CN114708037A - Product price monitoring method, system, terminal equipment and storage medium - Google Patents
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Abstract
The invention discloses a product price monitoring method, a system, terminal equipment and a storage medium, wherein the product price monitoring method comprises the steps of regularly grabbing market data of products from each target platform in a current statistical period; grouping the market data according to products and cities to obtain a plurality of market data groups; calculating the average price of the products of each market data group in the current statistical period according to a preset rule; calculating the difference value between the average price of the product of each market data group in the current statistical period and the average price of the product of the corresponding market data group in the previous statistical period; and when the difference is larger than a preset threshold value, sending early warning information to a target user. The invention can realize the automatic monitoring of the product price, not only reduces the labor cost and improves the working efficiency, but also ensures the data accuracy and provides guarantee for the high-efficiency and accurate display of the industry data.
Description
Technical Field
The invention relates to the field of price monitoring, in particular to a method, a system, terminal equipment and a storage medium for product price monitoring.
Background
In the trading market, price fluctuation risk is one of the most concerned risks for the trading body. The price data of the material market in the chemical plastic industry fluctuates in real time, and the monitored real-time price data has higher value for the clients in the industry, so that the clients can adjust the operation strategy in real time according to abnormal fluctuation of the price, and the larger economic loss is avoided, and the accuracy and the timeliness of the data analysis result need to be guaranteed.
At present, market data in the chemical plastic industry are mainly acquired by manual arrangement, each network platform needs one or more persons to carry out manual arrangement, and therefore the processing efficiency is low, the time consumption is long, and the labor cost investment is large; moreover, manual processing is easy to make mistakes, and the accuracy and timeliness of data cannot be guaranteed.
Disclosure of Invention
Aiming at the technical problems, the invention aims to realize automatic monitoring of product price, reduce labor cost, improve working efficiency, ensure data accuracy and provide guarantee for efficient and accurate display of industrial data.
In order to achieve the above object, the present invention provides a product price monitoring method, including:
capturing market data of products from each target platform regularly in a current statistical period;
grouping the market data according to products and cities to obtain a plurality of market data groups;
calculating the average price of the products of each market data group in the current statistical period according to a preset rule;
calculating the difference value between the average price of the product of each market data group in the current statistical period and the average price of the product of the corresponding market data group in the previous statistical period;
and when the difference value is larger than a preset threshold value, sending early warning information to a target user.
In some embodiments, the calculating, according to a preset rule, the average price of the product in each market data group in the current statistical period specifically includes:
determining the highest order of the product price according to the historical product price of the market data set;
counting the target number with the maximum occurrence frequency of the highest position in each price in the market data group in the current counting period;
and calculating the average price value of each product with the highest position of the market data set as the target number in the current statistical period, and taking the average price value as the average price of the products of the market data set.
In some embodiments, the periodically capturing market data of the product from each target platform in the current statistical period specifically includes:
capturing market data of products from each target platform regularly in a current statistical period, and storing the market data into a cloud database;
regularly pulling the market data from the cloud database in the current statistical period, and storing the market data into a cache database;
the grouping the market data according to products and cities to obtain a plurality of market data sets specifically comprises:
when the data quantity stored in the cache database is larger than a preset value, extracting market data in the cache database;
grouping the extracted market data according to products and cities to obtain a plurality of market data groups;
after the average price of the products of each market data group in the current statistical period is calculated according to the preset rule, the method further comprises the following steps:
and storing the market data group and the average price of the products of the market data group into a market database, and performing visual display.
In some embodiments, the grouping the extracted market data by product and city to obtain a plurality of market data sets specifically includes:
grouping the extracted market data according to product types to obtain a data group corresponding to each product;
and grouping the products in each data group according to cities to obtain a plurality of market data groups.
