CN117350750A - Marketing data analysis system and method based on big data - Google Patents
Marketing data analysis system and method based on big data Download PDFInfo
- Publication number
- CN117350750A CN117350750A CN202311361696.6A CN202311361696A CN117350750A CN 117350750 A CN117350750 A CN 117350750A CN 202311361696 A CN202311361696 A CN 202311361696A CN 117350750 A CN117350750 A CN 117350750A
- Authority
- CN
- China
- Prior art keywords
- data
- preset
- monitoring module
- component
- product
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 85
- 238000007405 data analysis Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 claims abstract description 100
- 230000008569 process Effects 0.000 claims abstract description 75
- 238000012545 processing Methods 0.000 claims abstract description 67
- 230000002159 abnormal effect Effects 0.000 claims abstract description 37
- 238000013500 data storage Methods 0.000 claims abstract description 24
- 230000000903 blocking effect Effects 0.000 claims abstract description 11
- 238000003860 storage Methods 0.000 claims description 36
- 230000001105 regulatory effect Effects 0.000 claims description 11
- 230000008859 change Effects 0.000 claims description 10
- 230000001960 triggered effect Effects 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 4
- NFEIBWMZVIVJLQ-UHFFFAOYSA-N mexiletine hydrochloride Chemical compound [Cl-].CC([NH3+])COC1=C(C)C=CC=C1C NFEIBWMZVIVJLQ-UHFFFAOYSA-N 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims 1
- 230000006872 improvement Effects 0.000 abstract description 11
- 230000009467 reduction Effects 0.000 description 10
- 238000012216 screening Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 4
- 230000007423 decrease Effects 0.000 description 4
- 230000033228 biological regulation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of data processing, in particular to a marketing data analysis system and method based on big data, comprising the following steps: the accuracy monitoring module is used for judging whether the data acquisition process reaches a deviation running state according to the variances of the product stock quantity acquired by a plurality of different types of data sources; the safety risk monitoring module is used for judging whether the data processing process is in a network attack risk state according to the abnormal quantity proportion of the collected data when responding to the starting signal; the bearing capacity monitoring module is used for judging whether the data storage process is in an overload operation state according to the system blocking time length in the unit period; the validity monitoring module is used for judging whether the data acquisition process reaches an invalid acquisition state according to the product quantity ratio with the complete traceability chain when the accuracy monitoring module outputs the first corresponding quantity. The invention realizes the improvement of the effectiveness and the safety of data processing.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a marketing data analysis system and method based on big data.
Background
In the prior art, a large data technology is generally used in a method for processing marketing data of a product, and specific processes are data acquisition, data screening and cleaning are performed on relevant user data of the product and product characteristic data on a relevant platform, and corresponding charts are generated according to the processed data, but the problems of inaccurate data acquisition and insufficient safety in a data source acquisition process exist in a data acquisition process and a data processing process in the prior art respectively.
Chinese patent publication No.: CN115983873a discloses a marketing data analysis system based on big data, the system comprising: the system comprises a data acquisition module, a database, a data analysis module, an intelligent screening module and a data use module; the output end of the data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the intelligent screening module, and the output end of the intelligent screening module is connected with the input end of the data use module; collecting comment data of all shops in a network through the data collecting module; storing all acquired data through the database; analyzing the authenticity of the user comments through the data analysis module, and judging whether the store performs false marketing or not; screening shops subjected to false marketing through the intelligent screening module; and identifying the user demand through the data use module, and performing intelligent pushing. It can be seen that the big data based marketing data analysis system has problems of reduced information and increased possibility of being attacked by the network due to too low collection speed of data source data caused by too small number of user classifications of stored regular purchased products, resulting in reduced effectiveness and security of data processing.
Disclosure of Invention
Therefore, the invention provides a marketing data analysis system and a marketing data analysis method based on big data, which are used for solving the problems that in the prior art, the information is reduced due to the fact that the number of the stored user classifications for regularly purchasing products is too small, and the possibility of being attacked by a network is increased due to the fact that the acquisition speed of data source data is too low, so that the effectiveness and the safety of data processing are reduced.
In order to achieve the above object, the present invention provides a marketing data analysis system based on big data, comprising: the accuracy monitoring module is used for judging whether the data acquisition process reaches a deviation running state according to the variance of the stock quantity of the products acquired by a plurality of different types of data sources, and if so, adjusting the stored user classification quantity for regularly purchasing the products to output a first corresponding quantity, or triggering a starting signal for monitoring the risk of network attack; the security risk monitoring module is connected with the accuracy monitoring module and is used for judging whether the data processing process is in a network attack risk state according to the abnormal quantity proportion of the acquired data when responding to the starting signal, and if so, the acquisition speed of the data source data is adjusted; the bearing capacity monitoring module is connected with the safety risk monitoring module and is used for judging whether the data storage process is in an overload running state according to the system katen time length in the unit period when the data acquisition speed of the data source is regulated, and regulating the storage capacity ratio of the first similar data or regulating the priority of the data storage if the data storage process is in the overload running state; the validity monitoring module is connected with the accuracy monitoring module and is used for judging whether the data acquisition process reaches an invalid acquisition state according to the product quantity ratio of the complete traceability chain when the accuracy monitoring module outputs the first corresponding quantity, and if so, the first corresponding quantity is adjusted to output the second corresponding quantity.
Further, the accuracy monitoring module comprises a calling component and a first judging component connected with the calling component, wherein,
the calling component is used for calling the product inventory amounts acquired by the data sources of the different types and calculating the variance of the product inventory amounts acquired by the data sources of the different types;
the first judging component is used for judging that the data acquisition process reaches a deviation running state when the variance of the product stock quantity acquired by a plurality of different types of data sources triggers a preset first variance condition and a preset second variance condition;
the preset first variance condition is that variances of product stock quantities acquired by a plurality of different types of data sources are larger than a preset first variance and smaller than or equal to a preset second variance; the preset second variance condition is that variances of product stock quantities acquired by a plurality of different types of data sources are larger than the preset second variances; the preset first variance is smaller than the preset second variance.
