CN118365191A - Product quality risk information assessment method based on public opinion big data - Google Patents
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Abstract
The invention relates to the technical field of public opinion monitoring, and particularly discloses a product quality risk information assessment method based on public opinion big data, which comprises the following steps: s1, acquiring product quality information of a network platform according to a preset time period, performing data cleaning, classifying data according to information attributes, and acquiring time-varying information of acquired data quantity of each type of product; s2, analyzing time-varying information of collected data volume of each type of product, wherein the analysis process comprises the following steps: carrying out first threshold early warning judgment on the data quantity of various products in each preset time period; performing second threshold early warning judgment according to time-varying information of the acquired data volume of each type of product; and carrying out change consistency comparison on the time-varying information and the historical change data according to the acquired data quantity of each type of product, and evaluating the public opinion state of the product quality risk information according to the comparison result.
Description
Technical Field
The invention relates to the technical field of public opinion monitoring, in particular to a product quality risk information assessment method based on public opinion big data.
Background
In the market supervision process, public opinion needs to be monitored, and in order to discover the possible quality safety risks of products in the use process in time, product quality risk information, injury cases, social product quality safety accidents or events and the like are monitored and captured in real time and in a full coverage way through information propagation channels related to product quality, such as network media, plane media, television news columns, self media and the like, and the product quality safety information is collected in an important way, wherein the product quality safety information comprises accident investigation, media exposure, consumption maintenance rights and the like; the method collects the safety risk conditions of the product quality found by the supervision of the production and circulation fields and the announcement information reported by major defect product recall organizations or institutions around the world, market supervision at all levels, customs and other departments, so that the data of a network platform are required to be collected and analyzed to improve the timeliness and accuracy of relevant dynamic monitoring.
With the rapid development of self-media, public opinion transmission of a user side occupies a large proportion, and in the prior art, monitoring and judging are mainly carried out through the change trend of the attention degree and the exposure degree of different products in the process of monitoring and analyzing public opinion data of the user side, and marking is carried out when the attention degree or the exposure degree exceeds a certain value and the management personnel carry out manual judgment.
The existing monitoring mode can only be monitored when obvious risks appear in public opinion of products in the process of analysis and judgment, so that the existing public opinion monitoring management method has the problem of low sensitivity, and further a certain hysteresis exists in a judgment result.
Disclosure of Invention
The invention aims to provide a product quality risk information assessment method based on public opinion big data, which solves the following technical problems:
how to improve the sensitivity and timeliness of product quality risk monitoring.
The aim of the invention can be achieved by the following technical scheme:
A product quality risk information assessment method based on public opinion big data, the method comprising:
S1, acquiring product quality information of a network platform according to a preset time period, performing data cleaning, classifying data according to information attributes, and acquiring time-varying information of acquired data quantity of each type of product;
S2, analyzing time-varying information of collected data volume of each type of product, wherein the analysis process comprises the following steps:
Carrying out first threshold early warning judgment on the data quantity of various products in each preset time period;
performing second threshold early warning judgment according to time-varying information of the acquired data volume of each type of product;
and carrying out change consistency comparison on the time-varying information and the historical change data according to the acquired data quantity of each type of product, and evaluating the public opinion state of the product quality risk information according to the comparison result.
Further, the process of step S2 includes:
the first threshold early warning judging process comprises the following steps:
Comparing the data volume of each type of product in a preset time period with a first threshold value of the type of product:
If the data quantity exceeds a first threshold value, marking the product;
If the data quantity does not exceed the first threshold value, performing a second threshold value early warning judgment process:
The second threshold early warning judging process comprises the following steps:
Acquiring historical acquisition data of each type of product, and acquiring data quantity dispersion of each preset time period in the historical acquisition data;
Obtaining a change early warning value according to the increment of the current preset time period of each product type relative to the last preset time period, the time-varying information of the data quantity and the data quantity dispersion, and comparing a second early warning value with a second threshold value of the product type:
If the second early warning value exceeds a second threshold value, judging to mark the product;
and if the second early warning value does not exceed the first threshold value, carrying out change consistency comparison, and evaluating the public opinion state of the product quality risk information according to the comparison result.
Further, the calculating process of the change early warning value includes:
By the formula:
Calculating to obtain a change early warning value W of each type of product;
Wherein Q T is the acquired data quantity of the current period of each type of product, Q T-1 is the acquired data quantity of the previous period of each type of product, Q (t) is the time-varying curve of the acquired data quantity of each type of product, Representing the current periodMaximum value, x1 and x2 are preset proportional coefficients, s is a dispersion coefficient of each type of product, m is the number of periods selected by historical data of each type of product, i epsilon [1, m ], Q i is the acquired data quantity of the ith period,The data volume average value is acquired in m periods of each type of product, and f s is a correction coefficient-discrete comparison table function.
