CN116051136A - Price monitoring and early warning system and method based on price fluctuation rule - Google Patents
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Abstract
The invention relates to the technical field of commodity price monitoring and early warning, in particular to a price monitoring and early warning system and method based on a price fluctuation rule. The price monitoring and early warning system and method based on the price fluctuation rule comprises a data collection statistics module, a data analysis module and a monitoring and early warning module; the data collection and statistics module comprises a data collection module and a data statistics module; the detection early warning module comprises a multi-time granularity warning condition discovery module, a multi-time granularity warning condition processing module and a multi-time span warning condition summarizing module, can timely discover abnormal conditions in commodity price fluctuation, guarantees price early warning accuracy, avoids price monitoring delay and false alarm conditions, and achieves an early warning prompt function for commodity price.
Description
Technical Field
The invention relates to the technical field of commodity price monitoring and early warning, and the IPC classification number: g06q, more particularly relates to a price monitoring and early warning system and method based on price fluctuation rule.
Background
The market price of the commodity relates to the personal interests of residents, directly influences the daily life of the residents and is an important factor for the relationship folks. The market price of the commodity is influenced by various factors such as commodity supply and demand conditions, natural disasters, public events, artificial stir-frying and the like, and severe price fluctuation is easy to form, so that social stability and daily life of residents are influenced. Therefore, a price monitoring mechanism and a platform system of the commodity are established and perfected, abnormal conditions in price fluctuation can be found in time, price risks can be timely handled and avoided, normal civil demands are guaranteed, and stable and healthy development of economy and society is promoted.
The problem of untimely, inaccurate and incomplete monitoring of commodity price monitoring exists in manual work. Some researchers currently put forward price pre-warning methods, which have the following problems:
1) Lack of scalability: the existing method is used for monitoring and early warning of a certain commodity price, and cannot be well applied to other commodity types. The current price management departments need to monitor tens of varieties, and price monitoring methods without good expansion capability cannot be suitable for monitoring and early warning of the varieties.
2) The abnormality discovery accuracy is low: existing researchers often pre-warn price anomalies by changing the relevant factors that affect the price. However, since commodity price fluctuation is often influenced by a plurality of factors, the methods such as warning sign and the like proposed by the existing method are often inaccurate, false alarm is easily formed, and the method is very impractical for price monitoring departments.
3) Abnormal discovery is not in time: some researchers found price anomalies as a percentage of price versus average price change. However, since the average price of the commodity is continuously changed, the price abnormality cannot be reflected in time by the method based on the percentage, and a hysteresis condition of price monitoring is often formed.
The fluctuation of commodity price at present is influenced by factors such as consumption capability, price psychological expectation, supply and demand relation of residents, so that the commodity price fluctuation is a complex change process, and the commodity price is warned and prompted, so that the problem to be solved is urgent. In summary, it is very necessary to study the commodity price monitoring and early warning method which can be found in time, is not mistaken for reporting and is easy to expand.
Disclosure of Invention
The invention aims to design a price monitoring and early warning system and method based on a price fluctuation rule, which can timely find out abnormal conditions in commodity price fluctuation, ensure the accuracy of price early warning and avoid the delayed report and false report conditions of price monitoring.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the price monitoring and early warning system based on the price fluctuation rule comprises a data collection statistics module, a data analysis module and a monitoring and early warning module;
the data collection and statistics module comprises a data collection module and a data statistics module;
the data collection module is used for collecting historical prices of commodities in a certain period of time to obtain a data sample;
the data statistics module analyzes the data sample by utilizing a price definition method, establishes a theoretical system for distinguishing normal price and abnormal price, and obtains a commodity normal price fluctuation boundary line as a price basic line and a commodity abnormal price fluctuation boundary line as a price risk line;
the price basic line is a boundary line of a normal price fluctuation range, the price risk line is a boundary line of commodity general risk change and high risk change, the general risk change range is between the price basic line and the price risk line, and the high risk change range exceeds the price risk line;
the data analysis module is used for calculating normal and abnormal data fluctuation thresholds of the daily average value of the data collection and statistics module, and obtaining a normal price fluctuation threshold and an abnormal price fluctuation threshold of the daily average value of the commodity in a certain time period, and obtaining commodity price fluctuation range intervals corresponding to each warning condition category according to the price fluctuation thresholds;
the calculation of the fluctuation threshold value of the normal and abnormal data of the average value of the daily difference comprises the following steps:
s1: calculating the price difference between two adjacent days of a certain commodity price in a certain time period, wherein positive value difference values form a set S1, and negative value difference values form a set S2;
s2: calculating the average value of the set S1, and marking the average value as a1; calculating the average value of the set S2, and marking the average value as a2; num (S1) and num (S2) are the number of elements in the two sets S1 and S2, respectively; the calculation formula is as follows:
s3: extracting a value composition set SE1 with a value larger than a1 in S1, extracting a value composition set SE2 with a value smaller than a2 in S2, and calculating average values b1 and b2 of SE1 and SE 2; the calculation formula is as follows:
s4: determining risk classifications and thresholds for normal and abnormal fluctuations;
s5: the police condition categories are in one-to-one correspondence with the commodity price fluctuation range intervals.
