CN112101980B - Method and system for analyzing purchasing preference of user - Google Patents
Method and system for analyzing purchasing preference of user Download PDFInfo
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Abstract
The invention discloses a method and a system for analyzing purchasing preference of a user, wherein the method comprises the following steps: analyzing the purchasing power of the user based on the historical purchasing expense; acquiring a period and a used period of a first product purchased by a user in a history manner, and analyzing the user in the history manner based on the period and the used period; acquiring the number of people purchasing the associated products in the user interaction circle, and acquiring the interaction circle associated purchasing preference based on the number of people purchasing the associated products; acquiring the number of people purchasing a first product in a user interaction circle, and analyzing interaction circle purchasing preference based on the number of people purchasing the first product in the interaction circle; acquiring a first product association use condition of a user, and analyzing product association preference of the user; and analyzing the purchasing preference of the user based on the analysis result. The purchasing preference of the user is comprehensively analyzed from multiple dimensions, and the purchasing preference of the user for the specific product is analyzed through deep mining of the historical data, so that the purchasing requirement of the user for the specific product is accurately reflected.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for analyzing purchasing preference of a user.
Background
Purchase preference analysis is to mine historical behavior data and predict future event purchase behavior of a user according to reliable calculation. With the continuous development of information technology, the information quantity generated by users is continuously increased, and the information quantity of products is also continuously increased; how to mine the information, and to analyze the preference of the user purchase becomes a key problem of product recommendation. In the existing purchase scene, products interested by the user are analyzed through historical browsing information of the user, and the analyzed products are recommended to the user.
The data mining depth of the analysis method is low, and only interested products are recommended to the user, but purchasing preference of the user on specific products cannot be analyzed.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a method and a system for analyzing purchasing preference of a user, which are used for deep mining of purchasing preference of the user for specific products based on historical data.
The invention discloses a method for analyzing purchasing preference of a user, which comprises the following steps: acquiring historical purchase expenditure of the user, and analyzing the purchase power of the user based on the historical purchase expenditure; acquiring a period and a used period of a first product purchased by a user in a history manner, and analyzing the user in the history manner based on the period and the used period; acquiring the number of people purchasing the associated products in the user interaction circle, and acquiring the interaction circle associated purchasing preference based on the number of people purchasing the associated products; acquiring the number of people purchasing the first product in the user interaction circle, and analyzing the interaction circle purchasing preference based on the number of people purchasing the first product in the interaction circle; acquiring a first product association use condition of a user, and analyzing product association preference of the user; the user purchase preferences are analyzed based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
Preferably, the present invention further includes a method of building a user purchase preference model: setting scoring models and weights of the purchasing power, the historical purchasing preference, the relationship purchasing preference of the relationship ring, the purchasing preference of the relationship ring and the relationship preference of the product respectively; and calculating the total score of the purchasing preference according to the score model and the weight.
Preferably, the method for constructing the scoring model of purchasing power comprises the following steps: setting a scoring interval of purchasing power, and evaluating the scoring of purchasing power according to the historical purchasing expenditure and the scoring interval.
Preferably, the method for constructing the historical purchase preference scoring model comprises the following steps: setting a scoring interval of the historical purchasing deadline, and evaluating the score of the historical purchasing preference according to the deadline, the used deadline and the scoring interval of the historical purchasing first product.
Preferably, the method for constructing the relationship circle associated purchase preference scoring model comprises the following steps: setting a scoring interval of the number of people buying the associated products in the interaction circle, and evaluating the score of the associated purchasing preference of the interaction circle according to the number of people buying the associated products in the interaction circle and the scoring interval.
Preferably, the method for constructing the interaction circle purchase preference scoring model comprises the following steps: setting a scoring interval for purchasing the first product in the interaction circle, and evaluating the score of the purchase preference of the interaction circle according to the number of the first product purchased in the interaction circle and the scoring interval.
Preferably, the method for constructing the associated preference scoring model comprises the following steps: setting a scoring interval of the user on the product association use condition, and evaluating the scoring of the product association preference according to the product association use condition and the scoring interval of the user.