Further, the consistency comparison process includes:
acquiring a tag sequence and a corresponding data volume change curve of each period in the historical change data;
Acquiring a label of the current period, and screening with historical data to acquire a reference change curve;
And consistency comparison is carried out on the time-varying information of the acquired data quantity of each product type in the current period and the corresponding reference variation curve.
Further, the process of obtaining the reference change curve includes:
By the formula:
calculating and obtaining the matching degree co j of the label of the current period and the jth period in the historical data;
Wherein n j is the number of tags in the j-th period in the history data, k e [1, n j],wfjk is whether the k-th tag is overlapped with the tag in the current period, if so, wf jk =1, otherwise, wf jk＝0,αk is the weight value of the k-th tag, which is determined according to the sequence of the tags;
And comparing the matching degree co j with a preset critical value co T, selecting all time periods of co j≥coT and corresponding data volume change curves thereof, and calculating the average value to obtain a reference change curve Q T (t).
Further, the process of performing the consistency comparison includes:
By the formula:
Calculating to obtain a consistency coefficient r;
Wherein Z is the number of points selected according to fixed time intervals in the current period, x is [1, Z ], Q x is the value of Q (t) corresponding to the xth time point, For all time points, Q x mean, Q Tx is the x-th time point corresponding to the value of Q T (t),Mean value for all time points Q Tx;
Comparing the consistency coefficient r with a preset interval [ p, 1):
If r is E [ p,1], the product is not marked;
Otherwise, marking the products;
Wherein, p is a threshold adjustment coefficient and p is [0.85,0.95].
Further, the acquisition data volume acquisition process of each type of product comprises the following steps:
By the formula:
Calculating and obtaining the acquired data quantity Q (t) of each type of product in the current period;
wherein H is the source data amount of the current period, y E [1, H ], v y (t) is the number of disclosure of the y-th source data, p y (t) is the number of evaluation of the y-th source data, f X is a preset definition function, and is an increasing function.
Further, after the product category is marked, the marked category and the high-association information corresponding to the category are sent to an artificial checking background;
The high-association information is the source information corresponding to the maximum value of f X(vy(t),py (t)).
The invention has the beneficial effects that:
(1) According to the method, on the basis of conventional low-threshold early warning judgment on the collected data quantity of each period, the second threshold early warning judgment is carried out according to the time-varying information of the collected data quantity of each type of product, the process can judge the variation trend of the data quantity, and further the judgment timeliness is improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of steps of a product quality risk information evaluation method based on public opinion big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in one embodiment, a product quality risk information assessment method based on public opinion big data is provided, the method includes: s1, acquiring product quality information of a network platform according to a preset time period, performing data cleaning, classifying data according to information attributes, and acquiring time-varying information of acquired data quantity of each type of product; it should be noted that, the data collection and data cleaning process in step S1 is performed by a common means in the prior art, which is not described in detail herein; s2, analyzing time-varying information of collected data volume of each type of product, wherein the analysis process comprises the following steps: carrying out first threshold early warning judgment on the data quantity of various products in each preset time period; performing second threshold early warning judgment according to time-varying information of the acquired data volume of each type of product; according to the method, the collected data volume of each type of product is compared with the historical change data according to the change consistency, the public opinion state of the product quality risk information is estimated according to the comparison result, according to the scheme, the implementation can judge the change trend of the data volume according to the collected data volume of each type of product and further improve the judgment timeliness on the basis that the collected data volume of each type of product is conventionally lower than the threshold early warning judgment, meanwhile, the embodiment also carries out the change consistency comparison according to the collected data volume of each type of product and the historical change data, the public opinion state of the product quality risk information is estimated according to the comparison result, and the process can carry out relatively accurate evaluation analysis on the public opinion information state according to the characteristics of the change trend of the data volume and further improve the judgment sensitivity.
It should be noted that, the data volume in the above scheme may be set according to a data type, and the acquisition process of the acquired data volume of each type of product in this embodiment includes: by the formula:
Calculating and obtaining the acquired data quantity Q (t) of each type of product in the current period; wherein, H is the source data quantity of the current period, y is [1, H ], v y (t) is the number of times of disclosure of the y-th source data, p y (t) is the number of times of evaluation of the y-th source data, f X is a preset definition function, and is an increasing function, the preset definition function is obtained by setting corresponding rules according to empirical data, and the method is not limited herein, so that the acquired data quantity obtained through the above process can reflect the attention degree and the exposure degree condition of the same category information of the current period, and is convenient for accurately monitoring the integral public opinion.