Further, the security risk monitoring module comprises an acquisition component and a second judging component connected with the acquisition component, wherein,
the acquisition component is used for acquiring the abnormal quantity of the acquired data and calculating the ratio of the abnormal quantity of the acquired data according to the acquisition result;
The second judging component is used for primarily judging that the data processing process is in a network attack risk state when the preset first variance condition is triggered independently, and secondarily judging that the data processing process is in the network attack risk state when the preset duty ratio condition is triggered;
the preset duty ratio condition is that the abnormal quantity duty ratio of the collected data is larger than the preset duty ratio.
Further, the bearing capacity monitoring module comprises a statistics component and a third judging component connected with the statistics component, wherein,
the statistics component is used for counting the system card time length in a unit period when the security risk monitoring module completes the adjustment of the acquisition speed of the data source data;
and the third judging component is used for judging that the data storage process is in an overload operation state when the system card time length in the unit period is longer than the preset time length.
Further, the effectiveness monitoring module comprises a calling component and a fourth judging component connected with the calling component, wherein,
the invoking component is used for counting the number of products with complete tracing chains in the acquired product information and calculating the number proportion of the products with complete tracing chains according to the product number counting result;
The fourth judging component is used for judging that the data acquisition process reaches an invalid acquisition state when the product quantity ratio of the complete tracing chain is smaller than the preset quantity ratio.
Further, the accuracy monitoring module further comprises a quantity adjusting component connected with the first judging component, and the quantity adjusting component is used for adjusting the stored user classification quantity for periodically purchasing the product according to the difference value between the variance of the product stock quantity acquired by a plurality of different types of data sources and a preset second variance when the data acquisition process is judged to reach a deviation running state so as to output a first corresponding quantity;
the variance of the product stock quantity obtained by the first corresponding quantity and a plurality of different types of data sources is in direct proportion to the difference value of the preset second variance.
Further, the security risk monitoring module further comprises a speed adjusting component connected with the second judging component and used for adjusting the acquisition speed of the data source data according to the difference value between the abnormal quantity ratio of the acquired data and the preset ratio when the data processing process is in the network attack risk state;
the data source data acquisition speed is in direct proportion to the difference value between the abnormal quantity ratio of the acquired data and the preset ratio.
Further, the bearing capacity monitoring module further comprises a duty ratio adjusting component connected with the third judging component, and the duty ratio adjusting component is used for adjusting the storage capacity duty ratio of the first type of similar data according to the difference value between the system blocking duration and the preset duration in the unit period when the data storage process is in an overload operation state, and adjusting the storage priority of the first type of mutation data in the second type of similar data to be before the storage priority of the second type of mutation data;
the storage capacity ratio of the first similar data is inversely proportional to the difference value between the system blocking duration and the preset duration in the unit period; the first similar data are data with the same data type and different data sources; the second similar data are data which are positioned at different moments and have the same data type; the first type of abrupt change data is data with the absolute value of the difference value of the data in the same period as the last year being larger than a preset absolute value, and the second type of abrupt change data is data with the absolute value of the difference value of the data in the same period as the last year being smaller than or equal to the preset absolute value.
Further, the validity monitoring module further comprises a quantity adjusting component connected with the fourth judging component, and the quantity adjusting component is used for adjusting the first corresponding quantity according to the difference value between the preset quantity ratio and the product quantity ratio with the complete tracing chain when the data acquisition process reaches a wireless acquisition state so as to output a second corresponding quantity;
Wherein the second corresponding number is inversely proportional to a difference between the preset number ratio and the product number ratio with the complete traceability chain.
The invention also provides a marketing data analysis method based on big data, which comprises the following steps:
step S1, a data processing module collects user data and product data from a plurality of data sources and processes the collected data to output a corresponding chart;
step S2, when triggering a preset first variance condition and a preset second variance condition, the accuracy monitoring module judges that the data acquisition process reaches a deviation running state according to variances of product stock quantity acquired by a plurality of different types of data sources, and adjusts the stored user classification quantity for periodically purchasing the product so as to output a first corresponding quantity, or triggers a starting signal for monitoring network attack risks;
step S3, when a response is generated to the starting signal, the safety risk monitoring module adjusts the acquisition speed of the data source data when judging that the data processing process is in a network attack risk state according to the abnormal quantity proportion of the acquired data;
step S4, when the adjustment of the acquisition speed of the data source data is completed, the bearing capacity monitoring module adjusts the storage capacity ratio of the first similar data or adjusts the priority of the data storage when judging that the data storage process is in an overload operation state according to the system card time length in the unit period;
And S5, when the data acquisition process reaches an invalid acquisition state according to the product quantity ratio with the complete tracing chain, the effectiveness monitoring module carries out secondary adjustment on the first corresponding quantity so as to output a second corresponding quantity.
Compared with the prior art, the system has the beneficial effects that by arranging the accuracy monitoring module, the safety risk monitoring module, the bearing capacity monitoring module and the effectiveness monitoring module, the accuracy monitoring module regulates the quantity of the stored user classification which regularly purchases the product when the accuracy of data acquisition is lower than an allowable range, the influence of the quantity of the collected data caused by too small quantity of the stored user classification which causes the reduction of the effectiveness of data processing is reduced, the influence of the quantity of the collected data caused by too low quantity of the stored user classification on the data caused by too small quantity of the stored user classification is reduced by regulating the acquisition speed of the data source data according to the abnormal quantity proportion of the acquired data, the influence of the acquisition speed which causes the reduction of the safety of the data processing due to the fact that the regulation of the acquisition speed of the data source data is too low is reduced, the influence of the storage capacity proportion of the first type similar data is regulated according to the system clamping time length in a unit period, the influence of the stored user classification which causes the reduction of the safety of the data processing due to the fact that the storage capacity proportion of the storage capacity of the first type similar data is too large is reduced, and the quality of the stored user classification which causes the quality of the data caused by the fact that the quality of the stored user classification is reduced due to the fact that the quality of the data purchase of the second type similar data is reduced according to the fact that the regulation of the storage capacity proportion of the storage of the data is not accurate is reduced.
Further, the system judges the accuracy of data acquisition by setting the preset first variance and the preset second variance, so that the influence of the decline of the effectiveness of data processing caused by inaccurate judgment of the accuracy of data acquisition is reduced, and the effectiveness and the safety of the data processing are further improved.