Preferably, the purchase preference model is used for analysis of purchase preferences of a mobile phone contract service: analyzing the purchasing power of the user based on the account expenditure of the user; analyzing historical purchase preferences based on remaining terms of the historically purchased mobile phone contract service; analyzing the related purchasing preference of the interaction circle based on the number of people purchasing mobile phones in the interaction circle; analyzing the contract purchasing preference of the interaction circle based on the number of people purchasing the mobile phone contract service in the interaction circle; based on the use condition of the mobile phone internet traffic, analyzing the product association preference of the user; the user purchase preferences are analyzed based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
Preferably, the present invention further includes a method of generating a recommendation list based on purchase preference analysis: acquiring a product of interest of a user; generating a first recommendation list according to products of interest of a user, wherein the first recommendation list comprises at least two recommended products; according to the purchasing preference analysis method, purchasing preference analysis is carried out on the recommended products; and sorting the first recommendation list according to the purchase preference to generate a second recommendation list.
The invention also provides a system adopting the purchasing preference analysis method, which comprises a purchasing power analysis module, a historical purchasing preference analysis module, an interaction circle associated purchasing preference analysis module, an interaction circle purchasing preference analysis module, a product associated preference analysis module and a purchasing preference analysis module, wherein the purchasing power analysis module is used for acquiring historical purchasing expenditure of a user and analyzing the purchasing power of the user based on the historical purchasing expenditure; the historical purchase preference analysis module is used for acquiring the deadline and the used deadline of the user historical purchased products and analyzing the user historical purchase preference based on the deadline and the used deadline of the user purchased products; the interaction circle associated purchasing preference analysis module is used for acquiring the number of people purchasing associated products in the interaction circle of the user and analyzing the interaction circle associated purchasing preference based on the number of people purchasing the associated products; the interaction circle purchasing preference analysis module is used for acquiring the number of people purchasing the product in the interaction circle of the user and judging the interaction circle purchasing preference based on the number of people purchasing the product in the interaction circle; the product association preference analysis module is used for acquiring the use condition of product association of a user and analyzing the product association preference of the user; the user purchase preference analysis module analyzes the user purchase preferences based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
Compared with the prior art, the invention has the beneficial effects that:
and comprehensively analyzing the purchasing preferences of the user from the purchasing power, the historical purchasing preferences, the relationship purchasing preferences of the relationship circles, the purchasing preferences of the relationship circles and the dimensions of the relationship purchasing preferences of the products, and analyzing the purchasing preferences of the user for the specific products through deep mining of the historical data so as to accurately reflect the purchasing requirements of the user for the specific products.
Drawings
FIG. 1 is a flow chart of a method of user purchase preference analysis of the present invention;
FIG. 2 is a flow chart of a method of analyzing purchasing preferences of a mobile phone contract service;
FIG. 3 is a flow chart of a method of generating a recommendation list;
FIG. 4 is a logical block diagram of a user purchase preference analysis system of the present invention;
FIG. 5 is a flow chart of a method of building a user purchase preference model.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is described in further detail below with reference to the attached drawing figures:
a method of user purchase preference analysis, as shown in fig. 1, the method comprising:
step 101: the historical purchase expenditure of the user is obtained, and the purchasing power of the user is analyzed based on the historical purchase expenditure.
The method comprises the steps that historical purchase expenditure can be obtained from the account information of a user, and it is worth proposing that sensitive information of the user is required to be obtained on the premise of authorization of the user; the user's purchase expenditure may also be obtained from the posting revenue of the financial system. The term of the historical purchase expenditure may be set within 1 year or 2 years, but is not limited thereto.
Step 102: the deadline and the elapsed time of the user's historical purchase of the first product are obtained, and the user's historical purchase preferences are analyzed based on the deadline and the elapsed time.
