[0036] By formula The evaluation coefficient PG for returning sports goods is obtained, wherein k1 and k2 are both proportional coefficients, and k1>k2>1; t1 is a correction factor, and the value determination process of t1 includes: retrieving the risk status of the corresponding type of returned sports goods, if the risk status is normal lending, t1=1; if the risk status is risk-controlled lending, 1.06≤t1≤1.08; comparing the evaluation coefficient PG for returning sports goods with a preset evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the sports goods meet the return requirements; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the sports goods do not meet the return requirements, and an immediate accountability signal is generated and sent to the mobile phone terminal of the manager; the evaluation coefficient of the sports goods is corrected through the marking result of the risk status, and the evaluation coefficient of the sports goods loaned under risk control is increased, thereby increasing the strictness of the product inspection of the corresponding sports goods when they are returned, thereby reducing the lending risk of sports goods as a whole.

[0037] Embodiment 2: Figure 2 As shown, a sports equipment recycling management system based on artificial intelligence includes a lending collection module, a lending management module, a risk analysis module, a risk assessment module and a return assessment module; the lending collection module, the lending management module, the risk analysis module, the risk assessment module and the return assessment module are communicatively connected in sequence.

[0038] The lending collection module is used to collect lending data for loaned sports equipment.

[0039] The lending management module is used to perform lending management analysis on the loaned sports equipment and obtain the replenishment priority sequence.

[0040] The risk analysis module is used to analyze the lending risk of sports equipment and obtain the risk coefficient of the type.

[0041] The risk assessment module is used to assess the lending risk of sporting goods and mark the risk status of the type.

[0042] The return evaluation module is used to perform return evaluation analysis when the sporting goods are returned and to determine whether the sporting goods meet the return requirements.

[0043] A sports equipment recycling management method based on artificial intelligence collects lending data of lent sports equipment during operation; generates a management cycle, obtains a replenishment priority sequence at the end of the management cycle and sends the replenishment priority sequence to a mobile phone terminal of a manager, and the manager recycles and purchases the sports equipment according to the replenishment priority sequence; marks the sports equipment that has been lent and returned within the management cycle as analysis objects, obtains defect data QX and maintenance data WH corresponding to the type of the analysis object, and performs numerical calculations to obtain a risk coefficient FX of the type; evaluates the lending risk of the sports equipment through the risk coefficient FX and marks the risk status of the type as normal lending or risk-controlled lending.

[0044] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0045] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of sample data and sets a corresponding risk coefficient for each group of sample data; substitutes the set risk coefficient and the collected sample data into the formula, and any three formulas constitute a three-variable linear equation system. The calculated coefficients are screened and averaged, and the values ​​of m1, m2, and m3 are obtained to be 4.48, 2.65, and 2.17, respectively;

[0046] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding risk coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the risk coefficient is proportional to the value of the defect data.

[0047] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", 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 representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0048] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A sports goods recycling management method based on artificial intelligence, characterized in that: The following steps are involved: Step 1: Collect lending data of the loaned sports equipment; Step 2: Conduct loan management analysis on loaned sports equipment: Generate a management cycle, obtain a replenishment priority sequence at the end of the management cycle and send the replenishment priority sequence to the manager's mobile phone terminal, and the manager recycles and purchases the sports equipment according to the replenishment priority sequence; Step 3: Conduct risk analysis on the lending of sports goods: mark the sports goods that have been lent and returned within the management cycle as analysis objects, obtain the defect data QX and maintenance data WH corresponding to the type of the analysis object, and perform numerical calculation to obtain the risk coefficient FX of the type; Step 4: Use the risk factor FX to assess the lending risk of sports goods and mark the risk status of the type as normal lending or risk-controlled lending; Step 5: Perform return evaluation analysis when the sports equipment is returned: Obtain the weight difference data ZC and heterodyne data WC of the sports equipment when the sports equipment is returned; The evaluation coefficient PG for returning sports goods is obtained, where k1 and k2 are both proportional coefficients, and k1>k2>1; t1 is the correction factor; The evaluation coefficient PG is used to determine whether the sporting goods meet the return requirements.

2. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 1, characterized in that: In step one, the lending data of the lent sporting goods include type, weight data and appearance data, the weight data is the weight value weighed when the lent sporting goods are lent, and the appearance data is the appearance image taken when the lent sporting goods are lent.

3. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 2, characterized in that: In step two, the process of obtaining the replenishment priority sequence includes: classifying and counting the sports goods stored in the warehouse during the management period, marking the ratio of the number of sports goods of the same type stored in the warehouse to the total number of sports goods of this type held as the type's borrowing value, marking the maximum value of the type's borrowing value during the management period as the type's borrowing data JK, and arranging the types in descending order according to the borrowing data JK to obtain the replenishment priority sequence.

4. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 3, characterized in that: In step three, the process of obtaining defect data QX includes: marking the analysis object that has an operating defect after return as a defect object, and marking the ratio of the number of defect objects corresponding to the same type to the number of analysis objects as the defect data QX of the type; the process of obtaining maintenance data WH includes: summing and averaging the repair costs of all defect objects corresponding to the type to obtain the repair value, summing and averaging the recycling prices of all defect objects corresponding to the type to obtain the recycling value, and marking the ratio of the repair value to the recycling value as maintenance data WH.

5. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 4, characterized in that: In step 4, the specific process of evaluating the risk of lending sporting goods includes: comparing the risk coefficient FX of the type with the preset risk threshold FXmax: if the risk coefficient FX is less than the risk threshold FXmax, it is determined that the overall lending risk of the type meets the requirements, and the risk status of the corresponding type is marked as normal lending; if the risk coefficient FX is greater than or equal to the risk threshold FXmax, it is determined that the overall lending risk of the type does not meet the requirements, and the risk status of the corresponding type is marked as risk-controlled lending.

6. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 5, characterized in that: In step five, the process of obtaining the weight difference data ZC includes: retrieving the weight data of the returned sports equipment when it was loaned out, marking the absolute value of the difference between the weight value of the returned sports equipment weighed when it is returned and the weight data when it was loaned out as the weight difference value, and marking the ratio of the weight difference value to the weight data as the weight difference data ZC; the process of obtaining the heterodyne data WC includes: photographing the returned sports equipment to obtain a returned image, amplifying the appearance image and the returned image into pixel grid images and performing grayscale transformation, and overlapping and comparing the appearance image with the returned image: marking the absolute value of the difference between the grayscale value of the same pixel grid in the appearance image and the grayscale value in the returned image as the gray difference value of the pixel grid, marking the pixel grid whose gray difference value is not less than the gray difference threshold as the difference grid, and marking the ratio of the number of difference grids to the total number of pixel grids in the returned image as the heterodyne data WC.

7. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 6, characterized in that: In step five, the process of determining the value of t1 includes: retrieving the risk status of the corresponding type of returned sports equipment. If the risk status is normal lending, t1=1; if the risk status is risk-controlled lending, 1.06≤t1≤1.

08.

8. The method for managing the recycling of sporting goods based on artificial intelligence according to claim 7, characterized in that: In step five, a determination is made as to whether the sports goods meet the return requirements by comparing the evaluation coefficient PG for returning the sports goods with the preset evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the sports goods meet the return requirements; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the sports goods do not meet the return requirements, and an immediate accountability signal is generated and sent to the manager's mobile phone terminal.

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