The detection and early warning module comprises a multi-time granularity warning condition discovery module, a multi-time granularity warning condition processing module and a multi-time span warning condition summarizing module;
the multi-time granularity warning condition discovery module is mainly used for timely discovering price abnormal fluctuation, monitoring warning conditions by using price change of the multi-stage time granularity 1, and combining a price fluctuation classification table and price fluctuation rule monitoring of the multi-stage time granularity 1 to obtain a commodity price early warning table of the multi-stage time granularity 1;
the multi-stage time granularity 1 is the normal value and the abnormal value of price fluctuation of a commodity at intervals of 1 day, 2 days, 3 days, 5 days, 10 days and 20 days;
the commodity price early warning table is used for carrying out early warning judgment on the price fluctuation condition of the commodity by the warning condition change of the multilevel time granularity 1;
and respectively adding and calculating the moderate warning condition and the high warning condition risks of the multi-level time granularity 1 in the commodity price early warning table to obtain the sum of the price fluctuation general risks and the high risk quantity of each value in the multi-level time granularity 1.
The adding calculation comprises the following steps:
s2-1: the general and high risk of price fluctuation over K days is recorded and denoted as a k Middle And a k High ；
S2-2: the sum of the general risk and the high risk at K days apart is denoted AlarmNum;
k is any one of the values listed in the multi-stage time granularity 1;
the multi-time granularity alert processing module is mainly used for screening and eliminating abnormal false alarm conditions, carrying out statistical analysis on general risks and high risks in the multi-level time granularity 2 supervision targets, and filtering transient price fluctuation abnormal conditions;
the multi-stage time granularity 2 comprises 1 day, 3 days, 5 days and 10 days.
In the time listed by the multi-level time granularity 2, if a plurality of high risks exist, carrying out risk early warning prompt in the corresponding time, otherwise paying attention to prompt;
the multi-time span alarm condition summarizing module is mainly used for sequentially sequencing and reporting alarm conditions, summarizing alarm times of commodities in different time periods and different categories in the multi-stage time granularity 3 alarm conditions, and sequentially displaying the alarm times;
the multi-stage temporal granularity 3 comprises approximately 5 days, 10 days, 20 days.
The invention also discloses a price monitoring and early warning method, which comprises the following steps:
1) Acquiring fluctuation of commodity historical prices in a certain time period to obtain a data sample;
2) Analyzing the data sample by using a price definition method to obtain a commodity normal price fluctuation boundary as a price base line and a commodity abnormal price fluctuation boundary as a price risk line;
3) Carrying out normal and abnormal data fluctuation threshold calculation on the data of the data collection module, determining risk classification and threshold of normal fluctuation and abnormal fluctuation, and obtaining commodity price fluctuation range intervals corresponding to each warning condition type;
4) Combining the commodity price fluctuation range interval with the price fluctuation rule monitoring of the multi-stage time granularity 1 to form a commodity price early warning table;
5) Adding and calculating the moderate warning condition and the high warning condition risk of the multi-level time granularity 1 in the commodity price early warning table to obtain the sum of the price fluctuation general risk and the high risk of each value in the multi-level time granularity 1;
6) The very short price fluctuation condition in a certain day is filtered through statistical analysis of the supervision targets of the multi-stage time granularity 2;
7) And summarizing the alarm times of the commodities in different time periods and different categories according to the filtered commodity price early warning table, and displaying the alarm times in a sequencing manner.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention can obtain the price base line of the commodity normal price fluctuation by sampling and analyzing the commodity historical price fluctuation, and reflect the current consumption capability, the expected and supply and demand relation. The risk classification and threshold value of the normal fluctuation and the abnormal fluctuation can be determined based on the price base line through normal fluctuation and abnormal fluctuation threshold value calculation, and the commodity price fluctuation early warning function is realized.