The first product may include a physical product or a service product, and the product has a certain period, for example, the period of the contract service may be 24 periods, and the remaining period of the contract service is calculated through the period and the used period, so that the remaining period of the contract service is used for analyzing historical purchasing preference of the user, that is, the preference of purchasing the product again by the user when the first product expires or is about to expire is higher.
Step 103: the method comprises the steps of obtaining the number of people purchasing associated products in a user interaction circle, and obtaining the interaction circle associated purchasing preference based on the number of people purchasing the associated products.
The interaction circle refers to a crowd passing through a direct ditch of a user within a certain period, and when the number of people purchasing related products in the interaction circle is large, the preference of the user for purchasing the first product is high. Direct communication refers to communication in a manner that can be detected by telephone contact, text message contact, or online contact.
Step 104: and acquiring the number of people purchasing the first product in the user interaction circle, and analyzing the interaction circle purchasing preference based on the number of people purchasing the first product in the interaction circle. I.e. the number of people in the circle of interaction who purchase the first product is higher, the preference of the user to purchase the first product is higher.
Step 105: and acquiring the use condition of the first product association by the user, and acquiring the product association preference of the user. The service condition has relevance with the first product, such as mobile phone contract service has relevance with a mobile phone, and the purchasing preference of the mobile phone contract service is influenced under the condition of mobile phone service; and also the purchasing preferences of the paper and ink cartridge, as affected by the use of the printer.
Step 106: the user purchase preferences are analyzed based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
The purchasing preferences of the user are comprehensively analyzed from the purchasing power, the historical purchasing preferences, the relationship purchasing preferences of the relationship circles, the purchasing preferences of the relationship circles and the dimensions of the relationship purchasing preferences of the products, and the purchasing preferences of the user for the specific products are analyzed through deep mining of the historical data, so that the purchasing requirements of the user for the specific products can be accurately reflected.
As shown in fig. 5, the present invention further includes a method of building a user purchase preference model:
step 501: and respectively setting scoring models and weights of the purchasing power, the historical purchasing preference, the relationship purchasing preference of the relationship ring, the purchasing preference of the relationship ring and the relationship preference of the product. The scoring model is used for quantitatively measuring the scoring values of the purchasing power, the historical purchasing preference, the relationship ring purchasing preference and the product relationship preference, and the weight is used for measuring the proportion of each score in the total scoring.
Step 502: and calculating the total score of the purchasing preference according to the score model and the weight. The purchase preference total score is used to quantitatively measure the purchase preference.
Example 1
The method for constructing the purchasing power scoring model comprises the following steps: setting a scoring interval of purchasing power; and evaluating the score of the purchasing power according to the historical purchasing expenditure and the scoring interval (P1).
Taking the mobile phone communication cost as an example: when the monthly communication fee is less than 60 yuan, the score is 0; scoring 0.2 at 60-80; scoring 0.4 at 80-120; scoring 0.6 at 120-180; a score of 0.8 between 180 and 300; above 300, score 1. However, the scoring interval may be set according to the type price of the product.
Example 2
The method for constructing the historical purchase preference scoring model comprises the following steps: setting a scoring interval of historical purchasing deadlines, and evaluating scores of historical purchasing preferences according to the deadlines of the first product purchased historically, the used deadlines and the scoring interval of the historical purchasing deadlines (P2).
Taking a mobile phone contract service as an example: the cell phone contract service was not used, and the score was 0; the number of remaining months (M) due to mobile phone contract service is greater than 24 months, and the score is 0; the number of remaining months due to mobile phone contract service is less than 24 months, and the score is: m/24; the mobile phone contract service life is less than 6 months, and the score is 0 or negative. Namely, the user who purchases the mobile phone contract service has a preference for purchasing the mobile phone contract service again which is larger than that of the user who does not purchase the mobile phone contract service; the user with less contract remaining months purchases the mobile phone contract service again, and the preference of the user with more contract remaining months is larger than that of the user with more contract remaining months. Wherein the preference for repurchase is low when the lifetime of the first product is low; the total score may be adjusted by a negative value when the first product lifetime is low without providing the first product to the user.
