CN116485378B - Renewable resource intelligent recovery data management system and method based on Internet of things - Google Patents
Renewable resource intelligent recovery data management system and method based on Internet of things Download PDFInfo
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Abstract
The application relates to the technical field of intelligent recycling of renewable resources, in particular to an intelligent recycling data management system and method of renewable resources based on the Internet of things, wherein the system comprises a platform data extraction module, a first behavior index analysis module, a first sequence pair construction module, an anomaly analysis module, a self-adaptive retention time length analysis module and an early warning response module; the platform data extraction module is used for acquiring user recovery data and corresponding scene traceability data when a recovery event is executed; the first behavioral index analysis module is used for analyzing a first behavioral index of the renewable resource intelligent recycling platform relative to a target execution event; the first sequence pair construction module is used for constructing a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data; the abnormality analysis module is used for judging whether the recovery event to be examined is abnormal or not; the self-adaptive holding time length analysis module is used for analyzing the self-adaptive holding time length of the renewable resource intelligent recycling machine in the monitoring period.
Description
Technical Field
The application relates to the technical field of intelligent recycling of renewable resources, in particular to an intelligent recycling data management system and method of renewable resources based on the Internet of things.
Background
Along with the rapid development of economy in China, environmental protection is more and more important for people, garbage classification recycling starts to advance into our lives in recent years, and various intelligent recycling machines appear at corners of various communities; the popularization of the intelligent recycling machine makes the awareness of people on garbage classification stronger, and the intelligent recycling machine establishes the association with the user through the medium of application programs of different garbage throwing modes and corresponding recycling prices, so that the user is willing to recycle renewable resources in the home by using the intelligent recycling machine; however, at the same time, some users use askew thinking to improve the weighing weight of the renewable resources to cause unequal actual weight and price, so that the intelligent recycling machine merchant end causes certain loss; meanwhile, in the recycling process of the renewable resources, the user can carry too many articles once, so that the originally set opening keeping time of the intelligent recycling machine cannot meet the requirement that the user delivers all the renewable resource articles, the user needs to log in for many times to verify and select delivery types, the delivery efficiency is reduced, the user experience of the intelligent recycling machine is easy to be poor, and the development of the intelligent recycling technology is not facilitated.
Disclosure of Invention
The application aims to provide an intelligent renewable resource recovery data management system and method based on the Internet of things, so as to solve the problems in the background technology.
In order to solve the technical problems, the application provides the following technical scheme: the renewable resource intelligent recovery data management method based on the Internet of things comprises the following analysis steps:
step S1: acquiring user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene tracing data when a recovery event is executed; the user recovery data comprises a user ID, the time when the user executes the recovery event and the recovery weight of the renewable resources in the event; the scene tracing data refer to monitoring data recorded when a recycling event is executed and sensing data in the intelligent recycling machine of the renewable resources, and the sensing data refer to humidity sensor data;
step S2: extracting and recording an execution recovery event corresponding to the difference value of the humidity sensor data and the environmental humidity data which is smaller than a first difference value threshold value as a target execution event, and analyzing a first behavior index of the renewable resource intelligent recovery platform relative to the target execution event; forming a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data;
step S3: based on the first behavior index, extracting recovery executing events corresponding to the first sequence pair containing similar specifications and when the humidity sensor data difference value is larger than a first difference value threshold value from the monitoring data as recovery events to be inspected, and extracting recovery data to be inspected and monitoring data to be inspected corresponding to the recovery events to be inspected; judging whether the recovery event to be examined is abnormal or not, outputting an early warning signal and analyzing an abnormal behavior index interval conforming to the recovery event to be examined under the abnormal condition;
step S4: extracting initial time and ending time of executing the recovery event by the user, and outputting a monitoring period corresponding to each user, wherein the monitoring period is the time from the ending time to the initial time; screening out execution recovery events belonging to the user corresponding to the abnormal behavior index interval; analyzing the self-adaptive holding time of the intelligent recycling machine of the renewable resources in the monitoring period; and based on the self-adaptive holding time length, performing time length self-adaptive early warning on the user using the renewable resource intelligent recycling machine in real time.
Further, in step S2, the analyzing the first behavioral index of the renewable resource intelligent recycling platform with respect to the target execution event includes the following analysis steps:
step S21: acquiring recovery weight w of the renewable resources corresponding to the target execution event, extracting a minimum value minw and a maximum value maxw of the recovery weight recorded by the renewable resource intelligent recovery platform, obtaining a recovery weight interval I, i=maxw=minw, and dividing the recovery weight interval into m recovery weight quantitative intervals in equal proportion;
step S22: extracting the delivery times p of the ith recovery weight quantitative section corresponding to the monitoring data record under each recovery weight i And total delivery duration t i The delivery times refer to the total times of delivering the renewable resources into the renewable resource intelligent recycling machine when a user executes a recycling event once, and the total delivery duration refers to the duration from the first delivery to the last delivery interval recorded by the user in the process of executing the recycling event once; using the formula:
U i ＝k 1 *w i0 +k 2 *p i0 +k 3 *t i0
calculating a first behavioral index U of the target execution event corresponding to the ith recovery weight quantitative section i ；i≤m；
Wherein w is i0 Indicating the i-th recovery weight quantitative section, recording the average recovery weight corresponding to all recovery weights; p is p i0 Mean value of delivery times per recovery weight recorded in ith recovery weight quantification section, t i0 Indicating the average value of the total delivery duration under each recovery weight recorded in the ith recovery weight quantification interval;
w i0 ＝(1/n)(∑w i )，p i0 ＝(1/n)(∑p i )，t i0 ＝(1/n)(∑t i ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein wi represents the recovery weight recorded in the ith recovery weight quantification interval, and n represents the number of target execution events contained in the ith recovery weight quantification interval; k (k) 1 Represents the reference coefficient, k, corresponding to the recovery weight 2 Representing the reference coefficient, k, corresponding to the number of delivery times 3 And representing the reference coefficient corresponding to the total delivery duration.