According to another aspect of the present invention, there is further provided a product price monitoring system comprising:
the data grabbing module is used for grabbing market data of the product from each target platform regularly in the current statistical period;
the grouping module is used for grouping the market data according to products and cities to obtain a plurality of market data groups;
the calculation module is used for calculating the average price of the products of each market data group in the current statistical period according to a preset rule;
the comparison module is used for calculating the difference value between the average product price of each market data group in the current statistical period and the average product price of the corresponding market data group in the last statistical period;
and the early warning module is used for sending early warning information to a target user when the difference value is greater than a preset threshold value.
In some embodiments, the computing module comprises:
a determining unit for determining the highest order of the product price according to the product historical price of the market data set;
the statistical unit is used for counting the target number with the maximum occurrence frequency of the highest position in each price in the market data group in the current statistical period;
and the calculating unit is used for calculating the average price value of each product with the highest position of the market data set as the target number in the current statistical period, and taking the average price value as the average price of the products of the market data set.
In some embodiments, the data crawling module comprises:
the data capturing unit is used for capturing market data of the products from each target platform regularly in the current statistical period and storing the market data into the cloud database;
the data pulling unit is used for pulling the market data from the cloud database at regular time in the current statistical period and storing the market data into a cache database;
the grouping module includes:
the data extraction unit is used for extracting the market data in the cache database when the data quantity stored in the cache database is larger than a preset value;
the grouping unit is used for grouping the extracted market data according to products and cities to obtain a plurality of market data groups;
the system also comprises a storage display module;
the storage display module is used for storing the market data group and the average product price of the market data group into a market database and carrying out visual display.
In some embodiments, the grouping unit is further configured to group the extracted market data according to product types to obtain a data group corresponding to each product, and group the products in each data group according to a city to obtain a plurality of market data groups.
According to another aspect of the present invention, the present invention further provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the computer program stored in the memory, and implement the operations performed by the product price monitoring method according to any of the above embodiments.
According to another aspect of the present invention, the present invention further provides a storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the operations performed by the product price monitoring method according to any of the above embodiments.
Compared with the prior art, the invention has the following technical effects:
according to the invention, the market data released on the network platform are automatically captured, the market data are automatically grouped and the average price of the product is calculated, the average price of the product in the current statistical period is compared with the average price of the product in the previous statistical period to screen out abnormal data, and the abnormal data are early warned, so that the automatic monitoring of the price of the product can be realized, the labor cost is reduced, the working efficiency is improved, the data accuracy is ensured, and the guarantee is provided for the efficient and accurate display of the industry data.
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The above features, technical features, advantages and modes of realisation of the present invention will be further described in the following detailed description of preferred embodiments thereof, which is to be read in connection with the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a product price monitoring method of the present invention;
FIG. 2 is a flow chart of another embodiment of a product price monitoring method of the present invention;
fig. 3 is a block diagram schematically illustrating the structure of a product price monitoring system according to the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In addition, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Referring to fig. 1, an embodiment of a product price monitoring method according to the present invention includes:
s100, capturing market data of products from each target platform regularly in a current statistical period;
specifically, the target platforms refer to each network platform with a cooperative relationship, and the product market data on each target platform is captured by a data capture tool. The market data of the product is captured regularly in each statistical period, the statistical period can be set according to actual needs, for example, 24 hours (one day) can be set as one statistical period, 12 hours can be set as one statistical period, and other arbitrary time can be set as one statistical period. This embodiment is described by taking 24 hours as an example of a statistical period.
Market data for a product may be captured every 10 minutes, half an hour, or an hour during the current statistical period. The market data of the product includes information of product type, price, city, etc., wherein the product type includes information of product name, brand, manufacturer, etc., in other embodiments, the market data of the product may further include other product information, which is not limited herein.
S200, grouping the market data according to products and cities to obtain a plurality of market data groups;
specifically, the captured product market data are grouped according to products and cities, namely, data with the same product type as the cities are divided into one group, and a plurality of market data groups are obtained.
