CN113139823A - Intelligent pricing system - Google Patents
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Abstract
The invention discloses an intelligent pricing system, which comprises: the price recommending module: the system is used for generating a recommendation correlation matrix and a price information table; generating a similar price generation table through the recommendation correlation matrix and the price information table, and establishing a service database; the price-adjusting rule engine module is used for generating a variable table through the service database information and the similar price generating table, and checking the format of each variable of the variable table through a preset price-adjusting rule engine to generate an execution instruction; the predetermined prediction module is used for predicting the booking situation of the house in one month in the future through machine learning; the optimizer is used for generating a price adjustment rule engine-simulation result according to the prediction result and an execution instruction output by the price adjustment rule engine, adjusting parameters according to the result and feeding the parameters back to the price adjustment rule engine, and repeatedly executing the process until the preset iteration times are reached; and the executor is used for executing the execution instruction finally output by the price-adjusting rule engine module and outputting a pricing result.
Description
Technical Field
The invention relates to the field of intelligent management of operation pricing activities, in particular to an intelligent pricing system.
Background
With the aggravation of the competition of the residents, the demand of fine operation is generated. The price jump is part of the fine operation, and the traditional method is that workers stare at the price of each platform and adjust according to the current selling condition and profit expectation. The labor cost and the energy are greatly occupied, the judgment result is personal and cannot be copied, the efficiency is low, the cost is high, and the requirements of merchants cannot be met.
Disclosure of Invention
The invention aims to overcome the technical defects and provide an intelligent pricing system, which can automatically and flexibly determine the price according to the market condition without human intervention, and can be set in the system under the unmanned condition, so that the optimization of pricing and the minimization of labor cost are ensured.
In order to achieve the above object, the present invention provides an intelligent pricing system, including: the system comprises a price recommending module, a price adjusting rule engine module, a reservation predicting module, an optimizer and an executor;
the price recommending module is used for periodically collecting market price information and mutual recommending relations and generating a recommending correlation matrix and a price information table; generating a similar price generation table through the recommendation correlation matrix and the price information table, sending the similar price generation table to the price adjustment rule engine module, and establishing a service database and sending the service database to the price adjustment rule engine module;
the price-adjusting rule engine module is used for generating a variable table through the information of the service database and the similar price generating table: checking the format of each variable of the variable table through a preset price-adjusting rule engine to generate an execution instruction; the price adjusting rule engine comprises a judgment condition, a price change formula and an action time range;
the predetermined prediction module is used for predicting the booking situation of the house in one month in the future through machine learning and outputting a prediction result;
the optimizer is used for generating a price adjustment rule engine-simulation result according to a prediction result output by the preset prediction module and an execution instruction output by the price adjustment rule engine, adjusting parameters according to the result and feeding the parameters back to the price adjustment rule engine, and repeatedly executing the process until the preset iteration times are reached;
and the executor is used for executing the execution instruction finally output by the price-adjusting rule engine module and outputting a pricing result.
As an improvement of the above system, the system further comprises: the price searching optimization module is used for searching the optimal price through a multi-channel price-off strategy; the method specifically comprises the following steps:
establishing a house B which is the same as the house A, modifying partial content, and setting up the house B, wherein the price of the house B is 100-200 yuan higher than that of the house A;
monitoring the order of the house B; if an order exists, placing the order for A to purchase; closing the information of the house A and the house B in other channels;
and if the order of the house B accounts for more than 30%, adjusting the price of the house A in the finally output execution instruction to the price of the house B, and putting the house B off shelf.
As an improvement of the above system, the recommended correlation matrix records the correlation relationship among all sold houses; the price information table records the price information and the selling condition of a certain house at different moments; the similar price generation table records the lowest price of the house set with the same recommended relationship, the highest price of the house set with the same recommended relationship and the middle price of the house set with the same recommended relationship; the service database records information of all houses, initial prices and their related information.
As an improvement of the above system, the variable records the minimum price of the house set with the same recommended relationship, the maximum price of the house set with the same recommended relationship, the medium price and the initial price of the house set with the same recommended relationship, and the number of selling days of all houses.