The first threshold early warning judging process comprises the following steps: comparing the data volume of each type of product in a preset time period with a first threshold value of the type of product, wherein the first thresholds of different types are selected and set according to experience data, so that if the data volume exceeds the first threshold value, the type of product is marked; if the data quantity does not exceed the first threshold value, performing a second threshold value early warning judgment process: the second threshold early warning judging process comprises the following steps: acquiring historical acquisition data of each type of product, and acquiring data quantity dispersion of each preset time period in the historical acquisition data; obtaining a change early warning value according to the increment of the current preset time period of each type of product relative to the previous preset time period, the time-varying information of the data quantity and the data quantity dispersion, wherein the calculation process of the change early warning value comprises the following steps:
By the formula:
calculating to obtain a change early warning value W of each type of product; wherein Q T is the acquired data quantity of the current period of each type of product, Q T-1 is the acquired data quantity of the previous period of each type of product, Q (t) is the time-varying curve of the acquired data quantity of each type of product, Representing the current periodMaximum value, x1 and x2 are preset proportional coefficients, which are selected and set after simulation according to empirical data, s is a dispersion coefficient of each type of product, m is the number of periods selected by historical data of each type of product, i epsilon [1, m ], Q i is the acquired data quantity of the ith period,The method comprises the steps that a data quantity average value is acquired in m periods of each type of product, f s is a correction coefficient-discrete comparison table function, the correction coefficient-discrete comparison table function is set according to a plurality of groups of experience simulation data fitting result references, the change early warning value is set according to the change characteristics of data, meanwhile, the size of the change early warning value W is adjusted through the data discrete state in the experience data, and further deviation caused by different types of the data products can be reduced, so that the change abnormality of each type of product can be timely and accurately judged through the calculated change early warning value, further timely acquisition and judgment of related public opinion information of the type of product are facilitated for related personnel, the type of product is subjected to fitting setting according to the experience data through comparing a second early warning value with a second threshold of the type of product, and therefore, if the second early warning value exceeds the second threshold, the type of product is judged to be marked; if the second early warning value does not exceed the first threshold value, carrying out change consistency comparison, and evaluating the public opinion state of the product quality risk information according to the comparison result, wherein the specific process comprises the following steps: acquiring a tag sequence and a corresponding data volume change curve of each period in the historical change data; acquiring a label of the current period, and screening with historical data to acquire a reference change curve; consistency comparison is carried out on the time-varying information of the acquired data volume of each type of product in the current period and a corresponding reference change curve, wherein the process for acquiring the reference change curve comprises the following steps: by the formula:
Calculating and obtaining the matching degree co j of the label of the current period and the jth period in the historical data; wherein n j is the number of tags in the j-th period in the history data, k e [1, n j],wfjk is whether the k-th tag is overlapped with the tag in the current period, if so, wf jk =1, otherwise, wf jk＝0,αk is the weight value of the k-th tag, which is determined according to the sequence of the tags; it should be noted that, the sequence of the tag sequence is set according to the occurrence frequency of different tags, the matching degree co j is compared with the preset critical value co T, and the preset critical value co T is set according to the empirical data, so that all the time periods of co j≥coT and the corresponding data volume change curves are selected to obtain the reference change curve Q T (t), and the high sensitivity judgment process of public opinion can be realized by comparing the reference change curve Q T (t) with the acquired data volume change curve with time.
The process for consistency comparison comprises the following steps:
By the formula:
Calculating to obtain a consistency coefficient r; wherein Z is the number of points selected according to fixed time intervals in the current period, x is [1, Z ], Q x is the value of Q (t) corresponding to the xth time point, For all time points, Q x mean, Q Tx is the x-th time point corresponding to the value of Q T (t),For the average value of all time points Q Tx, the closer the obtained consistency coefficient r is to 1, the closer the obtained consistency coefficient r is to the history synchronization result, and the consistency coefficient r is compared with a preset interval [ p,1], wherein p is a threshold adjustment coefficient which is set according to empirical data and meets p epsilon [0.85,0.95], so that if r epsilon [ p,1], the products are not marked; otherwise, the product is marked. After the product category is marked, the marked category and the high-association information corresponding to the category are sent to an artificial checking background, wherein the high-association information is the source information corresponding to the maximum value of f X(vy(t),py (t)), so that judgment is carried out according to the data of monitoring and evaluation, a manager can conveniently and accurately judge the problem of the product quality risk in time, and the workload is greatly reduced.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (8)
1. The product quality risk information assessment method based on public opinion big data is characterized by comprising the following steps:
S1, acquiring product quality information of a network platform according to a preset time period, performing data cleaning, classifying data according to information attributes, and acquiring time-varying information of acquired data quantity of each type of product;
S2, analyzing time-varying information of collected data volume of each type of product, wherein the analysis process comprises the following steps:
Carrying out first threshold early warning judgment on the data quantity of various products in each preset time period;
performing second threshold early warning judgment according to time-varying information of the acquired data volume of each type of product;
and carrying out change consistency comparison on the time-varying information and the historical change data according to the acquired data quantity of each type of product, and evaluating the public opinion state of the product quality risk information according to the comparison result.