S300, calculating the average price of the products of each market data set in the current statistical period according to a preset rule;
specifically, the market data is captured from a plurality of websites, and prices issued by each website may be different, so that unreasonable data needs to be filtered out according to a preset rule, and then the average price of products in each market data set is calculated, and the average price of the products is the market price of the corresponding products in a certain city, so that the calculated data is more practical.
S400, calculating the difference value between the average product price of each market data group in the current statistical period and the average product price of the corresponding market data group in the previous statistical period;
specifically, after the average price of the product of each market data group in the current statistical period is obtained, the average price of the product of each market data group is compared with the average price of the product of the corresponding market data group in the previous statistical period, and the difference between the average prices of the product of each market data group and the average price of the product of the corresponding market data group in the previous statistical period is calculated. The corresponding market data set in the previous statistical period refers to a market data set which is the same as the market data set in the current statistical period in product type and city in the previous statistical period.
And when the difference between the average product price of each market data group in the current statistical period and the average product price of the corresponding market data group in the previous statistical period needs to be calculated, only the historical data needs to be called.
And S500, when the difference value is larger than a preset threshold value, sending early warning information to a target user.
Specifically, if the average product price of a certain market data group in the current statistical period and the average product price of the corresponding market data group in the previous statistical period are greater than a preset threshold, the market data group is given an early warning identifier, and a message is sent to the message early warning queue MQ. The preset threshold value can be set according to the price fluctuation range which needs to be known by the target user. The early warning information is sent by adopting the message early warning queue, so that the information congestion generated when the information quantity is large can be avoided.
The method comprises the steps that an early warning message service is written to read messages in a message early warning queue, if the messages exist in the message early warning queue, APIs (application programming interfaces) of a short message platform and a nail platform can be called to send short messages and nail notifications to give prompts to target users, the content of the notifications comprises information of products, cities, price differences, time and the like, and the target users can be operation customer service, clients and the like. If there is no message in the queue, no processing is done.
The market data that this embodiment was released on through the automatic network platform that snatchs to carry out automatic grouping and calculate the average price of product to market data, compare through the average price of product with current statistical period with the average price of product of last statistical period again, with select unusual data, and carry out the early warning to unusual data, can realize the automatic control of product price, not only reduced the human cost, improved work efficiency, and ensured the data accuracy, provide the guarantee for the high efficiency of trade data, accurate show.
In an embodiment, the step S300 of calculating the average price of the product of each market data set in the current statistical period according to the preset rule in the above embodiment specifically includes:
s310, determining the highest position of the product price according to the historical product price of the market data set;
s320, counting the target number with the maximum occurrence frequency of the highest position in each price in the market data group in the current counting period;
s330, calculating the average price value of each product with the highest position of the market data set as the target number in the current statistical period, and taking the average price value as the average price of the products of the market data set.
Specifically, the price of the product is determined to be a few digits according to the historical price of the product, for example, if the probability of 95% of the price of the product in the last two years is 4 digits, the highest position of the price of the product is a thousand digits; if the probability of 95% of the product price in the last two years is 3 digits, the highest position of the product price is hundreds of digits; if the probability of 95% of the product price in the last two years is 2 digits, the highest position of the product price is ten digits.
After the highest position of the product price is determined, counting the number with the highest occurrence frequency of the highest position in the price data of the market data group, for example, the highest position is thousand positions, counting the number with the highest occurrence frequency of the thousand positions in the price data of the market data group as a target number, then filtering prices with the thousand positions not being the target number, calculating the average value of all prices with the thousand positions being the target number in the market data group, and taking the average value as the average price of the product in the market data group.
Illustratively, the market data of a set of market data sets is:
PP/Z30S/Niyao from north sea refinery 9600
PP/Z30S/residual Yao 10100 of north sea refinery
PP/Z30S/Niyao 9300 from north sea refinery
If the number with the most occurrence times of the thousand figures in the price data is 9, filtering out the price data with the thousand figures not being 9, namely filtering out 10100, and then calculating the average value of the remaining price data as (9600+9300)/2 as 9450; the average price of the product for the market data set is 9450.