According to objective rules, the greater the recovery weight of the renewable resources delivered by the user is, the corresponding delivery times and time length are correspondingly increased, and the weight is divided into intervals, so that the direct calling and the ordered analysis of the judgment type to which the real-time recovery weight belongs are facilitated.
Further, in step S2, forming the first sequence pair from the first behavior index and the regenerated resource specification data under the corresponding monitoring data includes the following analysis steps:
step S21: acquiring image data recorded by corresponding monitoring data of each recycling weight quantitative section, wherein the image data refers to an image picture captured by a user when delivery actions are executed and the renewable resources do not enter the renewable resource intelligent recycling machine; calculating the image proportion Q of the renewable resources of each image data in a primary target execution event of a user, wherein Q=g/G, G represents the size of the area in the image data corresponding to the renewable resources, and G represents the size of the captured image;
step S22: using the formula:
Y i ＝(1/n)[∑(Q i1 +Q i2 +...+Q ip )]
calculating the regenerated resource specification data Y of the ith recovery weight quantitative section i ，Q i1 +Q i2 +...+Q ip Representing the sum of the image proportions of the 1 st, 2 nd, p th renewable resources corresponding to the target execution event in the ith recycling weight quantification interval;
step S23: regenerated resource specification data Y of ith recovered weight quantitative section i And a first behavioral index U i Forming a first sequence pair A i ，A i ＝{U i ,Y i }。
Further, step S3 includes the following analysis steps:
the specification similarity refers to the regenerated resource specification data corresponding to the execution recovery event, and belongs to a specification interval consisting of minimum values and maximum values corresponding to the regenerated resource specification data containing all target execution events in the recovery weight quantitative interval in the first sequence pair; the analysis specification is that the analysis specification is similar in that the analysis specification is to control variables to analyze differences because there is no difference in appearance after the renewable resources such as paper shells are injected with water, and the place showing the difference is on the obtained recovery weight;
the humidity sensor data difference value refers to a humidity sensor data difference value between a target execution event and an execution recovery event which are similar to the first sequence pair and contain specifications;
acquiring image data recorded by monitoring data corresponding to a recovery event to be inspected, and calculating regenerated resource specification data U, U=Q of the recovery event to be inspected 1 +Q 2 +...+Q p ，Q 1 、Q 2 、...、Q p Representing the proportion of the images of the p renewable resources corresponding to the 1 st, 2 nd;
extracting recovery weight, delivery times and total delivery duration contained in recovery data to be inspected and monitoring data to be inspected, and calculating a first behavior index Y corresponding to a recovery event to be inspected;
when the recovery event to be examined contained in the same renewable resource specification data U is unique, outputting Y to be greater than or equal to Y i The time abnormality behavior index interval is [ Y ] i ,Y]；
When the recovery event to be inspected contained in the same renewable resource specification data U is not unique, outputting an abnormal behavior index interval as [ minY, maxY ], wherein minY represents the minimum value of the first behavior index corresponding to the recovery event to be inspected, and maxY represents the maximum value of the first behavior index corresponding to the recovery event to be inspected.
The analysis of the abnormal behavior index section is to analyze the quantitative difference of the recovery event executed by the corresponding user under the same index evaluation due to the abnormal humidity, and the abnormal humidity in the application indicates that the user may have water injection to increase weight when the user puts in the renewable resources, and the humidity sensor is arranged in the box body and is used for identifying the data of the abnormal humidity, and when the abnormality exists, an early warning signal is output to the user side to remind the user to prohibit the abnormal recovery.