The historical purchase preference score (P2) can also be analyzed through the associated products, such as a mobile phone leasing service, a user purchasing the mobile phone leasing service, the more the number of months (N) of the mobile phone leasing service is, the greater the preference of purchasing the mobile phone contract service is, and the score is: n/24.
Example 3
The method for constructing the relationship circle associated purchase preference scoring model comprises the following steps: setting a scoring interval of the number of people buying the associated products in the interaction circle, and evaluating the score of the associated purchasing preference of the interaction circle according to the number of people buying the associated products in the interaction circle and the scoring interval (P3).
Taking the example of buying mobile phone contract service, the user interaction circles are arranged in descending order of contact times, the first 10 people are taken, and the number of people who exchange mobile phones among them is obtained. When the number of people changing the mobile phone is 0, scoring 0; the number of people changing mobile phones is 1, and the score is 0.4; the number of people changing mobile phones is 2, and the score is 0.6; the number of people changing the mobile phone exceeds 3 and the score is 1. That is, the greater the number of people exchanging handsets in a circle, the greater the preference to purchase handset contract services. The mobile phone contract service is used as a first product, and the mobile phone is used as an associated product.
Example 4
The method for constructing the interaction circle purchase preference scoring model comprises the following steps: setting a scoring interval for purchasing the first product in the interaction circle, and evaluating the score of the interaction circle purchasing preference according to the number of the first product purchased in the interaction circle and the scoring interval (P4).
Taking the example of buying mobile phone contract service, the number of people buying mobile phone contract service in 10 people before the circle of contact is obtained. When the number of the purchased persons is 0, scoring 0; when the number of the purchased persons is 1, the score is 0.4; the number of buyers is 2, and the score is 0.6; when the number of purchases exceeds 3, score 1.
Example 5
The method for constructing the associated preference scoring model comprises the following steps: setting a scoring interval of the user on the product association use condition, and evaluating the scoring of the association preference according to the first product association use condition and the scoring interval of the user (P5). The use case may include frequency of use or amount of use. Wherein the dimensions of the first product association and its use should be set according to the category of the product and the most associated use.
Taking a mobile phone contract service as an example, taking the mobile phone traffic use condition as the most relevant use: when the use flow rate is 0, scoring 0; scoring 0.8 when using a flow rate of 0-30M; scoring when using a flow rate of 30-800M: flow rate/800; when the flow rate exceeds 800M, the score is 0.
The user purchase preference model comprises the score of the scoring model and the weight of each scoring model:
P＝P1×K1+P2×K2+P3×K3+P4×K4+P5×K5 (1)
where K1-K5 are weight coefficients and P is the total score for purchase preference.
The invention may also include a method of ranking purchasing preferences according to P: according to P and the scoring interval thereof, the purchasing preference is classified into the following table:
| total score of purchase preference (P) | Purchasing preference level |
| 0-30 | Non-potential users |
| 30-40 | Low grade of |
| 40-50 | Middle grade |
| 50-100 | High grade |
Example 6
As shown in fig. 2, the purchase preference model of the present invention is applied to analysis of purchase preferences of a mobile phone contract service:
step 201: based on the user's checkout expenditure, the user's purchasing power is analyzed, and a purchasing power score P1 is obtained.
Step 202: based on the remaining deadlines of the historically purchased cell phone contract service, historical purchase preferences are analyzed to obtain a historical purchase preference score P2. The mobile phone contract service is used as a first product.
Step 203: based on the number of people buying mobile phones in the interaction circle, the interaction circle associated purchasing preference is analyzed, and the interaction circle associated purchasing preference score P3 is obtained. Wherein, the mobile phone is used as the associated product.
Step 204: based on the number of people purchasing mobile phone contract services in the contact ring, the contact ring purchasing preference is analyzed, and a contact ring purchasing preference score P4 is obtained.
Step 205: based on the use condition of the mobile phone internet traffic, analyzing the product association preference of the user to obtain a product association preference score P5. The mobile phone internet traffic is used as a first product related service condition.