(2) According to the invention, price fluctuation risk classification is combined with price fluctuation rule monitoring at commodity price intervals of 1, 2, 3, 5, 10 and 20 days, the warning condition of short-term and large-amplitude price change can be timely found by monitoring price change at commodity price intervals of 1, 2 and 3 days, price fluctuation formed by long-term homodromous and slow change can be monitored by monitoring price change at commodity price intervals of 5, 10 and 20 days, and multi-level and multi-time granularity early warning judgment on the daily price fluctuation condition is realized.
(3) The invention processes the police conditions in the price supervision targets by adding and calculating the moderate police conditions and the high police condition risks of the price change of different days of commodity interval, and filters out very short price fluctuation conditions, thereby reducing false alarm of the police conditions and improving the hit rate of important police conditions.
(4) The invention can obtain the alarm times of commodities in different time periods and different categories through alarm condition summarization, the alarm conditions can be ranked according to the alarm times and the severity, and rising and falling conditions are listed respectively, so that an operator can conveniently acquire the most kinds of alarm conditions by one key, and the effects of monitoring and automatically early warning commodity prices are achieved.
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Embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, the drawings are for illustration and description only and are not to be construed as limiting the invention.
FIG. 1 is a flow chart of a method for price monitoring and early warning based on price fluctuation rule;
fig. 2 is a structural system diagram of a third embodiment of the price monitoring and early warning system based on price fluctuation rule.
Detailed Description
Example 1
Fig. 1 is a flowchart of a method for monitoring and early warning of commodity price according to a first embodiment of the present invention.
As shown in FIG. 1, the commodity price monitoring and early warning method comprises the following steps of;
1) Acquiring fluctuation of commodity historical prices in a certain time period to obtain a data sample;
2) Analyzing the data sample by using a price definition method to obtain a commodity normal price fluctuation boundary as a price base line and a commodity abnormal price fluctuation boundary as a price risk line;
3) Carrying out normal and abnormal data fluctuation threshold calculation on the data of the data collection module, determining risk classification and threshold of normal fluctuation and abnormal fluctuation, and obtaining commodity price fluctuation range intervals corresponding to each warning condition type; the method specifically comprises the following steps:
s1: calculating the price difference between two adjacent days of a certain commodity price in a certain time period, wherein positive value difference values form a set S1, and negative value difference values form a set S2;
s2: calculating the average value of the set S1, and marking the average value as a1; calculating the average value of the set S2, and marking the average value as a2; num (S1) and num (S2) are the number of elements in the two sets S1 and S2, respectively; the calculation formula is as follows:
s3: extracting a value composition set SE1 with a value larger than a1 in S1, extracting a value composition set SE2 with a value smaller than a2 in S2, and calculating average values b1 and b2 of SE1 and SE 2; the calculation formula is as follows:
s4: determining risk classifications and thresholds for normal and abnormal fluctuations;
s5: the police condition categories are in one-to-one correspondence with the commodity price fluctuation range intervals.
4) Combining the commodity price fluctuation range interval with the price fluctuation rule monitoring of the multi-stage time granularity 1 to form a commodity price early warning table;
5) Adding and calculating the moderate warning condition and the high warning condition risk of the multi-level time granularity 1 in the commodity price early warning table to obtain the sum of the price fluctuation general risk and the high risk of each value in the multi-level time granularity 1; the method specifically comprises the following steps:
s2-1: the general and high risk of price fluctuation over K days is recorded and denoted as a k Middle And a k High ；
S2-2: the sum of the general risk and the high risk at K days apart is denoted AlarmNum;
6) The very short price fluctuation condition in a certain day is filtered through statistical analysis of the supervision targets of the multi-stage time granularity 2;
7) And summarizing the alarm times of the commodities in different time periods and different categories according to the filtered commodity price early warning table, and displaying the alarm times in a sequencing manner.