Further, step S4 includes the following analysis steps:
the initial time is the corresponding moment when the user successfully logs in the renewable resource intelligent recycling platform;
the ending time is the time corresponding to the completion of delivering the regenerated resources by the user in the monitoring data;
marking and screening out the event after the recovery event is executed corresponding to the abnormal behavior index interval as an effective recovery event; extraction of the ith first sequence pair A i The number of times b of the effective recovery event is recorded i The opening times are the times of opening the delivery port of the intelligent recycling machine for the renewable resources in a monitoring period; calculating a deviation index Fi of the i-th first sequence pair Ai,
F i ＝[max(b i )-min(b i )]/b i0
max(b i ) Represents the maximum value of the number of times the ith first sequence pair is opened corresponding to the record valid recycling event, min (b i ) Representing the minimum value of the corresponding opening times of the ith first sequence pair record effective recycling event; b i0 Representing the average number of times the ith first sequence pair records a valid recovery event;
setting a deviation index threshold F 0 Extracting F i <F 0 The corresponding first sequence pair obtains a keeping opening time h set by the renewable resource intelligent recycling platform; calculating an adaptive hold time H for a first sequence pair 1 ，H 1 ＝h*max(b i ) The method comprises the steps of carrying out a first treatment on the surface of the The maximum value is taken as the product factor of the self-adaptive holding time length because the fact that whether the maximum value or the minimum value belongs to a reasonable condition is indicated under the condition of small deviation, and the maximum value is taken as the holding opening time length, so that the trouble caused by repeated operation opening of a user can be avoided;
extraction of F i ≥F 0 A corresponding first sequence pair, calculating the self-adaptive holding time length H of the first sequence pair 2 ，H 2 ＝h*b i ’ The method comprises the steps of carrying out a first treatment on the surface of the Wherein b i ’ Represents the ith first sequence pair A i The number of times b of the effective recovery event is recorded i Mode of (d);
F i ≥F 0 indicating that the number of times of opening in the first sequence pair is greatly changed, the reason for the difference may be that the delivery efficiency is reduced due to the fact that the user is older and acts slowly, or other actions affecting the delivery duration, and meanwhile, the mode is taken as a product factor of the self-adaptive retention duration to indicate that the delivery of the equivalent recovered articles can be realized in most cases;
and acquiring real-time renewable resource specification data in the execution recycling event, extracting a first sequence pair matched with the real-time renewable resource specification data as a target sequence pair, and acquiring the self-adaptive retention time length recorded by the target sequence pair for early warning. The analysis of the self-adaptive holding time length is to avoid repeated restarting delivery caused by excessive carrying of the renewable resources by a user and singleization of the set holding time length of the intelligent recycling machine, so that the recycling step becomes complicated and the development of the intelligent recycling technology is not facilitated.
The renewable resource intelligent recovery data management system comprises a platform data extraction module, a first behavior index analysis module, a first sequence pair construction module, an anomaly analysis module, a self-adaptive holding duration analysis module and an early warning response module;
the platform data extraction module is used for acquiring user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene tracing data when a recovery event is executed;
the first behavioral index analysis module is used for analyzing a first behavioral index of the renewable resource intelligent recycling platform relative to a target execution event;
the first sequence pair construction module is used for constructing a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data;
the abnormality analysis module is used for judging whether the recovery event to be examined is abnormal or not;
the self-adaptive holding time length analysis module is used for analyzing the self-adaptive holding time length of the renewable resource intelligent recycling machine in the monitoring period;
the early warning response module is used for responding to the output result of the abnormality analysis module and responding to the corresponding result of the self-adaptive holding duration analysis module.
Further, the first behavioral index analysis module comprises a recovery weight quantitative interval dividing unit, an interval data acquisition unit and a first behavioral index calculation unit;
the recovery weight quantitative section dividing unit is used for dividing the recovery weight section into recovery weight quantitative sections in equal proportion;
the interval data acquisition unit is used for extracting the delivery times, the total delivery duration and the average recovery weight of each recovery weight of the corresponding monitoring data record of the recovery weight quantitative interval;
the first behavior index calculation unit is used for calculating a first behavior index based on the data of the interval data acquisition unit.
Further, the abnormality analysis module comprises a recovery event to be inspected determining unit, an index comparing unit and an abnormal behavior index interval determining unit;
the recovery event to be inspected determining unit is used for extracting recovery events which are similar to the first sequence pair in specification and correspond to the first sequence pair in the monitoring data and are executed when the difference value of the humidity sensor data is larger than a first difference value threshold value as recovery events to be inspected;
the index comparison unit is used for calculating a first behavior index corresponding to the recovery event to be examined and comparing the first behavior index in the same regenerated resource specification data;
the abnormal behavior index section determining unit outputs an abnormal behavior index section corresponding to the result of the index comparing unit.
Further, the self-adaptive holding time length analysis module comprises an event screening unit, a deviation index calculation unit, a sequence pair matching unit and a self-adaptive holding time length calculation unit;
the event screening unit is used for marking and screening out the event which belongs to the abnormal behavior index interval and corresponds to the event which is subjected to the recovery execution and is effective recovery event;
the deviation index calculation unit is used for calculating a deviation index in the first sequence pair;
the sequence matching unit is used for setting a deviation index threshold value, matching a first sequence pair corresponding to the deviation index threshold value with the deviation index smaller than the deviation index threshold value, and matching a first sequence pair corresponding to the deviation index threshold value with the deviation index larger than or equal to the deviation index threshold value;
the self-adaptive holding time length calculation unit is used for calculating the self-adaptive holding time length based on the first sequence pair and the holding start time length set by the renewable resource intelligent recycling platform.