If the occurrence frequency of the highest position of the product price in the market data set is 1, directly taking the average value of all prices in the market data set as the average price of the product in the market data set. For example, when the highest bit is the thousand-digit number, but the highest bit of only one price in the market data set is the thousand-digit number, the average value of all prices in the market data set is directly taken as the average product price of the market data set. In the case of other special cases which do not satisfy the above rule, the average value of all prices in the market data set can be directly taken as the average price of the product in the market data set.
In an embodiment, the step S100 in the above embodiment specifically includes the step of periodically capturing market data of products from each target platform in the current statistical period:
s110, capturing market data of products from each target platform regularly in a current statistical period, and storing the market data into a cloud database;
s120, regularly pulling the market data from the cloud database in the current statistical period, and storing the market data into a cache database;
s200, grouping the market data according to products and cities to obtain a plurality of market data sets specifically comprises:
s210, when the data quantity stored in the cache database is larger than a preset value, extracting market data in the cache database;
s220, grouping the extracted market data according to products and cities to obtain a plurality of market data groups;
s300, calculating the average price of the products of each market data group in the current statistical period according to a preset rule, and then:
s600, storing the market data group and the average price of the products of the market data group into a market database, and performing visual display.
Specifically, a data grabbing tool is used, a timing grabbing mechanism is set, and market data of a plurality of platform websites are grabbed and stored in a cloud database.
And calling the API by the timer A to pull the data of each platform from the cloud database, and updating the pulling state of the cloud database to be pulled, wherein the purpose of updating the state is to pull the data in increments. In order to access the market data base infrequently and reduce the reading and writing pressure of the database, the acquired market data is stored in the cache database firstly.
And the timer B monitors the data in the cache database in real time, when the data volume of the market data is larger than a preset value, the market data in the cache database is taken out, and the data is grouped according to products and cities to obtain a plurality of market data groups.
And after calculating the average price of the products of each market data group in the current statistical period, storing the final market price data into a market data base (RDS database), accessing the back-end service of each platform through an interface by a front-end display port, and organizing market data by inquiring the RDS database by the back-end service to display the market data to customers.
In an embodiment, the grouping the extracted market data according to the product and the city to obtain a plurality of market data sets in S220 in the above embodiment specifically includes:
s221, grouping the extracted market data according to product types to obtain a data group corresponding to each product;
s222, grouping the products in each data group according to cities to obtain a plurality of market data groups.
Specifically, the market data in the buffer database are grouped into single products, and then the groups of the products are subdivided according to cities to obtain a plurality of market data groups. The products and cities within each market data set are the same.
Referring to fig. 2, another embodiment of a product price monitoring method according to the present invention includes:
(1) using a data grabbing tool, setting a timing grabbing mechanism, grabbing market data of a plurality of platform websites such as plausis, Longzhong and Zhonghuan, and storing the market data in a cloud database;
(2) writing a timer A, calling an API (application programming interface) method to pull data of each platform from the cloud database, and updating the pull state of the cloud database to be pulled, wherein the purpose of updating the state is to pull the data in increments. In order to not frequently access the database RDS and reduce the reading and writing pressure of the database, the acquired data is firstly stored in the REDIS cache database.
Compiling a timer B to monitor data in a REDIS cache database in real time, taking out the data when the data volume is more than 5000, grouping the data by single products, then grouping the products in cities corresponding to the products, filtering out unsatisfied prices (filtering rule reference examples) in price fields of the products and the cities according to a rule with high occurrence probability of the highest digit, and finally taking the average value of all product prices meeting the rule in a market data group as the final price of the city corresponding to the product, wherein the product is a product name/brand number/manufacturer;
comparing the average price of a certain product + city in the current statistical period with the average price of the same product in the same city in the previous statistical period, if the difference is more than 1000, giving an early warning identifier to the record, simultaneously sending a message to a message early warning queue MQ, if no data more than 1000 exists, not processing, and finally storing the early warning and non-early warning final market price data into a market situation RDS database.