2. The product quality risk information evaluation method based on public opinion big data according to claim 1, wherein the process of step S2 comprises:
the first threshold early warning judging process comprises the following steps:
Comparing the data volume of each type of product in a preset time period with a first threshold value of the type of product:
If the data quantity exceeds a first threshold value, marking the product;
If the data quantity does not exceed the first threshold value, performing a second threshold value early warning judgment process:
The second threshold early warning judging process comprises the following steps:
Acquiring historical acquisition data of each type of product, and acquiring data quantity dispersion of each preset time period in the historical acquisition data;
Obtaining a change early warning value according to the increment of the current preset time period of each product type relative to the last preset time period, the time-varying information of the data quantity and the data quantity dispersion, and comparing a second early warning value with a second threshold value of the product type:
If the second early warning value exceeds a second threshold value, judging to mark the product;
and if the second early warning value does not exceed the first threshold value, carrying out change consistency comparison, and evaluating the public opinion state of the product quality risk information according to the comparison result.
3. The method for evaluating product quality risk information based on public opinion big data according to claim 2, wherein the calculating process of the change early warning value comprises:
By the formula:
Calculating to obtain a change early warning value W of each type of product;
Wherein Q T is the acquired data quantity of the current period of each type of product, Q T-1 is the acquired data quantity of the previous period of each type of product, Q (t) is the time-varying curve of the acquired data quantity of each type of product, Representing the current periodMaximum value, x1 and x2 are preset proportional coefficients, s is a dispersion coefficient of each type of product, m is the number of periods selected by historical data of each type of product, i epsilon [1, m ], Q i is the acquired data quantity of the ith period,The data volume average value is acquired in m periods of each type of product, and f s is a correction coefficient-discrete comparison table function.
4. The method for evaluating product quality risk information based on public opinion big data according to claim 3, wherein the consistency comparison process comprises:
acquiring a tag sequence and a corresponding data volume change curve of each period in the historical change data;
Acquiring a label of the current period, and screening with historical data to acquire a reference change curve;
And consistency comparison is carried out on the time-varying information of the acquired data quantity of each product type in the current period and the corresponding reference variation curve.
5. The method for evaluating product quality risk information based on public opinion big data according to claim 4, wherein the process of obtaining the reference change curve comprises the steps of:
By the formula:
calculating and obtaining the matching degree co j of the label of the current period and the jth period in the historical data;
Wherein n j is the number of tags in the j-th period in the history data, k e [1, n j],wfjk is whether the k-th tag is overlapped with the tag in the current period, if so, wf jk =1, otherwise, wf jk＝0,αk is the weight value of the k-th tag, which is determined according to the sequence of the tags;
And comparing the matching degree co j with a preset critical value co T, selecting all time periods of co j≥coT and corresponding data volume change curves thereof, and calculating the average value to obtain a reference change curve Q T (t).
6. The method for evaluating product quality risk information based on public opinion big data according to claim 5, wherein the process of performing consistency comparison comprises:
By the formula:
Calculating to obtain a consistency coefficient r;
Wherein Z is the number of points selected according to fixed time intervals in the current period, x is [1, Z ], Q x is the value of Q (t) corresponding to the xth time point, For all time points, Q x mean, Q Tx is the x-th time point corresponding to the value of Q T (t),Mean value for all time points Q Tx;
Comparing the consistency coefficient r with a preset interval [ p, 1):
If r is E [ p,1], the product is not marked;
Otherwise, marking the products;
Wherein, p is a threshold adjustment coefficient and p is [0.85,0.95].
7. The method for evaluating product quality risk information based on public opinion big data according to claim 6, wherein the acquisition process of the acquired data amount of each type of product comprises:
By the formula:
Calculating and obtaining the acquired data quantity Q (t) of each type of product in the current period;
wherein H is the source data amount of the current period, y E [1, H ], v y (t) is the number of disclosure of the y-th source data, p y (t) is the number of evaluation of the y-th source data, f X is a preset definition function, and is an increasing function.
8. The method for evaluating product quality risk information based on public opinion big data according to claim 7, wherein when the product category is marked, the marked category and the corresponding high-association information of the category are sent to an artificial check background;
The high-association information is the source information corresponding to the maximum value of f X(vy(t),py (t)).
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| CN119338358A (en) * | 2024-12-20 | 2025-01-21 | 张家口恒洋电器有限公司 | A method and device for data collection of automatic guided transport vehicles for mining |
| CN119338358B (en) * | 2024-12-20 | 2025-04-04 | 张家口恒洋电器有限公司 | A method and device for collecting data of automatic guided transport vehicle for mining |
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