Compared with the prior art, the application has the following beneficial effects: the application analyzes user behavior data and resource specification data under reasonable delivery conditions by extracting user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene traceability data when a recovery event is executed; based on the analysis, analyzing the abnormal event recorded by the platform, screening and early warning, thereby obtaining the execution recovery event meeting the analysis requirement, ensuring that the acquired data is not affected by the abnormality, and reasonably avoiding excessive loss caused by the intelligent recovery machine merchant terminal; in addition, the self-adaptive maintaining time length of the intelligent recycling machine for the renewable resources is intelligently adjusted based on the user delivery times recorded in the monitoring period; the method has the advantages that the problems that the renewable resources are excessively carried by a user once and the set and kept time of the intelligent recycling machine is single are avoided, and delivery is restarted for a plurality of times, so that the recycling step becomes simple and intelligent, and the development of the intelligent recycling technology is facilitated.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent renewable resource recycling data management system based on the internet of things.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the present application provides the following technical solutions: the renewable resource intelligent recovery data management method based on the Internet of things comprises the following analysis steps:
step S1: acquiring user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene tracing data when a recovery event is executed; the user recovery data comprises a user ID, the time when the user executes the recovery event and the recovery weight of the renewable resources in the event; the scene tracing data refer to monitoring data recorded when a recycling event is executed and sensing data in the intelligent recycling machine of the renewable resources, and the sensing data refer to humidity sensor data; the humidity sensor data obtain the humidity data in the renewable resource intelligent recycling machine; the recovery weight data is obtained by calculating the sum of a weight recording sensor and a counter which are arranged in the renewable resource intelligent recovery platform; the user ID can be acquired by verifying and logging in through a portrait identification sensor arranged in the intelligent recovery platform, and ID information is acquired; the intelligent recycling platform is also provided with a distance sensor for sensing the garbage loading degree in the recycling machine, and the data can be displayed and operated on the intelligent recycling machine through the comprehensive information display panel.
Step S2: extracting and recording an execution recovery event corresponding to the difference value of the humidity sensor data and the environmental humidity data which is smaller than a first difference value threshold value as a target execution event, and analyzing a first behavior index of the renewable resource intelligent recovery platform relative to the target execution event; forming a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data; when the difference between the humidity sensor and the environment humidity is larger, the user is informed to add moisture when delivering the renewable resources for recycling so as to increase the weight of the renewable resources; a difference value smaller than the first difference threshold value indicates that the recycling event performed in this case does not have the problem that the weight of the renewable resources does not coincide with the actual weight due to the water injection treatment of the renewable resources by the user;
step S3: based on the first behavior index, extracting recovery executing events corresponding to the first sequence pair containing similar specifications and when the humidity sensor data difference value is larger than a first difference value threshold value from the monitoring data as recovery events to be inspected, and extracting recovery data to be inspected and monitoring data to be inspected corresponding to the recovery events to be inspected; judging whether the recovery event to be examined is abnormal or not, outputting an early warning signal and analyzing an abnormal behavior index interval conforming to the recovery event to be examined under the abnormal condition;
step S4: extracting initial time and ending time of executing the recovery event by the user, and outputting a monitoring period corresponding to each user, wherein the monitoring period is the time from the ending time to the initial time; screening out execution recovery events belonging to the user corresponding to the abnormal behavior index interval; analyzing the self-adaptive holding time of the intelligent recycling machine of the renewable resources in the monitoring period; and based on the self-adaptive holding time length, performing time length self-adaptive early warning on the user using the renewable resource intelligent recycling machine in real time.
In step S2, the analyzing the first behavioral index of the renewable resource intelligent recycling platform with respect to the target execution event includes the following analysis steps:
step S21: acquiring recovery weight w of the renewable resources corresponding to the target execution event, extracting a minimum value minw and a maximum value maxw of the recovery weight recorded by the renewable resource intelligent recovery platform, obtaining a recovery weight interval I, i=maxw=minw, and dividing the recovery weight interval into m recovery weight quantitative intervals in equal proportion; in practice, dividing according to requirements;
step S22: extracting the delivery times p of the ith recovery weight quantitative section corresponding to the monitoring data record under each recovery weight i And total delivery duration t i The delivery times refer to the total times of delivering the renewable resources into the renewable resource intelligent recycling machine when a user executes a recycling event once, and the total delivery duration refers to the duration from the first delivery to the last delivery interval recorded by the user in the process of executing the recycling event once; using the formula:
U i ＝k 1 *w i0 +k 2 *p i0 +k 3 *t i0
calculating a first behavior of the target execution event corresponding to the ith recovery weight quantification intervalIndex U i ；i≤m；
Wherein w is i0 Indicating the i-th recovery weight quantitative section, recording the average recovery weight corresponding to all recovery weights; p is p i0 Mean value of delivery times per recovery weight recorded in ith recovery weight quantification section, t i0 Indicating the average value of the total delivery duration under each recovery weight recorded in the ith recovery weight quantification interval;
w i0 ＝(1/n)(∑w i )，p i0 ＝(1/n)(∑p i )，t i0 ＝(1/n)(∑t i ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein wi represents the recovery weight recorded in the ith recovery weight quantification interval, and n represents the number of target execution events contained in the ith recovery weight quantification interval; k (k) 1 Represents the reference coefficient, k, corresponding to the recovery weight 2 Representing the reference coefficient, k, corresponding to the number of delivery times 3 And representing the reference coefficient corresponding to the total delivery duration.
According to objective rules, the greater the recovery weight of the renewable resources delivered by the user is, the corresponding delivery times and time length are correspondingly increased, and the weight is divided into intervals, so that the direct calling and the ordered analysis of the judgment type to which the real-time recovery weight belongs are facilitated.