Furthermore, the system of the invention carries out secondary judgment on the security risk under the preset duty ratio by setting the preset duty ratio, thereby reducing the influence of the security reduction of the data processing caused by inaccurate secondary judgment on the security risk and further realizing the improvement of the effectiveness and the security of the data processing.
Further, the system judges the data capacity bearing capacity when the system card time length in the unit period is longer than the preset time length by setting the preset time length, reduces the influence of the reduction of the safety of the data processing caused by inaccurate judgment of the data capacity bearing capacity, and further improves the effectiveness and the safety of the data processing.
Further, the system judges the effectiveness of data acquisition when the product quantity ratio with the complete tracing chain is smaller than the preset quantity ratio by setting the preset quantity ratio, reduces the influence of the decline of the effectiveness of data processing caused by inaccurate judgment of the effectiveness of data acquisition, and further improves the effectiveness and the safety of the data processing.
Furthermore, the system adjusts the stored user classification quantity of the product purchased regularly under the preset second variance condition by setting the difference between the variances of the product stock quantity acquired by a plurality of different types of data sources and the preset second variance, so that the influence of the reduced effectiveness of data processing caused by the too small stored user classification quantity of the product purchased regularly is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
Furthermore, the system adjusts the acquisition speed of the data source data under the preset duty ratio by setting the difference value between the abnormal quantity duty ratio of the acquired data and the preset duty ratio, reduces the influence of the reduction of the safety of the data processing caused by the too low acquisition speed, and further improves the effectiveness and the safety of the data processing.
Furthermore, the system adjusts the storage capacity ratio of the first type of similar data under the condition of the preset duration by setting the difference value between the system blocking duration and the preset duration in the unit period, reduces the influence of the reduction of the safety of data processing caused by the overlarge storage capacity ratio of the first type of similar data, and further realizes the improvement of the effectiveness and the safety of the data processing.
Further, the system of the invention sets the difference between the preset quantity ratio and the product quantity ratio with the complete tracing chain, and performs secondary adjustment on the first corresponding quantity of the clients with the same purchasing time in repeated purchasing clients under the preset quantity ratio condition, thereby reducing the influence of the reduced effectiveness of data processing caused by the overlarge first corresponding quantity and further realizing the improvement of the effectiveness and the safety of the data processing.
Drawings
FIG. 1 is a block diagram showing the overall structure of a marketing data analysis system based on big data according to an embodiment of the present invention;
FIG. 2 is an overall flow chart of a big data based marketing data analysis system in accordance with an embodiment of the present invention;
FIG. 3 is a flowchart showing a step S2 of the big data-based marketing data analysis system according to the embodiment of the present invention;
fig. 4 is a specific flowchart of step S3 of the big data-based marketing data analysis system according to the embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Fig. 1, fig. 2, fig. 3, and fig. 4 show an overall block diagram, an overall flowchart, a specific flowchart of step S2, and a specific flowchart of step S3 of the marketing data analysis system based on big data according to the embodiment of the invention. The invention discloses a marketing data analysis system based on big data, which comprises:
the accuracy monitoring module is used for determining whether the data acquisition process reaches a deviation running state according to the variance of the stock quantity of the products acquired by a plurality of different types of data sources, and if so, adjusting the stored user classification quantity for regularly purchasing the products to output a first corresponding quantity, or triggering a starting signal for monitoring the risk of network attack;
the security risk monitoring module is connected with the accuracy monitoring module and is used for determining whether the data processing process is in a network attack risk state according to the abnormal quantity proportion of the acquired data when responding to the starting signal, and if so, the acquisition speed of the data source data is adjusted;
the bearing capacity monitoring module is connected with the safety risk monitoring module and is used for determining whether the data storage process is in an overload running state according to the system katen time length in a unit period when the data acquisition speed of the data source is regulated, and regulating the storage capacity ratio of the first similar data or regulating the priority of the data storage if the data storage process is in the overload running state;
The validity monitoring module is connected with the accuracy monitoring module and is used for determining whether the data acquisition process reaches an invalid acquisition state according to the product quantity ratio of the complete traceability chain when the accuracy monitoring module outputs the first corresponding quantity, and if so, adjusting the first corresponding quantity to output a second corresponding quantity.
Specifically, the variance of the product inventory amounts obtained by the plurality of different types of data sources is a variance obtained by performing statistical calculation on the product inventory amounts obtained by the plurality of different types of data sources, and the calculation method of the variance of the product inventory amounts obtained by the plurality of different types of data sources is a conventional technical means well known to those skilled in the art, so that a process of calculating the variance of the product inventory amounts obtained by the plurality of different types of data sources is not described herein.
Specifically, the calculation formula of the abnormal quantity ratio of the acquired data is as follows:
wherein G is the abnormal number of the collected data, K is the number of the collected abnormal data, and T is the total number of the collected data.
Specifically, the system blocking duration in the unit period is the duration of system blocking caused by overlarge data capacity in the data storage process in a single period.
Specifically, the calculation formula of the product quantity ratio with the complete traceability chain is as follows:
wherein S is the product quantity ratio with complete tracing chains, E is the quantity of products with complete tracing chains, and F is the total quantity of products.
Specifically, the user categories include users who have purchased the product only once and users who have purchased the product twice and more.
In particular, the traceability chain means a product information chain which relates to the whole process of product production to user signing in, such as the process of product production, transportation, packaging, ordering and transferring.
According to the system, the accuracy monitoring module, the safety risk monitoring module, the bearing capacity monitoring module and the effectiveness monitoring module are arranged, when the accuracy of data acquisition is lower than the allowable range, the accuracy monitoring module adjusts the stored user classification quantity of the product periodically purchased, the influence of the fact that the acquired quantity is too small due to the adjustment inaccuracy of the stored user classification quantity of the product periodically purchased is reduced, the acquisition speed of data source data is adjusted according to the abnormal quantity proportion of acquired data, the influence of the fact that the acquisition speed is too low due to the adjustment inaccuracy of the acquisition speed of the data source data on the safety of data processing is reduced, the influence of the fact that the storage capacity proportion of the first type of similar data is long when the system in a unit period is low on the fact that the storage capacity proportion of the first type of similar data is not accurate is adjusted is reduced, the influence of the fact that the data processing safety of the data processing is reduced due to the fact that the storage capacity proportion of the second type of similar data is excessively large is reduced according to the adjustment inaccuracy of the storage capacity proportion of the first type of similar data, the fact that the quality of the data is adjusted according to the product quantity of the product with complete tracing chain is reduced, and the effect of the fact that the data processing of the second type of purchasing is reduced due to the fact that the user to the adjustment of the storage capacity proportion of the second type of the product is reduced according to the fact that the quality proportion of the quality of the product with respect to the quality of the product with complete tracing chain is reduced.