Step 206: the user purchase preferences are analyzed based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences. Taking K1 as 20, K2 as 20, K3 as 25, K4 as 25, K5 as 10, calculating a score (P) of the user's preference for mobile phone contract service purchase according to formula 1:
P＝P1×20+P2×20+P3×25+P4×25+P5×10 (2)。
the account outgoing expenditure of the user, the remaining term of the mobile phone contract service purchased in the history, the number of people buying mobile phones or buying mobile phone contract service in the contact circles, and the use condition of the mobile phone internet traffic are pre-stored history data or can be obtained through calculation through the data.
The present invention also tested the purchase preference analysis method online for 1 month: the purchase preference model is established according to the analysis method of the purchase preference of the mobile phone contract service, the mobile phone contract service is recommended to users with different purchase preference grades, and the purchase rate of the mobile phone contract service is shown in the following table:
| purchasing preference level | Purchase rate |
| Non-potential users | 0.5％ |
| Low grade of | 3.7％ |
| Middle grade | 9.5％ |
| High grade | 18.1％ |
As can be seen from the table, the model provided by the invention can effectively reflect the purchasing preference of the user.
Example 7
As shown in FIG. 3, the present invention further includes a method of generating a recommendation list based on purchase preference analysis:
step 301: the product interested by the user is obtained, and the product can be obtained according to the product browsed or consulted by the user.
Step 302: and generating a first recommendation list according to the products of interest of the user, wherein the first recommendation list comprises at least two recommended products. The product associated with the product or the associated product can be generated according to the product of interest of the user, for example, when the user consults or browses the mobile phone, the mobile phone of the same kind, the mobile phone shell and the mobile phone package can be recommended to the user.
Step 303: and carrying out purchase preference analysis on the recommended products according to the purchase preference analysis method.
Step 304: and sorting the first recommendation list according to the purchase preference to generate a second recommendation list. The ordering may be in descending order of total score of purchase preferences.
The invention also provides a system for analyzing the purchasing preference of the user, as shown in fig. 4, the system comprises a purchasing power analysis module 1, a historical purchasing preference analysis module 2, an interaction circle associated purchasing preference analysis module 3, an interaction circle purchasing preference analysis module 4, a product associated preference analysis module 5 and a purchasing preference analysis module 6, wherein the purchasing power analysis module 1 is used for acquiring the historical purchasing expenditure of the user and analyzing the purchasing power of the user based on the historical purchasing expenditure; the historical purchase preference analysis module 2 is used for acquiring the deadline and the used deadline of the user historical purchased products and analyzing the user historical purchase preference based on the deadline and the used deadline of the user purchased products; the interaction circle associated purchasing preference analysis module 3 is used for acquiring the number of people purchasing associated products in the interaction circle of the user and analyzing the interaction circle associated purchasing preference based on the number of people purchasing the associated products; the interaction circle purchasing preference analysis module 4 is used for acquiring the number of people purchasing the product in the interaction circle of the user, and judging the interaction circle purchasing preference based on the number of people purchasing the product in the interaction circle; the product association preference analysis module 5 is used for acquiring the use condition of product association of the user and analyzing the product association preference of the user; the user purchase preference analysis module 6 analyzes the user purchase preferences based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A method of user purchase preference analysis, the method comprising:
acquiring historical purchase expenditure of the user, and analyzing the purchase power of the user based on the historical purchase expenditure;
acquiring a period and a used period of a first product purchased by a user in a history manner, and analyzing the user in the history manner based on the period and the used period;
acquiring the number of people purchasing the associated products in the user interaction circle, and acquiring the interaction circle associated purchasing preference based on the number of people purchasing the associated products;
acquiring the number of people purchasing the first product in the user interaction circle, and analyzing the interaction circle purchasing preference based on the number of people purchasing the first product in the interaction circle;
acquiring a first product association use condition of a user, and analyzing product association preference of the user;
analyzing user purchase preferences based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences;
the method for establishing the user purchasing preference model is also included:
setting scoring models and weights of the purchasing power, the historical purchasing preference, the relationship purchasing preference of the relationship ring, the purchasing preference of the relationship ring and the relationship preference of the product respectively;