Writing an early warning message consumption service to read a message in a message early warning queue, if the message exists in the queue, calling APIs (application programming interfaces) of a short message platform and a nail platform to send a short message and a nail notification to give a service prompt to an operator, wherein the content of the notification comprises a product, a city, a price difference and time, and if no message exists in the queue, the notification is not processed.
And the operator service enters a DB management background-market price management menu to screen out early-warning records after receiving the early-warning prompt, judges whether the price difference of the records is a data problem or normal fluctuation of market price, and edits the records to modify the price if the price difference needs to be modified. And after all the early warning records are manually processed, releasing the data of the current day into an online state through one-key releasing operation of the management menu.
The front end display port accesses the back end service of each platform through the interface, and the back end service organizes market data and displays the market data to the client by inquiring the RDS database.
Illustratively, the RDS database has stored the data for the last statistical period:
and (3) capturing the obtained data of the current statistical period:
wherein, the online of the prasux, the Longzhong and the Chinese plastron are respectively the names of network platforms, PP is the category, and Z30S is the brand; north sea refineries are manufacturers.
The method comprises the following steps: grouping the market data in the current statistical period according to products and cities;
market data set one
Market data set two
Step two: filtering data according to a rule that the highest position of the price has the most occurrence times, and then taking an average value;
the price of the market data group PP/Z30S/North sea refinery/Dongguan is 9358, 9865, 9120 respectively;
if the most number of occurrences of the highest order (thousand-digit) of the price data is 9, the average price of the product in the market data group one is (9358+9865+9120)/3 ═ 9447;
the prices of the market data group II PP/Z30S/North sea refinery/Yuyao are 9600, 10100 and 9300 respectively;
if the highest position (thousand-digit) of the price data has the most number of times of appearance 9, 10100 is filtered out, and the average price of the products in the market data group two is (9600+9300)/2 ═ 9450
Step three: comparing the average price of the products in the current statistical period with the average price of the products in the previous statistical period, and giving early warning and informing an operator service when the difference value exceeds a preset threshold value (if the preset threshold value is 1000);
9447-.
9450-8300-1150 & gt 1000, writing into database and sending out early warning information to target user.
The present invention also provides an embodiment of a product price monitoring system, as shown in fig. 3, including:
the data grabbing module 10 is used for grabbing market data of products from each target platform regularly in the current statistical period;
the grouping module 20 is used for grouping the market data according to products and cities to obtain a plurality of market data groups;
the calculation module 30 is used for calculating the average price of the products of each market data group in the current statistical period according to a preset rule;
the comparison module 40 is used for calculating the difference value between the average product price of each market data set in the current statistical period and the average product price of the corresponding market data set in the previous statistical period;
and the early warning module 50 is used for sending early warning information to the target user when the difference value is greater than a preset threshold value.
In some embodiments, the calculation module 30 includes:
a determining unit for determining the highest order of the product price according to the product historical price of the market data set;
the statistical unit is used for counting the target number with the maximum occurrence frequency of the highest position in each price in the market data group in the current statistical period;
and the calculating unit is used for calculating the average price value of each product with the highest position of the market data set as the target number in the current statistical period, and taking the average price value as the average price of the products of the market data set.
In some embodiments, the data crawling module 10 comprises:
the data capturing unit is used for capturing market data of the products from each target platform regularly in the current statistical period and storing the market data into the cloud database;
the data pulling unit is used for pulling the market data from the cloud database at regular time in the current statistical period and storing the market data into a cache database;
the grouping module 20 includes:
the data extraction unit is used for extracting the market data in the cache database when the data quantity stored in the cache database is larger than a preset value;
the grouping unit is used for grouping the extracted market data according to products and cities to obtain a plurality of market data groups;
the system also comprises a storage display module;
the storage and display module is used for storing the market data group and the average price of the products of the market data group into a market database and carrying out visual display.
In some embodiments, the grouping unit is further configured to group the extracted market data according to product types to obtain a data group corresponding to each product, and group the products in each data group according to a city to obtain a plurality of market data groups.