With continued reference to fig. 2, the accuracy monitoring module includes a calling component and a first determining component coupled to the calling component, wherein,
the calling component is used for calling the product inventory amounts acquired by the data sources of the different types and calculating the variance of the product inventory amounts acquired by the data sources of the different types;
the first judging component is used for judging that the data acquisition process reaches a deviation running state when the variance of the product stock quantity acquired by a plurality of different types of data sources triggers a preset first variance condition and a preset second variance condition;
the preset first variance condition is that variances of product stock quantities acquired by a plurality of different types of data sources are larger than a preset first variance and smaller than or equal to a preset second variance; the preset second variance condition is that variances of product stock quantities acquired by a plurality of different types of data sources are larger than the preset second variances; the preset first variance is smaller than the preset second variance.
Specifically, the calling component is a program that calls and computes variances for product inventory acquired by several different types of data sources.
Specifically, the first determination component is a program for determining whether the data acquisition process reaches a deviation running state according to variances of product stock amounts acquired by a plurality of different types of data sources.
Specifically, the variances of the product stock amounts obtained by the plurality of different types of data sources are denoted as Q, the preset first variance is denoted as Q1, q1=50 pieces are set, the preset second variance is denoted as Q2, q2=55 pieces are set, the differences between the variances of the product stock amounts obtained by the plurality of different types of data sources and the preset second variance are denoted as Δq, and Δq=q-Q2 is set.
According to the system, the preset first variance and the preset second variance are set, so that the accuracy of data acquisition is judged, the influence of the decline of the effectiveness of data processing caused by inaccurate judgment of the accuracy of data acquisition is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
With continued reference to fig. 2, the security risk monitoring module includes an acquisition component and a second determination component coupled to the acquisition component, wherein,
the acquisition component is used for acquiring the abnormal quantity of the acquired data and calculating the ratio of the abnormal quantity of the acquired data according to the acquisition result;
the second judging component is used for primarily judging that the data processing process is in a network attack risk state when the preset first variance condition is triggered independently, and secondarily judging that the data processing process is in the network attack risk state when the preset duty ratio condition is triggered;
The preset duty ratio condition is that the abnormal quantity duty ratio of the collected data is larger than the preset duty ratio.
Specifically, the acquisition component is a program that acquires an abnormal amount of acquired data and calculates an abnormal amount ratio of the acquired data.
Specifically, the second determining component is a program for performing secondary determination on whether the data processing process is in a network attack risk state according to the abnormal number proportion of the collected data.
Specifically, the preset duty ratio is denoted by P1, p1=0.4 is set, the abnormal number duty ratio of the collected data is denoted by P, the difference between the abnormal number duty ratio of the collected data and the preset duty ratio is denoted by Δp, and Δp=p-P1 is set.
According to the system, the preset duty ratio is set, and the security risk is secondarily judged under the preset duty ratio condition, so that the influence of security degradation of data processing caused by inaccurate secondary judgment of the security risk is reduced, and the effectiveness and the security of the data processing are further improved.
With continued reference to fig. 2, the load-bearing capacity monitoring module includes a statistics component and a third determination component coupled to the statistics component, wherein,
The statistics component is used for counting the system card time length in a unit period when the security risk monitoring module completes the adjustment of the acquisition speed of the data source data;
and the third judging component is used for judging that the data storage process is in an overload operation state when the system card time length in the unit period is longer than the preset time length.
Specifically, the statistics component is a program that performs statistics on the system-on-time length in a unit period.
Specifically, the third determining component is a program for determining whether the data storage process is in an overload operation state according to the system-on time length in the unit period.
Specifically, the preset time period is denoted as Y1, y1=0.03 s is set, the system-on time period in the unit period is denoted as Y, the difference between the system-on time period in the unit period and the preset time period is denoted as Δy, and Δy=y-Y1 is set.
According to the system, the preset time length is set, and the data capacity bearing capacity is judged when the system pause time length in the unit period is longer than the preset time length, so that the influence of the reduction of the safety of data processing caused by inaccurate judgment of the data capacity bearing capacity is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
With continued reference to fig. 2, the validity monitoring module includes a retrieving component and a fourth determining component coupled to the retrieving component, wherein,
the invoking component is used for counting the number of products with complete tracing chains in the acquired product information and calculating the number proportion of the products with complete tracing chains according to the product number counting result;
the fourth judging component is used for judging that the data acquisition process reaches an invalid acquisition state when the product quantity ratio of the complete tracing chain is smaller than the preset quantity ratio.
Specifically, the invoking component is a program for counting the number of products with complete tracing chains in the acquired product information and calculating the product number ratio with complete tracing chains.
Specifically, the fourth determining component is a program for determining whether the data acquisition process reaches an invalid acquisition state according to the product quantity ratio with the complete tracing chain.
Specifically, the preset number ratio is denoted as R0, r0=0.4 is set, the product number ratio with the complete tracing chain is denoted as R, the difference between the preset number ratio and the product number ratio with the complete tracing chain is denoted as Δr, and Δr=r0-R is set.
According to the system, the preset quantity duty ratio is set, and when the quantity duty ratio of the products with the complete tracing chains is smaller than the preset quantity duty ratio, the effectiveness of data acquisition is judged, so that the influence of the decline of the effectiveness of data processing caused by inaccurate judgment of the effectiveness of data acquisition is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
With continued reference to fig. 3, the accuracy monitoring module further includes a quantity adjusting component connected to the first determining component, configured to adjust the stored classified quantity of users who purchase the product periodically according to a difference between a variance of the inventory quantity of the product obtained by a plurality of different types of data sources and a preset second variance to output a first corresponding quantity when it is determined that the data acquisition process reaches a deviation running state;
the variance of the product stock quantity obtained by the first corresponding quantity and a plurality of different types of data sources is in direct proportion to the difference value of the preset second variance.