In step S2, forming the first performance index and the regenerated resource specification data under the corresponding monitoring data into the first sequence pair includes the following analysis steps:
step S21: acquiring image data recorded by corresponding monitoring data of each recycling weight quantitative section, wherein the image data refers to an image picture captured by a user when delivery actions are executed and the renewable resources do not enter the renewable resource intelligent recycling machine; calculating the image proportion Q of the renewable resources of each image data in a primary target execution event of a user, wherein Q=g/G, G represents the size of the area in the image data corresponding to the renewable resources, and G represents the size of the captured image;
step S22: using the formula:
Y i ＝(1/n)[∑(Q i1 +Q i2 +...+Q ip )]
calculating the regenerated resource specification data Yi, Q of the ith recovery weight quantitative section i1 +Q i2 +...+Q ip Representing the sum of the image proportions of the 1 st, 2 nd, p th renewable resources corresponding to the target execution event in the ith recycling weight quantification interval;
as shown in the examples:
the recovery weight quantitative interval is [0.5kg,1.5kg ];
and the two target execution events exist in the interval as event 1 and event 2 respectively;
the number of delivery times is recorded in the event 1 to be 2 times, and the proportion of the images of the renewable resources in the image frames captured by each delivery is 0.14 and 0.19;
event 2 records 3 delivery times; the proportion of the images of the renewable resources in the image frames captured by each delivery is 0.16, 0.18 and 0.13;
the recycling resource specification data Y of the recycling weight quantification section,
Y＝(1/2)[(0.14+0.19)+(0.18+0.16+0.13)]＝0.4。
step S23: regenerated resource specification data Y of ith recovered weight quantitative section i And a first behavioral index U i Forming a first sequence pair A i ，A i ＝{U i ,Y i }。
Step S3 comprises the following analysis steps:
the specification similarity refers to the regenerated resource specification data corresponding to the execution recovery event, and belongs to a specification interval consisting of minimum values and maximum values corresponding to the regenerated resource specification data containing all target execution events in the recovery weight quantitative interval in the first sequence pair; the analysis specification is that the analysis specification is similar in that the analysis specification is to control variables to analyze differences because there is no difference in appearance after the renewable resources such as paper shells are injected with water, and the place showing the difference is on the obtained recovery weight;
the humidity sensor data difference value refers to a humidity sensor data difference value between a target execution event and an execution recovery event which are similar to the first sequence pair and contain specifications;
acquiring image data recorded by monitoring data corresponding to the recovery event to be inspected, and calculating a renewable resource rule of the recovery event to be inspectedLattice data U, u=q 1 +Q 2 +...+Q p ，Q 1 、Q 2 、...、Q p Representing the image proportion of the p renewable resources corresponding to the 1 st, 2 nd, the third and the fourth recycling events to be inspected, wherein the image proportion of the renewable resources is the same as the calculation mode;
extracting recovery weight, delivery times and total delivery duration contained in recovery data to be inspected and monitoring data to be inspected, and calculating a first behavior index Y corresponding to a recovery event to be inspected;
the first behavioral index corresponding to the recovery event to be inspected is the same as the first behavioral index calculated in the recovery weight quantitative interval, U i ＝k 1 *w i0 +k 2 *p i0 +k 3 *t i0 In the substitution process, w of recovery event to be examined i0 Represents the weight recovered once corresponding to the event, p i0 Representing sum of delivery times corresponding to the event, t i0 Representing the total delivery duration corresponding to the event;
when the recovery event to be examined contained in the same renewable resource specification data U is unique, outputting Y to be greater than or equal to Y i The time abnormality behavior index interval is [ Y ] i ,Y]；
When the recovery event to be inspected contained in the same renewable resource specification data U is not unique, outputting an abnormal behavior index interval as [ minY, maxY ], wherein minY represents the minimum value of the first behavior index corresponding to the recovery event to be inspected, and maxY represents the maximum value of the first behavior index corresponding to the recovery event to be inspected.
The analysis of the abnormal behavior index section is to analyze the quantitative difference of the recovery event executed by the corresponding user under the same index evaluation due to the abnormal humidity, and the abnormal humidity in the application indicates that the user may have water injection to increase weight when the user puts in the renewable resources, and the humidity sensor is arranged in the box body and is used for identifying the data of the abnormal humidity, and when the abnormality exists, an early warning signal is output to the user side to remind the user to prohibit the abnormal recovery.