Example two
A price monitoring and early warning method based on a price fluctuation rule comprises the following steps:
acquiring fluctuation of commodity historical prices in a certain time period to obtain a data sample;
analyzing the data sample by using a price definition method to obtain a commodity normal price fluctuation boundary as a price base line and a commodity abnormal price fluctuation boundary as a price risk line, as shown in table 1;
TABLE 1 Commodity normal and abnormal price Classification criteria
According to commodity price fluctuation rules, the abnormal price change is further subdivided into general risk and high risk change. The price risk line is a boundary between a commodity general risk change and a high risk change, the general risk change range is between the price base line and the price risk line, and the high risk change range exceeds the price risk line, as shown in table 2;
TABLE 2 Commodity abnormal price Classification criteria
And carrying out normal and abnormal data fluctuation threshold calculation on the data of the data collection and statistics module. In this embodiment, taking the price of two adjacent days as an example, the calculation of the normal fluctuation and abnormal fluctuation threshold of the mean value of the daily difference is performed, and the steps include:
s1: the price difference between two adjacent days of a certain commodity price in 2021 is calculated, a positive value difference value forms a set S1, and a negative value difference value forms a set S2.
S2: calculating the average value of the set S1, and marking the average value as a1; the mean of the set S2 is calculated and denoted as a2. As shown in the following formulas, num (S1) and num (S2) are the numbers of elements of the two sets S1 and S2, respectively.
S3: values greater than a1 in S1 are extracted to form set SE1, and values less than a2 in S2 are extracted to form set SE2. The means b1 and b2 of SE1 and SE2 are calculated as shown in the following formula:
fourth step: risk classifications and thresholds for normal and abnormal fluctuations are determined.
The normal price fluctuation threshold and the abnormal price fluctuation threshold of the average value of the daily difference of certain commodity in 2021 are obtained through the steps, and are shown in the table 3:
TABLE 3 Normal and abnormal price volatility thresholds for the mean of the daily differences of certain commodities
Through the analysis, the alarm categories are in one-to-one correspondence with the commodity price fluctuation range intervals, as shown in table 4:
TABLE 4 police condition category and commodity price fluctuation range interval
Price fluctuation classification and price fluctuation rule monitoring of the multi-stage time granularity are combined together, and price fluctuation normal and abnormal values of a certain commodity in the multi-stage time granularity 1 are monitored at intervals of 1 day, 2 days, 3 days, 5 days, 10 days and 20 days to form a commodity price early warning table, wherein the commodity price early warning table is shown in table 5:
table 5 price early warning table for certain commodity in 2021
The sum of the general risk and the number of the high risk of price fluctuation of a certain commodity in 2021 every k days is obtained by adding and calculating the medium warning condition and the high warning condition risk of the multi-stage commodity price warning table, and the method comprises the following steps:
s2-1: the general and high risk of price fluctuation over K days is recorded and denoted as a k Middle And a k High ；
S2-2: the sum of the general risk and the high risk at K days apart is denoted AlarmNum;
according to the method, screening and eliminating abnormal false alarm conditions of the price of a commodity in 2021, and simultaneously supervising and counting general risks and high risks of the commodity for 1 day, 3 days, 5 days and 10 days continuously, wherein in the continuous time periods, if normal changes or general police conditions exist in most of continuous time, attention is formed; if a plurality of high risks exist in most of continuous time, risk early warning is carried out; as shown in table 6:
TABLE 6 price risk Table for certain Commodity and processing method
Summarizing the alarm times of commodities in different time periods and different categories in the multi-level time granularity 3 alarm condition, and sequentially displaying the alarm times as shown in a table 7:
table 7 alarm times display of commodities
Example III
FIG. 2 is a system configuration diagram of an embodiment of a price fluctuation pre-warning system for a commodity according to the present invention
As shown in fig. 2, the commodity price fluctuation monitoring and early warning system comprises: the system comprises a data collection statistics module, a data analysis module and a monitoring and early warning module;
the data collection and statistics module comprises a data collection module and a data statistics module;
the data collection module is used for collecting historical prices of commodities in a certain period of time to obtain a data sample;