Specifically, this embodiment is a system embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which are not described in detail herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of program modules is illustrated, and in practical applications, the above-described distribution of functions may be performed by different program modules, that is, the internal structure of the apparatus may be divided into different program units or modules to perform all or part of the above-described functions. Each program module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one processing unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software program unit. In addition, the specific names of the program modules are only for convenience of distinguishing from each other and are not used for limiting the protection scope of the present invention.
In one embodiment of the invention, a terminal device comprises a processor and a memory, wherein the memory is used for storing a computer program; and the processor is used for executing the computer program stored on the memory and realizing the product price monitoring method in the corresponding method embodiment.
The terminal equipment can be desktop computers, notebooks, palm computers, tablet computers, mobile phones, man-machine interaction screens and other equipment. The terminal device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the foregoing is merely an example of a terminal device and is not limiting of terminal devices, and that more or fewer components than those shown, or some of the components in combination, or different components may be included, such as: the terminal device may also include input/output interfaces, display devices, network access devices, communication buses, communication interfaces, and the like. A communication interface and a communication bus, and may further comprise an input/output interface, wherein the processor, the memory, the input/output interface and the communication interface complete communication with each other through the communication bus. The memory stores computer programs, and the processor is used for executing the computer programs stored on the memory and realizing the product price monitoring method in the corresponding method embodiment.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be an internal storage unit of the terminal device, such as: hard disk or memory of the terminal device. The memory may also be an external storage device of the terminal device, such as: the terminal equipment is provided with a plug-in hard disk, an intelligent memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like. Further, the memory may also include both an internal storage unit and an external storage device of the terminal device. The memory is used for storing the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.
A communication bus is a circuit that connects the described elements and enables transmission between the elements. For example, the processor receives commands from other elements through the communication bus, decrypts the received commands, and performs calculations or data processing according to the decrypted commands. The memory may include program modules such as kernels (kernel), middleware (middleware), Application Programming Interfaces (API), and applications. The program modules may be comprised of software, firmware or hardware, or at least two of the same. The input/output interface forwards commands or data entered by a user via the input/output interface (e.g., sensor, keyboard, touch screen). The communication interface connects the terminal equipment with other network equipment, user equipment and a network. For example, the communication interface may be connected to the network by wire or wirelessly to connect to external other network devices or user equipment. The wireless communication may include at least one of: wireless fidelity (WiFi), Bluetooth (BT), Near Field Communication (NFC), Global Positioning Satellite (GPS) and cellular communications, among others. The wired communication may include at least one of: universal Serial Bus (USB), high-definition multimedia interface (HDMI), asynchronous transfer standard interface (RS-232), and the like. The network may be a telecommunications network and a communications network. The communication network may be a computer network, the internet of things, a telephone network. The terminal device may be connected to the network via a communication interface, and a protocol used by the terminal device to communicate with other network devices may be supported by at least one of an application, an Application Programming Interface (API), middleware, a kernel, and a communication interface.
In an embodiment of the present invention, a storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operations performed by the corresponding embodiments of the product price monitoring method. For example, the storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
They may be implemented in program code that is executable by a computing device such that it is executed by the computing device, or separately, or as individual integrated circuit modules, or as a plurality or steps of individual integrated circuit modules. Thus, the present invention is not limited to any specific combination of hardware and software.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described apparatus/terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one type of logical function division, and other division manners may be available in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units may be stored in a storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by sending instructions to relevant hardware through a computer program, where the computer program may be stored in a storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. The computer program may be in the form of source code, object code, an executable file or some intermediate form, among others. The storage medium may include: any entity or device capable of carrying the computer program, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the content of the storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example: in certain jurisdictions, in accordance with legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunications signals.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A product price monitoring method, comprising:
capturing market data of products from each target platform regularly in a current statistical period;
grouping the market data according to products and cities to obtain a plurality of market data groups;
calculating the average price of the products of each market data group in the current statistical period according to a preset rule;
calculating the difference value between the average price of the product of each market data group in the current statistical period and the average price of the product of the corresponding market data group in the previous statistical period;
and when the difference is larger than a preset threshold value, sending early warning information to a target user.
2. A product price monitoring method according to claim 1,
the calculating the average price of the product of each market data group in the current statistical period according to the preset rule specifically comprises the following steps:
determining the highest order of the product price according to the historical product price of the market data set;
counting the target number with the maximum occurrence frequency of the highest position in each price in the market data group in the current counting period;
and calculating the average price value of each product with the highest position of the market data set as the target number in the current statistical period, and taking the average price value as the average price of the products of the market data set.
3. A product price monitoring method according to claim 1 or 2,
the market data for grabbing the product from each target platform regularly in the current statistical period specifically includes:
capturing market data of products from each target platform regularly in a current statistical period, and storing the market data into a cloud database;
regularly pulling the market quotation data from the cloud database in the current statistical period, and storing the market quotation data in a cache database;
the grouping the market data according to products and cities to obtain a plurality of market data sets specifically comprises:
when the data quantity stored in the cache database is larger than a preset value, extracting market data in the cache database;
grouping the extracted market data according to products and cities to obtain a plurality of market data groups;
after the average price of the products of each market data group in the current statistical period is calculated according to the preset rule, the method further comprises the following steps:
and storing the market data group and the average price of the products of the market data group into a market database, and performing visual display.
4. A product price monitoring method according to claim 3,
the grouping the extracted market data according to products and cities to obtain a plurality of market data sets specifically comprises:
grouping the extracted market data according to product types to obtain a data group corresponding to each product;
and grouping the products in each data group according to cities to obtain a plurality of market data groups.
5. A product price monitoring system, comprising:
the data grabbing module is used for grabbing market data of the product from each target platform regularly in the current statistical period;
the grouping module is used for grouping the market data according to products and cities to obtain a plurality of market data groups;
the calculation module is used for calculating the average price of the products of each market data group in the current statistical period according to a preset rule;
the comparison module is used for calculating the difference value between the average product price of each market data group in the current statistical period and the average product price of the corresponding market data group in the last statistical period;
and the early warning module is used for sending early warning information to a target user when the difference value is greater than a preset threshold value.
6. A product price monitoring system according to claim 5,
the calculation module comprises:
a determining unit for determining the highest order of the product price according to the product historical price of the market data set;
the statistical unit is used for counting the target number with the maximum occurrence frequency of the highest position in each price in the market data group in the current statistical period;
and the calculating unit is used for calculating the average price value of each product with the highest position of the market data set as the target number in the current statistical period, and taking the average price value as the average price of the products of the market data set.
7. A product price monitoring system according to claim 5 or 6,
the data capture module comprises:
the data capturing unit is used for capturing market data of the products from each target platform regularly in the current statistical period and storing the market data into the cloud database;
the data pulling unit is used for pulling the market data from the cloud database at regular time in the current statistical period and storing the market data into a cache database;
the grouping module includes:
the data extraction unit is used for extracting the market data in the cache database when the data quantity stored in the cache database is larger than a preset value;
the grouping unit is used for grouping the extracted market data according to products and cities to obtain a plurality of market data groups;
the system also comprises a storage display module;
the storage display module is used for storing the market data group and the average product price of the market data group into a market database and carrying out visual display.
8. A product price monitoring system according to claim 5 or 6,
the grouping unit is further configured to group the extracted market data according to product types to obtain a data group corresponding to each product, and group the products in each data group according to cities to obtain a plurality of market data groups.
9. A terminal device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor being configured to execute the computer program stored in the memory to perform the operations performed by the product price monitoring method according to any one of claims 1 to 4.
10. A storage medium having stored therein at least one instruction, which is loaded and executed by a processor to perform the operations performed by the product price monitoring method according to any one of claims 1 to 4.
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