Step S4 comprises the following analysis steps:
the initial time is the corresponding moment when the user successfully logs in the renewable resource intelligent recycling platform;
the ending time is the time corresponding to the completion of delivering the regenerated resources by the user in the monitoring data;
marking and screening out the event after the recovery event is executed corresponding to the abnormal behavior index interval as an effective recovery event; extraction of the ith first sequence pair A i The number of times b of the effective recovery event is recorded i The opening times are the times of opening the delivery port of the intelligent recycling machine for the renewable resources in a monitoring period; calculating a deviation index Fi of the i-th first sequence pair Ai,
F i ＝[max(b i )-min(b i )]/b i0
max(b i ) Represents the maximum value of the number of times the ith first sequence pair is opened corresponding to the record valid recycling event, min (b i ) Representing the minimum value of the corresponding opening times of the ith first sequence pair record effective recycling event; b i0 Representing the average number of times the ith first sequence pair records a valid recovery event;
setting a deviation index threshold F 0 Extracting F i <F 0 The corresponding first sequence pair obtains a keeping opening time h set by the renewable resource intelligent recycling platform; calculating an adaptive hold time H for a first sequence pair 1 ，H 1 ＝h*max(b i ) The method comprises the steps of carrying out a first treatment on the surface of the The maximum value is taken as the product factor of the self-adaptive holding time length because the fact that whether the maximum value or the minimum value belongs to a reasonable condition is indicated under the condition of small deviation, and the maximum value is taken as the holding opening time length, so that the trouble caused by repeated operation opening of a user can be avoided;
extraction of F i ≥F 0 A corresponding first sequence pair, calculating the self-adaptive holding time length H of the first sequence pair 2 ，H 2 ＝h*b i ’ The method comprises the steps of carrying out a first treatment on the surface of the Wherein b i ’ Represents the ith first sequence pair A i The number of times b of the effective recovery event is recorded i Mode of (d);
F i ≥F 0 indicating that the number of turns on in the first sequence pair varies greatly, the difference may be due to the older age of the userThe slow action causes the delivery efficiency to be reduced, or other actions affecting the delivery duration, and the mode is taken as the product factor of the self-adaptive retention duration, so that the delivery of the equivalent recovered articles can be realized in most cases;
and acquiring real-time renewable resource specification data in the execution recycling event, extracting a first sequence pair matched with the real-time renewable resource specification data as a target sequence pair, and acquiring the self-adaptive retention time length recorded by the target sequence pair for early warning. The real-time renewable resource specification data can be obtained by estimating renewable resource items carried by users through image data recorded by a renewable resource recycling platform, and the estimation mode is the same as the proportion calculation of the renewable resource images; the matching means that the real-time renewable resource specification data belongs to a specification section in the first sequence pair, the specification section being composed of a minimum value and a maximum value corresponding to the renewable resource specification data in which the recovery weight quantification section includes all the target execution events. The analysis of the self-adaptive holding time length is to avoid repeated restarting delivery caused by excessive carrying of the renewable resources by a user and singleization of the set holding time length of the intelligent recycling machine, so that the recycling step becomes complicated and the development of the intelligent recycling technology is not facilitated.
The renewable resource intelligent recovery data management system comprises a platform data extraction module, a first behavior index analysis module, a first sequence pair construction module, an anomaly analysis module, a self-adaptive holding duration analysis module and an early warning response module;
the platform data extraction module is used for acquiring user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene tracing data when a recovery event is executed;
the first behavioral index analysis module is used for analyzing a first behavioral index of the renewable resource intelligent recycling platform relative to a target execution event;
the first sequence pair construction module is used for constructing a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data;
the abnormality analysis module is used for judging whether the recovery event to be examined is abnormal or not;
the self-adaptive holding time length analysis module is used for analyzing the self-adaptive holding time length of the renewable resource intelligent recycling machine in the monitoring period;
the early warning response module is used for responding to the output result of the abnormality analysis module and responding to the corresponding result of the self-adaptive holding duration analysis module.
The first behavioral index analysis module comprises a recovery weight quantitative interval dividing unit, an interval data acquisition unit and a first behavioral index calculation unit;
the recovery weight quantitative section dividing unit is used for dividing the recovery weight section into recovery weight quantitative sections in equal proportion;
the interval data acquisition unit is used for extracting the delivery times, the total delivery duration and the average recovery weight of each recovery weight of the corresponding monitoring data record of the recovery weight quantitative interval;
the first behavior index calculation unit is used for calculating a first behavior index based on the data of the interval data acquisition unit.
The abnormality analysis module comprises a recovery event determination unit to be examined, an index comparison unit and an abnormal behavior index interval determination unit;
the recovery event to be inspected determining unit is used for extracting recovery events which are similar to the first sequence pair in specification and correspond to the first sequence pair in the monitoring data and are executed when the difference value of the humidity sensor data is larger than a first difference value threshold value as recovery events to be inspected;
the index comparison unit is used for calculating a first behavior index corresponding to the recovery event to be examined and comparing the first behavior index in the same regenerated resource specification data;
the abnormal behavior index section determining unit outputs an abnormal behavior index section corresponding to the result of the index comparing unit.
The self-adaptive holding time length analysis module comprises an event screening unit, a deviation index calculation unit, a sequence pair matching unit and a self-adaptive holding time length calculation unit;
the event screening unit is used for marking and screening out the event which belongs to the abnormal behavior index interval and corresponds to the event which is subjected to the recovery execution and is effective recovery event;
the deviation index calculation unit is used for calculating a deviation index in the first sequence pair;
the sequence matching unit is used for setting a deviation index threshold value, matching a first sequence pair corresponding to the deviation index threshold value with the deviation index smaller than the deviation index threshold value, and matching a first sequence pair corresponding to the deviation index threshold value with the deviation index larger than or equal to the deviation index threshold value;
the self-adaptive holding time length calculation unit is used for calculating the self-adaptive holding time length based on the first sequence pair and the holding start time length set by the renewable resource intelligent recycling platform.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present application has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. The renewable resource intelligent recovery data management method based on the Internet of things is characterized by comprising the following analysis steps of:
step S1: acquiring user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene tracing data when a recovery event is executed; the user recycling data comprises a user ID, the time of a user executing recycling event and the recycling weight of the renewable resources in the event; the scene traceability data are monitoring data recorded when a recycling event is executed and sensing data in the intelligent recycling machine of the renewable resources, and the sensing data are humidity sensor data;
step S2: extracting and recording an execution recovery event corresponding to the difference value of the humidity sensor data and the environmental humidity data which is smaller than a first difference value threshold value as a target execution event, and analyzing a first behavior index of the renewable resource intelligent recovery platform relative to the target execution event; forming a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data;
step S3: based on the first behavior index, extracting recovery executing events corresponding to the first sequence pair containing similar specifications and when the humidity sensor data difference value is larger than a first difference value threshold value from the monitoring data as recovery events to be inspected, and extracting recovery data to be inspected and monitoring data to be inspected corresponding to the recovery events to be inspected; judging whether the recovery event to be examined is abnormal or not, outputting an early warning signal and analyzing an abnormal behavior index interval conforming to the recovery event to be examined under the abnormal condition;
step S4: extracting initial time and ending time of executing the recovery event by the user, and outputting a monitoring period corresponding to each user, wherein the monitoring period is the time length from the ending time to the initial time; screening out execution recovery events belonging to the user corresponding to the abnormal behavior index interval; analyzing the self-adaptive holding time of the intelligent recycling machine of the renewable resources in the monitoring period; and based on the self-adaptive holding time length, performing time length self-adaptive early warning on the user using the renewable resource intelligent recycling machine in real time.