the data statistics module analyzes the data sample by utilizing a price definition method, establishes a theoretical system for distinguishing normal price and abnormal price, and obtains a commodity normal price fluctuation boundary line as a price basic line and a commodity abnormal price fluctuation boundary line as a price risk line;
the data analysis module is used for calculating normal and abnormal data fluctuation thresholds of the daily average value of the data collection and statistics module, and obtaining a normal price fluctuation threshold and an abnormal price fluctuation threshold of the daily average value of the commodity in a certain time period, and obtaining commodity price fluctuation range intervals corresponding to each warning condition category according to the fluctuation thresholds;
the monitoring and early warning module comprises a multi-time granularity warning condition discovery module, a multi-time granularity warning condition processing module and a multi-time span warning condition summarizing module;
the multi-time granularity alert condition discovery module is mainly used for timely discovering price abnormal fluctuation, and monitoring alert conditions by using price change of multi-stage time granularity 1;
the multi-time granularity alert processing module is mainly used for screening and eliminating abnormal false alarm conditions, carrying out statistical analysis on general risks and high risks in the multi-level time granularity 2 supervision targets, and filtering transient price fluctuation abnormal conditions;
the multi-time span alarm condition summarizing module is mainly used for sequentially sequencing and reporting alarm conditions, summarizing alarm times of commodities in different time periods and different categories in the multi-stage time granularity 3 alarm conditions, and sequentially displaying the alarm times.
Claims (10)
1. The price monitoring and early warning system based on the price fluctuation rule is characterized by comprising a data collection statistics module, a data analysis module and a monitoring and early warning module;
the data collection and statistics module comprises a data collection module and a data statistics module;
the detection and early warning module comprises a multi-time granularity warning condition discovery module, a multi-time granularity warning condition processing module and a multi-time span warning condition summarizing module.
2. The price monitoring and early warning system based on price fluctuation law according to claim 1, wherein the data collection module is used for collecting historical prices of commodities in a certain time period to obtain data samples.
3. The price monitoring and early warning system based on the price fluctuation rule according to claim 1, wherein the data statistics module analyzes the data sample by utilizing a price definition method, establishes a theoretical system for distinguishing normal price and abnormal price, and obtains a commodity normal price fluctuation boundary line as a price basic line and a commodity abnormal price fluctuation boundary line as a price risk line.
4. A price monitoring and early warning system based on price fluctuation law according to claim 3, wherein the price base line is a boundary line of a normal price fluctuation range, the price risk line is a boundary line of a commodity general risk change and a high risk change, the general risk change range is between the price base line and the price risk line, and the high risk change range exceeds the price risk line.
5. The price monitoring and early warning system based on price fluctuation rule according to claim 1, wherein the data analysis module calculates normal and abnormal data fluctuation threshold values of the daily difference mean value of the data collection and statistics module, and can obtain the normal price fluctuation threshold value and the abnormal price fluctuation threshold value of the daily difference mean value of a commodity in a certain period of time, and obtain commodity price fluctuation range intervals corresponding to each warning condition category according to the fluctuation threshold values; the calculation of the fluctuation threshold value of the normal and abnormal data of the average value of the daily difference comprises the following steps:
s1: calculating the price difference between two adjacent days of a certain commodity price in a certain time period, wherein positive value difference values form a set S1, and negative value difference values form a set S2;
s2: calculating the average value of the set S1, and marking the average value as a1; calculating the average value of the set S2, and marking the average value as a2; num (S1) and num (S2) are the number of elements in the two sets S1 and S2, respectively; the calculation formula is as follows:
s3: extracting a value composition set SE1 with a value larger than a1 in S1, extracting a value composition set SE2 with a value smaller than a2 in S2, and calculating average values b1 and b2 of SE1 and SE 2; the calculation formula is as follows:
s4: determining risk classifications and thresholds for normal and abnormal fluctuations;
s5: the police condition categories are in one-to-one correspondence with the commodity price fluctuation range intervals.