Specifically, the accuracy monitoring module uses a preset first quantity adjustment coefficient to adjust the stored user classification quantity of the product purchased periodically to a first stored user classification quantity of the product purchased periodically under a preset first variance value condition; the preset first variance difference condition is that the variance of the product stock quantity acquired by a plurality of different types of data sources is smaller than or equal to the preset variance difference value;
The accuracy monitoring module uses a preset second quantity adjustment coefficient to adjust the stored user classification quantity of the product purchased regularly to a second stored user classification quantity of the product purchased regularly under the condition of a preset second variance difference; the preset second variance difference condition is that the difference between the variances of the product stock quantities acquired by the data sources of different types and the preset second variance is larger than the preset variance difference;
wherein the preset first number adjustment coefficient is smaller than the preset second number adjustment coefficient.
Specifically, the preset variance difference is denoted as Δq0, Δq0=20 pieces are set, the preset first quantity adjustment coefficient is denoted as α1, α1=1.2, the preset second quantity adjustment coefficient is denoted as α2, α2=1.4, the stored number of user classifications for periodic purchases of the product is denoted as V, where 1 < α1 < α2, the adjusted first corresponding quantity is denoted as V ', V' =v× (1+αi)/2, where αi is the preset i-th quantity adjustment coefficient, and i=1, 2 are set.
According to the system, the variance of the product stock quantity acquired by a plurality of different types of data sources is set to be different from the preset second variance, the stored user classification quantity for periodically purchasing the product is adjusted under the preset second variance, the influence of the reduced effectiveness of data processing caused by the too small stored user classification quantity for periodically purchasing the product is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
With continued reference to fig. 3, the security risk monitoring module further includes a speed adjusting component connected to the second determining component, configured to adjust, when the data processing process is in a network attack risk state, a data acquisition speed of the data source according to a difference between an abnormal number duty ratio of the acquired data and a preset duty ratio;
the data source data acquisition speed is in direct proportion to the difference value between the abnormal quantity ratio of the acquired data and the preset ratio.
Specifically, the security risk monitoring module uses a preset first speed adjustment coefficient to adjust the acquisition speed of the data source data to the acquisition speed of the first data source data under the condition of a preset first duty ratio difference value; the preset first duty ratio difference condition is that the difference value between the abnormal quantity duty ratio of the collected data and the preset duty ratio is smaller than or equal to the preset duty ratio difference value;
the safety risk monitoring module uses a preset second speed adjustment coefficient to adjust the acquisition speed of the data source data to the acquisition speed of the second data source data under the condition of a preset second duty ratio difference value; the preset second duty ratio difference condition is that the difference between the abnormal quantity duty ratio of the collected data and the preset duty ratio is larger than the preset duty ratio difference;
Wherein the preset first speed adjustment coefficient is smaller than the preset second speed adjustment coefficient.
Specifically, the preset duty ratio difference is denoted as Δp0, Δp0=0.2, the preset first speed adjustment coefficient is denoted as β1, β1=1.1, the preset second speed adjustment coefficient is denoted as β2, β2=1.3, the acquisition speed of the data source data is denoted as H, wherein 1 < β1 < β2, the acquisition speed of the adjusted data source data is denoted as H ', H' =h× (1+2βj)/3, wherein βj is the preset j-th speed adjustment coefficient, and j=1, 2.
According to the system, the difference value between the abnormal quantity duty ratio of the acquired data and the preset duty ratio is set, the acquisition speed of the data source data is adjusted under the preset duty ratio condition, the influence of the reduction of the safety of data processing caused by the too low acquisition speed is reduced, and the effectiveness and the safety of the data processing are further improved.
With continued reference to fig. 4, the load-bearing capacity monitoring module further includes a duty ratio adjusting component connected to the third determining component, configured to adjust a storage capacity duty ratio of the first type of similar data according to a difference between a system on duration and a preset duration in a unit period when the data storage process is in an overload operation state, and adjust a storage priority of the first type of abrupt change data in the second type of similar data to be before the storage priority of the second type of abrupt change data;
The storage capacity ratio of the first similar data is inversely proportional to the difference value between the system blocking duration and the preset duration in the unit period; the first similar data are data with the same data type and different data sources; the second similar data are data which are positioned at different moments and have the same data type; the first type of abrupt change data is data with the absolute value of the difference value of the data in the same period as the last year being larger than a preset absolute value, and the second type of abrupt change data is data with the absolute value of the difference value of the data in the same period as the last year being smaller than or equal to the preset absolute value.
Specifically, the bearing capacity monitoring module uses a preset second duty ratio adjustment coefficient to adjust the storage capacity duty ratio of the first similar data to a first duty ratio under the condition of a preset first time length difference value; the preset first time length difference condition is that the difference value between the system blocking time length in the unit period and the preset time length is smaller than or equal to the preset time length difference value;
the bearing capacity monitoring module uses a preset first duty ratio adjustment coefficient to adjust the storage capacity duty ratio of the first similar data to a second duty ratio under the condition of a preset second duration difference value; the preset second time length difference condition is that the difference between the system blocking time length in the unit period and the preset time length is larger than the preset time length difference;
The preset first duty ratio adjustment coefficient is smaller than the preset second duty ratio adjustment coefficient.
Specifically, the preset duration difference is denoted as Δy0, Δy0=0.02 s is set, the preset first duty cycle adjustment coefficient is denoted as γ1, γ1=0.8 is set, the preset second duty cycle adjustment coefficient is denoted as γ2, γ2=0.9 is set, the storage capacity ratio of the first type of similar data is denoted as L, wherein 0 < γ1 < γ2 < 1, the storage capacity ratio of the adjusted first type of similar data is denoted as L ', L' =lx (1+3γm)/4 is set, γm is the preset m-th duty cycle adjustment coefficient, and m=1, 2 is set.
Specifically, the data types include an inventory amount of the product, a price of the product, a weight of the product, and an order quantity of the product.