2. The method for managing renewable resource intelligent recovery data based on the internet of things according to claim 1, wherein the method is characterized by comprising the following steps: the step S2 of analyzing the first behavioral index of the renewable resource intelligent recycling platform with respect to the target execution event comprises the following analysis steps:
step S21: acquiring recovery weight w of the renewable resources corresponding to the target execution event, extracting a minimum value minw and a maximum value maxw of the recovery weight recorded by the renewable resource intelligent recovery platform, obtaining a recovery weight interval I, i=maxw=minw, and dividing the recovery weight interval into m recovery weight quantitative intervals in equal proportion;
step S22: extracting the delivery times p of the ith recovery weight quantitative section corresponding to the monitoring data record under each recovery weight i And total delivery duration t i The delivery times refer to the total times of delivering the renewable resources into the intelligent renewable resource recycling machine when a user executes a recycling event once, and the total delivery duration refers to the duration from the first delivery to the last delivery interval recorded in the process of executing the recycling event once; using the formula:
U i ＝k 1 *w i0 +k 2 *p i0 +k 3 *t i0
calculating a first behavioral index U of the target execution event corresponding to the ith recovery weight quantitative section i ；i≤m；
Wherein w is i0 Indicating the i-th recovery weight quantitative section, recording the average recovery weight corresponding to all recovery weights; p is p i0 Mean value of delivery times per recovery weight recorded in ith recovery weight quantification section, t i0 Indicating the average value of the total delivery duration under each recovery weight recorded in the ith recovery weight quantification interval;
w i0 ＝(1/n)(∑w i )，p i0 ＝(1/n)(∑p i )，t i0 ＝(1/n)(∑t i ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein wi represents the recovery weight recorded in the ith recovery weight quantification interval, and n represents the number of target execution events contained in the ith recovery weight quantification interval; k (k) 1 Represents the reference coefficient, k, corresponding to the recovery weight 2 Representing the reference coefficient, k, corresponding to the number of delivery times 3 And representing the reference coefficient corresponding to the total delivery duration.
3. The method for managing the renewable resource intelligent recovery data based on the internet of things according to claim 2, wherein the method is characterized by comprising the following steps of: in the step S2, the forming the first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data includes the following analysis steps:
step S21: acquiring image data recorded by corresponding monitoring data of each recycling weight quantitative section, wherein the image data refers to an image picture captured by a user when delivery action is executed and renewable resources do not enter the renewable resource intelligent recycling machine; calculating the image proportion Q of the renewable resources of each image data in a primary target execution event of a user, wherein Q=g/G, G represents the size of the area in the image data corresponding to the renewable resources, and G represents the size of the captured image;
step S22: using the formula:
Y i ＝(1/n)[∑(Q i1 +Q i2 +...+Q ip )]
calculating the regenerated resource specification data Y of the ith recovery weight quantitative section i ，Q i1 +Q i2 +...+Q ip Representing the sum of the image proportions of the 1 st, 2 nd, p th renewable resources corresponding to the target execution event in the ith recycling weight quantification interval;
step S23: regenerated resource specification data Y of ith recovered weight quantitative section i And a first behavioral index U i Forming a first sequence pair A i ，A i ＝{U i ,Y i }。
4. The method for intelligently recovering and managing data of renewable resources based on the internet of things according to claim 3, wherein the method is characterized by comprising the following steps of: the step S3 includes the following analysis steps:
the specification similarity refers to the regenerated resource specification data corresponding to the execution recovery event, and belongs to a specification interval in the first sequence pair, wherein the specification interval comprises minimum values and maximum values corresponding to the regenerated resource specification data of all target execution events;
the humidity sensor data difference value refers to a humidity sensor data difference value between a target execution event and an execution recovery event which are similar to the first sequence pair containing specification;
acquiring image data recorded by monitoring data corresponding to a recovery event to be inspected, and calculating regenerated resource specification data U, U=Q of the recovery event to be inspected 1 +Q 2 +...+Q p ，Q 1 、Q 2 、...、Q p Indicating that the recovery event to be inspected corresponds to the 1 st, the 2 nd image scale of p renewable resources,
extracting recovery weight, delivery times and total delivery duration contained in recovery data to be inspected and monitoring data to be inspected, and calculating a first behavior index Y corresponding to a recovery event to be inspected;
when the recovery event to be examined contained in the same renewable resource specification data U is unique, outputting Y to be equal to or greater than Yi, wherein the abnormal behavior index interval is [ Y ] i ,Y]；
When the recovery event to be inspected contained in the same renewable resource specification data U is not unique, outputting an abnormal behavior index interval as [ minY, maxY ], wherein minY represents the minimum value of the first behavior index corresponding to the recovery event to be inspected, and maxY represents the maximum value of the first behavior index corresponding to the recovery event to be inspected.