As an improvement of the above system, the reservation prediction module is implemented by:
periodically collecting the market conditions of the whole industry, carrying out deconstruction processing and storing the deconstruction processing into a log;
generating a mirror image of the whole market at a certain moment, periodically and incrementally synchronizing a local database, and generating a corresponding table of actual transaction data and the current market state;
quantizing the data to make the data accord with integral distribution, and converting the numerical value to make the numerical value fall between 0 and 1;
extracting 33% of data as a test set, and the balance being a training set;
training a random forest model by using a training set, inputting labeled data, and outputting a classifier model aiming at the data; then testing the trained model on a test set; repeatedly optimizing;
and predicting the booking condition of the current data after one month by using the trained model, and outputting a prediction result.
As an improvement of the above system, the system further comprises: the unmanned pricing module is used for scheduling and checking the execution instructions at regular time and executing the execution instructions which cannot be called by the program; and scheduling at fixed time, checking a price searching switch of the price adjusting rule engine, and starting the price searching switch or optimizing the price adjusting rule according to a pre-optimization strategy.
The invention has the advantages that:
1. the system of the invention is used for automatic price adjustment, in particular to an unmanned system which intelligently decides price and automatically executes;
2. the system of the invention does not need personnel intervention, can automatically and flexibly determine the price according to the market condition and set the price into the system under the unmanned condition, and ensures the optimization of pricing and the minimization of labor cost.
Drawings
Fig. 1 is a schematic structural diagram of the intelligent pricing system of the invention.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the intelligent pricing system of the present invention includes: the system comprises a price recommendation module, a price adjusting rule engine module, a reservation prediction module, an optimizer, an actuator, a price searching optimization module and an unmanned pricing module;
the price recommending module is used for acquiring price information disclosed by the market through the information acquisition system and recommending current reasonable pricing, so that the price recommending module can be quickly adjusted in market competition and occupies a reasonable pricing interval;
periodically collecting market price information and mutual recommendation relations to form a recommendation correlation matrix and a price information table; generating a similar price generation table through the recommendation correlation matrix and the price information table:
and after the scheduler is started, reading the DAG code file of the ETL function, calculating a preset scheduling condition, and running the corresponding ETL job when the trigger condition (such as the number of points every day or every xx minutes) is met. After the ETL program is started by the scheduling program, reading the data in the recommendation correlation matrix and the price information table, cleaning and converting the data, and generating new data after the following processing: price mirror image data at a certain moment in the whole market; generating recommendation relation and degree-centrality data of a specified house in a recommendation network; price data such as the average price of the market of the specified house in the recommended house source is generated, and the corresponding data is stored in a similar price generation table (the price information can be directly configured and used for automatically processing price change in a price adjustment rule engine module).
The service database information is generated locally by a human.
The price adjusting rule engine module: the price display platform is used for executing the final price display platform through the preset rules, so that the falling of the rules is realized.
The user-configured price-adjusting rule engine comprises: judging conditions, a price change formula and an action time range; generating a variable table through the information of the service database and the similar price generating table; and analyzing the variables of the variable table by using an analyzer based on the price-adjusting rule engine: reading a rule text at regular intervals, converting the condition + formula into a Boolean expression, inputting real-time data in a database, outputting a price adjustment coefficient, and finally summarizing the price coefficient and the action range to generate an execution instruction;
a reservation prediction module: through machine learning, the booking situation of one month in the future is predicted, price recorded by the pricing rule engine module is subjected to simulated confrontation, a prediction result is output, the best price is optimized through an optimizer, and then the original rule is adjusted and optimized.
Periodically collecting the market conditions of the whole industry, carrying out deconstruction processing and storing the deconstruction processing into a log;
generating a mirror image of the whole market at a certain moment, and periodically and incrementally synchronizing the local database; generating a corresponding table of actual transaction data and the current market state (a wide table with a particularly high dimension);
quantizing the data to make the data accord with integral distribution, and converting the numerical value to make the numerical value fall between 0 and 1;
extracting 33% of data as a test set, and the balance being a training set;
training a random forest model (inputting labeled data and outputting a classifier model aiming at the data) by using a training set; then testing the trained model on a test set; and (4) repeatedly optimizing, and predicting the booking condition of the current data after one month by using the machine learning trained model.