According to the system, the difference value between the system blocking duration in the unit period and the preset duration is set, the storage capacity ratio of the first type of similar data is adjusted under the preset duration condition, the influence of the reduction of the safety of data processing caused by the overlarge storage capacity ratio of the first type of similar data is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
With continued reference to fig. 4, the validity monitoring module further includes a quantity adjusting component connected to the fourth determining component, configured to adjust the first corresponding quantity according to a difference between a preset quantity ratio and a product quantity ratio with a complete tracing chain when the data acquisition process reaches a wireless acquisition state, so as to output a second corresponding quantity;
Wherein the second corresponding number is inversely proportional to a difference between the preset number ratio and the product number ratio with the complete traceability chain.
Specifically, the effectiveness monitoring module secondarily adjusts the first corresponding number to a third number by using a preset fourth number secondary adjustment coefficient under the condition of presetting a first number duty ratio difference; the preset first quantity duty ratio difference condition is that the difference between the preset quantity duty ratio and the product quantity duty ratio with the complete tracing chain is smaller than or equal to the preset quantity duty ratio difference;
the effectiveness monitoring module secondarily adjusts the first corresponding number to a fourth number by using a preset third number secondary adjustment coefficient under the condition of presetting a second number duty ratio difference; the preset second quantity duty ratio difference condition is that the difference between the preset quantity duty ratio and the product quantity duty ratio with the complete tracing chain is larger than the preset quantity duty ratio difference;
wherein the third number of secondary adjustment coefficients is less than the fourth number of secondary adjustment coefficients.
Specifically, the preset number of duty ratio differences is denoted as Δr0, Δr0=20 is set, the preset third number of secondary adjustment coefficients is denoted as α3, α3=0.7, the preset fourth number of secondary adjustment coefficients is denoted as α4, α4=0.8, the adjusted second corresponding number is denoted as V ", V" =v' × (1+αw)/2 is set, wherein αw is the preset w-th number of secondary adjustment coefficients, and w=3, 4 is set.
According to the system, the preset quantity ratio and the difference value of the product quantity ratio with the complete traceability chain are set, and the first corresponding quantity of the clients with the same purchasing time in repeated purchasing clients is secondarily adjusted under the preset quantity ratio, so that the influence of the reduced effectiveness of data processing caused by the overlarge first corresponding quantity is reduced, and the improvement of the effectiveness and the safety of the data processing is further realized.
With continued reference to fig. 2, a marketing data analysis method based on big data includes:
step S1, a data processing module collects user data and product data from a plurality of data sources and processes the collected data to output a corresponding chart;
step S2, when triggering a preset first variance condition and a preset second variance condition, the accuracy monitoring module judges that the data acquisition process reaches a deviation running state according to variances of product stock quantity acquired by a plurality of different types of data sources, and adjusts the stored user classification quantity for periodically purchasing the product so as to output a first corresponding quantity, or triggers a starting signal for monitoring network attack risks;
step S3, when a response is generated to the starting signal, the safety risk monitoring module adjusts the acquisition speed of the data source data when judging that the data processing process is in a network attack risk state according to the abnormal quantity proportion of the acquired data;
Step S4, when the adjustment of the acquisition speed of the data source data is completed, the bearing capacity monitoring module adjusts the storage capacity ratio of the first similar data or adjusts the priority of the data storage when judging that the data storage process is in an overload operation state according to the system card time length in the unit period;
and S5, when the data acquisition process reaches an invalid acquisition state according to the product quantity ratio with the complete tracing chain, the effectiveness monitoring module carries out secondary adjustment on the first corresponding quantity so as to output a second corresponding quantity.
Specifically, the collected data includes registration information of the user, browsing history of the user, transaction information of the user, basic information of the product, price and stock quantity of the product, evaluation of the product by the user, and sales data of the product.
Specifically, the step S2 includes:
step S21, when triggering a preset first variance condition and a preset second variance condition, the accuracy monitoring module judges that the data acquisition process reaches a deviation running state according to variances of product stock quantity acquired by a plurality of different types of data sources;
in step S22, the accuracy monitoring module adjusts the stored classified number of users who purchase the product regularly when the deviation running state is reached, so as to output the first corresponding number, or triggers a start signal for monitoring the risk of network attack.
Specifically, the step S3 includes:
step S31, when a response is generated to the starting signal, the security risk monitoring module judges that the data processing process is in a network attack risk state according to the abnormal quantity proportion of the collected data;
and S32, the security risk monitoring module adjusts the acquisition speed of the data source data when the security risk monitoring module is in a network attack risk state.
Example 1
In this embodiment 1, the accuracy monitoring module adjusts the stored classification quantity of the users who purchase the product regularly according to the difference between the variance of the stock quantity of the product obtained by the data sources of several different types and the preset second variance, wherein the preset difference is denoted as Δq0, Δq0=20 pieces, the preset first quantity adjustment coefficient is denoted as α1, α1=1.2, the preset second quantity adjustment coefficient is denoted as α2, α2=1.4, and the stored classification quantity of the users who purchase the product regularly is denoted as V, wherein 1 < α1 < α2, α1=1.2, α2=1.4, Δq0=20, and v=4.
In this embodiment 1, Δq=25 pieces, the accuracy monitoring module determines Δq > - Δq0 and adjusts the stored number of categories of users who purchase the product regularly to a second number by using a preset first number adjustment coefficient, so as to calculate V' =4× (1+1.2)/2=5 pieces.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (10)
1. A marketing data analysis system based on big data, comprising:
the accuracy monitoring module is used for judging whether the data acquisition process reaches a deviation running state according to the variance of the stock quantity of the products acquired by a plurality of different types of data sources, and if so, adjusting the stored user classification quantity for regularly purchasing the products to output a first corresponding quantity, or triggering a starting signal for monitoring the risk of network attack;
the security risk monitoring module is connected with the accuracy monitoring module and is used for judging whether the data processing process is in a network attack risk state according to the abnormal quantity proportion of the acquired data when responding to the starting signal, and if so, the acquisition speed of the data source data is adjusted;
The bearing capacity monitoring module is connected with the safety risk monitoring module and is used for judging whether the data storage process is in an overload running state according to the system katen time length in the unit period when the data acquisition speed of the data source is regulated, and regulating the storage capacity ratio of the first similar data or regulating the priority of the data storage if the data storage process is in the overload running state;
the validity monitoring module is connected with the accuracy monitoring module and is used for judging whether the data acquisition process reaches an invalid acquisition state according to the product quantity ratio of the complete traceability chain when the accuracy monitoring module outputs the first corresponding quantity, and if so, the first corresponding quantity is adjusted to output the second corresponding quantity.