5. The method for intelligently recovering data based on renewable resources by using the internet of things according to claim 4, which is characterized by comprising the following steps: the step S4 includes the following analysis steps:
the initial time is the corresponding moment when the user successfully logs in the renewable resource intelligent recycling platform;
the ending time refers to the moment corresponding to the completion of delivering the regenerated resources by the user in the monitoring data;
marking and screening out the event after the recovery event is executed corresponding to the abnormal behavior index interval as an effective recovery event; extraction of the ith first sequence pair A i The number of times b of the effective recovery event is recorded i The opening times are the times of opening the delivery port of the intelligent recycling machine for the renewable resources in a monitoring period; calculating the deviation index F of Ai in the ith first sequence pair i ，
F i ＝[max(b i )-min(b i )]/b i0
max(b i ) Represents the maximum value of the number of times the ith first sequence pair is opened corresponding to the record valid recycling event, min (b i ) Representing the corresponding number of times the ith first sequence pair records the active reclamation eventA minimum value; b i0 Representing the average number of times the ith first sequence pair records a valid recovery event;
setting a deviation index threshold F 0 Extracting F i <F 0 The corresponding first sequence pair obtains a keeping opening time h set by the renewable resource intelligent recycling platform; calculating an adaptive hold time H for a first sequence pair 1 ，H 1 ＝h*max(b i )；
Extraction of F i ≥F 0 A corresponding first sequence pair, calculating the self-adaptive holding time length H of the first sequence pair 2 ，H 2 ＝h*b i ’ The method comprises the steps of carrying out a first treatment on the surface of the Wherein b i ’ Represents the ith first sequence pair A i The number of times b of the effective recovery event is recorded i Mode of (d);
and acquiring real-time renewable resource specification data in the execution recycling event, extracting a first sequence pair matched with the real-time renewable resource specification data as a target sequence pair, and acquiring the self-adaptive retention time length recorded by the target sequence pair for early warning.
6. The renewable resource intelligent recycling data management system applying the renewable resource intelligent recycling data management method based on the Internet of things according to any one of claims 1 to 5 is characterized by comprising a platform data extraction module, a first behavioral index analysis module, a first sequence pair construction module, an anomaly analysis module, an adaptive retention time length analysis module and an early warning response module;
the platform data extraction module is used for acquiring user recovery data recorded by the renewable resource intelligent recovery platform and corresponding scene traceability data when a recovery event is executed;
the first behavioral index analysis module is used for analyzing a first behavioral index of the renewable resource intelligent recycling platform relative to a target execution event;
the first sequence pair construction module is used for constructing a first sequence pair by the first behavior index and the regenerated resource specification data under the corresponding monitoring data;
the abnormality analysis module is used for judging whether the recovery event to be examined is abnormal or not;
the self-adaptive holding time length analysis module is used for analyzing the self-adaptive holding time length of the renewable resource intelligent recycling machine in the monitoring period;
the early warning response module is used for responding to the output result of the abnormality analysis module and responding to the corresponding result of the self-adaptive holding duration analysis module.
7. The renewable resource intelligent reclamation data management system according to claim 6, wherein: the first behavioral index analysis module comprises a recovery weight quantitative interval dividing unit, an interval data acquisition unit and a first behavioral index calculation unit;
the recovery weight quantitative section dividing unit is used for dividing the equal proportion of the recovery weight section into recovery weight quantitative sections;
the interval data acquisition unit is used for extracting the delivery times, the total delivery duration and the average recovery weight of each recovery weight of the corresponding monitoring data record of the recovery weight quantitative interval;
the first behavioral index calculation unit is used for calculating a first behavioral index based on the data of the interval data acquisition unit.
8. The renewable resource intelligent reclamation data management system according to claim 7, wherein: the abnormality analysis module comprises a recovery event determination unit to be examined, an index comparison unit and an abnormal behavior index interval determination unit;
the recovery event to be examined determining unit is used for extracting recovery events to be examined, which are corresponding to the first sequence pair in the monitoring data and are similar to the first sequence pair in specification and when the difference value of the humidity sensor data is larger than a first difference value threshold value;
the index comparison unit is used for calculating a first behavior index corresponding to the recovery event to be examined and comparing the first behavior index in the same regenerated resource specification data;
the abnormal behavior index interval determining unit outputs an abnormal behavior index interval corresponding to the result of the index comparing unit.
9. The renewable resource intelligent reclamation data management system according to claim 8, wherein: the self-adaptive holding time length analysis module comprises an event screening unit, a deviation index calculation unit, a sequence pair matching unit and a self-adaptive holding time length calculation unit;
the event screening unit is used for marking and screening out the event which belongs to the abnormal behavior index interval and corresponds to the recovery event to be effective recovery event;
the deviation index calculation unit is used for calculating a deviation index in the first sequence pair;
the sequence pair matching unit is used for setting a deviation index threshold value, matching a first sequence pair corresponding to the deviation index threshold value, and enabling the deviation index to be larger than or equal to the first sequence pair corresponding to the deviation index threshold value;
the self-adaptive holding time length calculation unit is used for calculating the self-adaptive holding time length based on the first sequence pair and the holding start time length set by the renewable resource intelligent recycling platform.
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