6. The price monitoring and early warning system based on price fluctuation law according to claim 1, wherein the multi-time granularity alert condition discovery module is mainly used for timely discovering price abnormal fluctuation, monitoring alert conditions by using price change of multi-stage time granularity 1, and combining price fluctuation classification and price fluctuation law monitoring of multi-stage time granularity 1 to obtain a commodity price early warning table of multi-stage time granularity 1; the commodity price early warning table is used for carrying out early warning judgment of multilevel time granularity 1 on the price fluctuation condition of commodities; the multilevel time granularity 1 is normal and abnormal value of price fluctuation of 1 day, 2 days, 3 days, 5 days, 10 days and 20 days.
7. The price monitoring and early warning system based on the price fluctuation rule according to claim 6, wherein the medium warning condition and the high warning condition risk of the multi-level commodity price early warning table are summed and calculated to obtain the sum of the general risk and the high risk quantity of price fluctuation in a certain day; the adding calculation comprises the following steps:
s2-1: the general and high risk of price fluctuation over K days was recorded and noted as ak Middle And ak High ；
s2-2: the sum of the general risk and the high risk at K days apart is denoted AlarmNum;
the K is any one of the values listed in the above multi-level time granularity 1.
8. The price monitoring and early warning system based on price fluctuation rule according to claim 1, wherein the multi-time granularity warning condition processing module is mainly used for screening and eliminating abnormal false alarm conditions, carrying out statistical analysis on general risks and high risks in the multi-level time granularity 2 supervision targets, and filtering transient price fluctuation abnormal conditions; the multi-stage time granularity 2 comprises 1 day, 3 days, 5 days and 10 days.
9. The price monitoring and early warning system based on price fluctuation rule according to claim 1, wherein the multi-time span alarm condition summarizing module is mainly used for sequentially sequencing and reporting alarm conditions, summarizing alarm times of commodities in different time periods and different categories in the multi-level time granularity 3 alarm conditions, and sequentially displaying according to the alarm times; the multi-stage temporal granularity 3 comprises approximately 5 days, 10 days, 20 days.
10. The invention also discloses a monitoring method of the price monitoring and early warning system based on the price fluctuation rule as claimed in any one of claims 1-9, which is characterized by comprising the following steps:
1) Acquiring fluctuation of commodity historical prices in a certain time period to obtain a data sample;
2) Analyzing the data sample by using a price definition method to obtain a commodity normal price fluctuation boundary as a price base line and a commodity abnormal price fluctuation boundary as a price risk line;
3) Carrying out normal and abnormal data fluctuation threshold calculation on the data of the data collection module, determining risk classification and threshold of normal fluctuation and abnormal fluctuation, and obtaining commodity price fluctuation range intervals corresponding to each warning condition type;
4) Combining the commodity price fluctuation range interval with the price fluctuation rule monitoring of the multi-stage time granularity 1 to form a commodity price early warning table;
5) Adding and calculating the moderate warning condition and the high warning condition risks in the commodity price early warning table to obtain the sum of the price fluctuation general risks and the high risks of each value in the multi-level time granularity 1;
6) The very short price fluctuation condition in a certain day is filtered through statistical analysis of the supervision targets of the multi-stage time granularity 2;
7) And summarizing the alarm times of the commodities in different time periods and different categories according to the filtered commodity price early warning table, and displaying the alarm times in a sequencing manner.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116911914A (en) * | 2023-09-08 | 2023-10-20 | 杭州联海网络科技有限公司 | Marketing management method and system based on visual terminal |
| CN117217787A (en) * | 2023-08-28 | 2023-12-12 | 南京财经大学 | Consumption platform data analysis processing system based on management science |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117217787A (en) * | 2023-08-28 | 2023-12-12 | 南京财经大学 | Consumption platform data analysis processing system based on management science |
| CN117217787B (en) * | 2023-08-28 | 2024-05-07 | 南京财经大学 | Consumption platform data analysis processing system based on management science |
| CN116911914A (en) * | 2023-09-08 | 2023-10-20 | 杭州联海网络科技有限公司 | Marketing management method and system based on visual terminal |
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