2. The big data based marketing data analysis system of claim 1, wherein the accuracy monitoring module comprises a calling component and a first decision component coupled to the calling component, wherein,
the calling component is used for calling the product inventory amounts acquired by the data sources of the different types and calculating the variance of the product inventory amounts acquired by the data sources of the different types;
The first judging component is used for judging that the data acquisition process reaches a deviation running state when the variance of the product stock quantity acquired by a plurality of different types of data sources triggers a preset first variance condition and a preset second variance condition;
the preset first variance condition is that variances of product stock quantities acquired by a plurality of different types of data sources are larger than a preset first variance and smaller than or equal to a preset second variance; the preset second variance condition is that variances of product stock quantities acquired by a plurality of different types of data sources are larger than the preset second variances; the preset first variance is smaller than the preset second variance.
3. The big data based marketing data analysis system of claim 2, wherein the security risk monitoring module comprises an acquisition component and a second decision component coupled to the acquisition component, wherein,
the acquisition component is used for acquiring the abnormal quantity of the acquired data and calculating the ratio of the abnormal quantity of the acquired data according to the acquisition result;
the second judging component is used for primarily judging that the data processing process is in a network attack risk state when the preset first variance condition is triggered independently, and secondarily judging that the data processing process is in the network attack risk state when the preset duty ratio condition is triggered;
The preset duty ratio condition is that the abnormal quantity duty ratio of the collected data is larger than the preset duty ratio.
4. The big data based marketing data analysis system of claim 3, wherein the load bearing capacity monitoring module comprises a statistics component and a third decision component coupled to the statistics component, wherein,
the statistics component is used for counting the system card time length in a unit period when the security risk monitoring module completes the adjustment of the acquisition speed of the data source data;
and the third judging component is used for judging that the data storage process is in an overload operation state when the system card time length in the unit period is longer than the preset time length.
5. The big data based marketing data analysis system of claim 4, wherein the effectiveness monitoring module comprises a retrieval component and a fourth decision component coupled to the retrieval component, wherein,
the invoking component is used for counting the number of products with complete tracing chains in the acquired product information and calculating the number proportion of the products with complete tracing chains according to the product number counting result;
the fourth judging component is used for judging that the data acquisition process reaches an invalid acquisition state when the product quantity ratio of the complete tracing chain is smaller than the preset quantity ratio.
6. The big data based marketing data analysis system of claim 5, wherein the accuracy monitoring module further comprises a quantity adjustment component connected to the first decision component for adjusting the stored number of categories of users who purchase the product periodically to output a first corresponding quantity based on a difference between a variance of the inventory of the product acquired by the plurality of different types of data sources and a preset second variance when it is decided that the data acquisition process reaches a biased operational state;
the variance of the product stock quantity obtained by the first corresponding quantity and a plurality of different types of data sources is in direct proportion to the difference value of the preset second variance.
7. The big data based marketing data analysis system of claim 6, wherein the security risk monitoring module further comprises a speed adjustment component connected to the second decision component for adjusting the speed of the collection of data source data according to the difference between the abnormal number duty cycle of the collected data and the preset duty cycle when the data processing process is in a network attack risk state;
the data source data acquisition speed is in direct proportion to the difference value between the abnormal quantity ratio of the acquired data and the preset ratio.
8. The big data-based marketing data analysis system according to claim 7, wherein the bearing capacity monitoring module further comprises a duty ratio adjusting component connected with the third judging component, wherein the duty ratio adjusting component is configured to adjust a storage capacity duty ratio of the first type of similar data according to a difference value between a system stuck time length and a preset time length in a unit period when the data storage process is in an overload operation state, and adjust a storage priority of the first type of abrupt change data in the second type of similar data to be before a storage priority of the second type of abrupt change data;
the storage capacity ratio of the first similar data is inversely proportional to the difference value between the system blocking duration and the preset duration in the unit period; the first similar data are data with the same data type and different data sources; the second similar data are data which are positioned at different moments and have the same data type; the first type of abrupt change data is data with the absolute value of the difference value of the data in the same period as the last year being larger than a preset absolute value, and the second type of abrupt change data is data with the absolute value of the difference value of the data in the same period as the last year being smaller than or equal to the preset absolute value.
9. The big data based marketing data analysis system of claim 8, wherein the effectiveness monitoring module further comprises a quantity adjustment component coupled to the fourth decision component for adjusting the first corresponding quantity to output a second corresponding quantity based on a difference between a preset quantity ratio and a product quantity ratio having a complete trace chain when the data collection process reaches a wireless collection state;
wherein the second corresponding number is inversely proportional to a difference between the preset number ratio and the product number ratio with the complete traceability chain.