And adjusting the rules of the price-adjusting rule engine module, generating an execution instruction, importing the execution instruction into a machine-learned trained model, and performing man-machine confrontation of the rules so as to adjust and optimize the existing rules.
And the executor is used for calling the API interface and executing the execution instruction output by the price-adjusting rule engine module.
A price searching optimization module: and searching for the optimal price through a multi-channel price-off strategy.
Looking up a price searching switch in a price adjusting rule engine, if the price searching switch is turned on, creating a house B which is the same as the existing house A, modifying partial content, and calling an API (application program interface) to put on shelf B to an existing channel (the price is about 100-200 yuan higher than A); monitoring the order of the house B; if an order exists, placing the order for A to purchase; closing the house states of the houses A and B in other channels; if the order of the house B accounts for more than 30%, the price of the house A is adjusted to be the price of the house B, and the house B is put on shelf.
Unmanned pricing module: the operation of the modules is completed by simulating the operation of a human on the existing PMS through an RPA (software flow automation) technology, and unmanned pricing is realized. Scheduling and checking the execution instruction at regular time, and executing a price-adjusting execution instruction which cannot be called through an API (application programming interface); and scheduling at fixed time, checking the existing price adjustment rule price searching switch, and starting the switch or optimizing the price adjustment rule according to a pre-optimization strategy, so that the whole system can be reduced in dimension and used.
Example (c):
the information stored in the price recommendation module includes: a recommendation correlation matrix, a price information table, a service database and a similar price generation table.
The recommendation correlation matrix is established by acquiring price information disclosed by the market through an information acquisition system:
recommendation relevance matrix
price information table
The service database is manually established locally:
business database information
| House | House information | Rent costWait for information | Investor-related information | City related information |
| a house | xxx | xxx | xxx | xxx |
Generating a similar price generation table through the recommendation correlation matrix and the price information table:
similar price generating table
In the rule engine module, generating a variable table through the service database information and the similar price generating table:
variable meter
The set price-adjusting rule engine:
price-adjusting rule engine
Generate price-adjusted rules engine-execute instructions:
| house | Date | New price | Decoration style |
| a house | Day 1 of the future | 142.5 | Japanese style |
| a house | Day 2 of the future | 142.5 | Japanese style |
| a house | Day 3 of the future | 142.5 | Japanese style |
| a house | Day 4 of the future | 142.5 | Japanese style |
| a house | Day 5 of the future | 142.5 | Japanese style |
| a house | Day 6 of the future | 142.5 | Japanese style |
| a house | Day 7 of the future | 142.5 | Japanese style |
The predetermined prediction module predicts the house selling condition;
the optimizer generates a price adjustment rule engine-simulation result according to the prediction module and the price adjustment rule engine-pre-execution instruction:
rating rules Engine-simulation results
| House | Date | New price | Decoration style | Predicted results |
| a house | Day 1 of the future | 142.5 | Japanese style | Forecast non-saleable |
| a house | Day 2 of the future | 142.5 | Japanese style | Forecast non-saleable |
| a house | Day 3 of the future | 142.5 | Japanese style | Forecast non-saleable |
| a house | Day 4 of the future | 142.5 | Japanese style | Forecast non-saleable |
| a house | Day 5 of the future | 142.5 | Japanese style | Forecast non-saleable |
| a house | Day 6 of the future | 142.5 | Japanese style | Forecast non-saleable |
| a house | Day 7 of the future | 142.5 | Japanese style | Forecast availability |
And adjusting parameters and feeding the parameters back to a price adjustment rule engine according to the simulation result, and repeatedly executing the process until the preset iteration times are reached.
The actuator runs and executes the instructions:
| house | Date | New price |
| a house | Day 1 of the future | 142.5 |
| a house | Day 2 of the future | 142.5 |
| a house | Day 3 of the future | 142.5 |
| a house | Day 4 of the future | 142.5 |
| a house | Day 5 of the future | 142.5 |
| a house | Day 6 of the future | 142.5 |
| a house | (Future)Day 7 | 142.5 |
The price searching optimization module is used for searching the optimal price through a multi-channel price-off strategy;
the improvement points of the invention are that:
in the price recommendation module, a similarity algorithm based on propagation similarity is used. In the price adjustment rule engine module: using an autonomous research algorithm (namely analyzing the conditions, formulas and rule grammar of the algorithm through a Boolean expression), and converting a rule set into a price judgment strategy;
finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. An intelligent pricing system, the system comprising: the system comprises a price recommending module, a price adjusting rule engine module, a reservation predicting module, an optimizer and an executor;
the price recommending module is used for periodically collecting market price information and mutual recommending relations and generating a recommending correlation matrix and a price information table; generating a similar price generation table through the recommendation correlation matrix and the price information table, sending the similar price generation table to the price adjustment rule engine module, and establishing a service database and sending the service database to the price adjustment rule engine module;
the price-adjusting rule engine module is used for generating a variable table through the information of the service database and the similar price generating table: checking the format of each variable of the variable table through a preset price-adjusting rule engine to generate an execution instruction; the price adjusting rule engine comprises a judgment condition, a price change formula and an action time range;
the predetermined prediction module is used for predicting the booking situation of the house in one month in the future through machine learning and outputting a prediction result;
the optimizer is used for generating a price adjustment rule engine-simulation result according to a prediction result output by the preset prediction module and an execution instruction output by the price adjustment rule engine, adjusting parameters according to the result and feeding the parameters back to the price adjustment rule engine, and repeatedly executing the process until the preset iteration times are reached;
and the executor is used for executing the execution instruction finally output by the price-adjusting rule engine module and outputting a pricing result.
2. The intelligent pricing system of claim 1, wherein the system further comprises: the price searching optimization module is used for searching the optimal price through a multi-channel price-off strategy; the method specifically comprises the following steps:
establishing a house B which is the same as the house A, modifying partial content, and setting up the house B, wherein the price of the house B is 100-200 yuan higher than that of the house A;
monitoring the order of the house B; if an order exists, placing the order for A to purchase; closing the information of the house A and the house B in other channels;
and if the order of the house B accounts for more than 30%, adjusting the price of the house A in the finally output execution instruction to the price of the house B, and putting the house B off shelf.
3. The intelligent pricing system of claim 1 or 2, wherein the recommendation relevance matrix records relevance relationships between all sold houses; the price information table records the price information and the selling condition of a certain house at different moments; the similar price generation table records the lowest price of the house set with the same recommended relationship, the highest price of the house set with the same recommended relationship and the middle price of the house set with the same recommended relationship; the service database records information of all houses, initial prices and their related information.
4. The intelligent pricing system of claim 3, wherein the variables record the recommended relationship of all houses as lowest price of the set of houses, as highest price of the set of houses, as medium price, as initial price and as many days as sold.
5. The intelligent pricing system of claim 1, wherein the reservation forecasting module is implemented by:
periodically collecting the market conditions of the whole industry, carrying out deconstruction processing and storing the deconstruction processing into a log;
generating a mirror image of the whole market at a certain moment, periodically and incrementally synchronizing a local database, and generating a corresponding table of actual transaction data and the current market state;
quantizing the data to make the data accord with integral distribution, and converting the numerical value to make the numerical value fall between 0 and 1;
extracting 33% of data as a test set, and the balance being a training set;
training a random forest model by using a training set, inputting labeled data, and outputting a classifier model aiming at the data; then testing the trained model on a test set; repeatedly optimizing;
and predicting the booking condition of the current data after one month by using the trained model, and outputting a prediction result.
6. The intelligent pricing system of claim 1, wherein the system further comprises: the unmanned pricing module is used for scheduling and checking the execution instructions at regular time and executing the execution instructions which cannot be called by the program; and scheduling at fixed time, checking a price searching switch of the price adjusting rule engine, and starting the price searching switch or optimizing the price adjusting rule according to a pre-optimization strategy.
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