calculating a total score of the purchase preference according to the score model and the weight;
the method for constructing the scoring model of the purchasing power comprises the following steps: setting a scoring interval of purchasing power, and evaluating the scoring of the purchasing power according to the historical purchasing expenditure and the scoring interval;
the method for constructing the historical purchase preference scoring model comprises the following steps: setting a scoring interval of historical purchasing deadlines, and evaluating scores of historical purchasing preferences according to the deadlines, the used deadlines and the scoring interval of the first product purchased historically;
the method for constructing the relationship circle associated purchase preference scoring model comprises the following steps: setting a scoring interval of the number of people buying the associated products in the interaction circle, and evaluating the score of the associated purchasing preference of the interaction circle according to the number of people buying the associated products in the interaction circle and the scoring interval;
the method for constructing the interaction circle purchase preference scoring model comprises the following steps: setting a scoring interval for purchasing the first product in the interaction circle, and evaluating the score of the purchasing preference of the interaction circle according to the number of the first product purchased in the interaction circle and the scoring interval;
the method for constructing the product association preference scoring model comprises the following steps: setting a scoring interval of the user on the product association use condition, and evaluating the scoring of the product association preference according to the product association use condition and the scoring interval of the user.
2. The method of purchasing preference analysis according to claim 1, wherein the purchasing preference model is used for analysis of purchasing preferences of a mobile phone contract service,
analyzing the purchasing power of the user based on the account expenditure of the user;
analyzing historical purchase preferences based on remaining terms of the historically purchased mobile phone contract service;
analyzing the related purchasing preference of the interaction circle based on the number of people purchasing mobile phones in the interaction circle;
analyzing the contract purchasing preference of the interaction circle based on the number of people purchasing the mobile phone contract service in the interaction circle;
based on the use condition of the mobile phone internet traffic, analyzing the product association preference of the user;
the user purchase preferences are analyzed based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
3. The method of purchasing preference analysis of claim 1, further comprising the steps of generating a recommendation list based on the purchasing preference analysis:
acquiring a product of interest of a user;
generating a first recommendation list according to products of interest of a user, wherein the first recommendation list comprises at least two recommended products;
according to the purchasing preference analysis method, purchasing preference analysis is carried out on the recommended products;
and sorting the first recommendation list according to the purchase preference to generate a second recommendation list.
4. A system employing the purchasing preference analysis method of any one of claim 1 to 3, wherein the system includes a purchasing power analysis module, a historical purchasing preference analysis module, an interaction circle associated purchasing preference analysis module, an interaction circle purchasing preference analysis module, a product associated preference analysis module, and a purchasing preference analysis module,
the purchasing power analysis module is used for acquiring historical purchasing expenditure of the user and analyzing purchasing power of the user based on the historical purchasing expenditure;
the historical purchase preference analysis module is used for acquiring the deadline and the used deadline of the user historical purchased products and analyzing the user historical purchase preference based on the deadline and the used deadline of the user purchased products;
the interaction circle associated purchasing preference analysis module is used for acquiring the number of people purchasing associated products in the interaction circle of the user and analyzing the interaction circle associated purchasing preference based on the number of people purchasing the associated products;
the interaction circle purchasing preference analysis module is used for acquiring the number of people purchasing the product in the interaction circle of the user and judging the interaction circle purchasing preference based on the number of people purchasing the product in the interaction circle;
the product association preference analysis module is used for acquiring the use condition of product association of a user and analyzing the product association preference of the user;
the purchase preference analysis module analyzes the user purchase preferences based on the purchasing power, historical purchase preferences, contact ring associated purchase preferences, contact ring purchase preferences, and product associated preferences.
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| CN114022228B (en) * | 2022-01-06 | 2022-04-19 | 深圳市思迅软件股份有限公司 | Economic information data processing method, system, computer equipment and storage medium |
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