10. A marketing data analysis method based on big data using the method of claims 1-9, comprising:
step S1, a data processing module collects user data and product data from a plurality of data sources and processes the collected data to output a corresponding chart;
step S2, when triggering a preset first variance condition and a preset second variance condition, the accuracy monitoring module judges that the data acquisition process reaches a deviation running state according to variances of product stock quantity acquired by a plurality of different types of data sources, and adjusts the stored user classification quantity for periodically purchasing the product so as to output a first corresponding quantity, or triggers a starting signal for monitoring network attack risks;
Step S3, when a response is generated to the starting signal, the safety risk monitoring module adjusts the acquisition speed of the data source data when judging that the data processing process is in a network attack risk state according to the abnormal quantity proportion of the acquired data;
step S4, when the adjustment of the acquisition speed of the data source data is completed, the bearing capacity monitoring module adjusts the storage capacity ratio of the first similar data or adjusts the priority of the data storage when judging that the data storage process is in an overload operation state according to the system card time length in the unit period;
and S5, when the data acquisition process reaches an invalid acquisition state according to the product quantity ratio with the complete tracing chain, the effectiveness monitoring module carries out secondary adjustment on the first corresponding quantity so as to output a second corresponding quantity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311361696.6A CN117350750A (en) | 2023-10-20 | 2023-10-20 | Marketing data analysis system and method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311361696.6A CN117350750A (en) | 2023-10-20 | 2023-10-20 | Marketing data analysis system and method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117350750A true CN117350750A (en) | 2024-01-05 |
Family
ID=89368840
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311361696.6A Pending CN117350750A (en) | 2023-10-20 | 2023-10-20 | Marketing data analysis system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117350750A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112507208A (en) * | 2020-11-02 | 2021-03-16 | 北京迅达云成科技有限公司 | Network data acquisition system based on big data |
| WO2021077844A1 (en) * | 2019-10-22 | 2021-04-29 | 苏宁云计算有限公司 | Risk supervision method and system for commodity net price |
| CN114093171A (en) * | 2022-01-21 | 2022-02-25 | 杭州海康威视数字技术股份有限公司 | Traffic running state monitoring method and device based on multi-source data fusion |
| WO2022057260A1 (en) * | 2020-09-15 | 2022-03-24 | 浙江大学 | Industrial control system communication network anomaly classification method |
| JP7130884B1 (en) * | 2021-09-17 | 2022-09-05 | 株式会社ミスミグループ本社 | LOGISTICS MANAGEMENT DEVICE, CONTROL METHOD AND CONTROL PROGRAM OF LOGISTICS MANAGEMENT DEVICE |
| CN115641192A (en) * | 2022-11-10 | 2023-01-24 | 佛罗伦(中国)有限公司 | Accurate marketing method and system for container assets |
| CN116245735A (en) * | 2023-05-08 | 2023-06-09 | 北京惠朗时代科技有限公司 | Print control instrument operation supervision system based on data analysis |
| CN116628774A (en) * | 2023-05-30 | 2023-08-22 | 安徽国防科技职业学院 | Data storage integrity supervision system based on cloud computing |
| CN116756427A (en) * | 2023-06-27 | 2023-09-15 | 鼎翰文化股份有限公司 | Travel information pushing system based on big data |
-
2023
- 2023-10-20 CN CN202311361696.6A patent/CN117350750A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021077844A1 (en) * | 2019-10-22 | 2021-04-29 | 苏宁云计算有限公司 | Risk supervision method and system for commodity net price |
| WO2022057260A1 (en) * | 2020-09-15 | 2022-03-24 | 浙江大学 | Industrial control system communication network anomaly classification method |
| CN112507208A (en) * | 2020-11-02 | 2021-03-16 | 北京迅达云成科技有限公司 | Network data acquisition system based on big data |
| JP7130884B1 (en) * | 2021-09-17 | 2022-09-05 | 株式会社ミスミグループ本社 | LOGISTICS MANAGEMENT DEVICE, CONTROL METHOD AND CONTROL PROGRAM OF LOGISTICS MANAGEMENT DEVICE |
| CN114093171A (en) * | 2022-01-21 | 2022-02-25 | 杭州海康威视数字技术股份有限公司 | Traffic running state monitoring method and device based on multi-source data fusion |
| CN115641192A (en) * | 2022-11-10 | 2023-01-24 | 佛罗伦(中国)有限公司 | Accurate marketing method and system for container assets |
| CN116245735A (en) * | 2023-05-08 | 2023-06-09 | 北京惠朗时代科技有限公司 | Print control instrument operation supervision system based on data analysis |
| CN116628774A (en) * | 2023-05-30 | 2023-08-22 | 安徽国防科技职业学院 | Data storage integrity supervision system based on cloud computing |
| CN116756427A (en) * | 2023-06-27 | 2023-09-15 | 鼎翰文化股份有限公司 | Travel information pushing system based on big data |
Non-Patent Citations (2)
| Title |
|---|
| 余强明;朱小栋;吴靓;姚润坤;张瑶瑶;: "大数据时代顾客隐私关注度对企业精准营销效益影响的实证研究", 重庆工商大学学报(自然科学版), no. 04, 9 June 2020 (2020-06-09) * |
| 佟乐;: "汽车行业数据智能分析平台的构建与分析", 内蒙古科技与经济, no. 16, 30 August 2017 (2017-08-30) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20090177520A1 (en) | Techniques for casual demand forecasting | |
| CN112002424A (en) | Intelligent analysis management system for intelligent community endowment health based on big data | |
| CN108628973B (en) | Agricultural data sharing system based on block chain | |
| CN114219558B (en) | Intelligent agricultural product recommendation system based on data mining | |
| CN115860579B (en) | Production quality monitoring system for flour processing | |
| CN111178708A (en) | Target scoring method and device, computer-readable storage medium and electronic equipment | |
| CN117350750A (en) | Marketing data analysis system and method based on big data | |
| CN110852906B (en) | Method and system for identifying electricity stealing suspicion based on high-dimensional random matrix | |
| CN113988723A (en) | User behavior locking method and system based on power consumption data anomaly analysis | |
| Sun et al. | Equilibrium joining strategies in the single server queues with negative customers | |
| JP4296026B2 (en) | Product demand forecasting system, product sales volume adjustment system | |
| JP2009043291A (en) | System for predicting demand of merchandise and system for adjusting number of sales of merchandise | |
| CN117217865A (en) | Personalized recommendation system based on big data analysis | |
| CN116089401B (en) | User data management method and system | |
| CN115907837B (en) | Futures data analysis and risk prediction method and system based on machine learning | |
| CN116186017B (en) | Big data collaborative supervision method and platform | |
| CN108710635A (en) | A kind of content recommendation method and device | |
| CN116611829A (en) | Consumption supervision system based on block chain | |
| CN116151860A (en) | Intelligent ocean service digital management system based on electronic commerce data analysis | |
| CN113129530B (en) | Intelligent security electronic fence alarm system based on Internet of things and machine vision | |
| TWI629654B (en) | Wisdom door city welcome product recommendation system and method thereof | |
| CN111353389B (en) | Intelligent weighing device based on multi-sensor recognition and analysis method thereof | |
| CN115496441A (en) | Raw material warehouse entry and exit management system applied to die cutting workshop | |
| CN112772449B (en) | Automatic monitoring, analyzing and managing system for activity intensity for feeding miniature pigs | |
| CN115222339B (en) | Cloud warehouse data analysis system and method based on cloud logistics environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |