CN111814518A - Garbage delivery monitoring method based on order grabbing mode and related products - Google Patents

Garbage delivery monitoring method based on order grabbing mode and related products Download PDF

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CN111814518A
CN111814518A CN201910290939.9A CN201910290939A CN111814518A CN 111814518 A CN111814518 A CN 111814518A CN 201910290939 A CN201910290939 A CN 201910290939A CN 111814518 A CN111814518 A CN 111814518A
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李涛
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Shenzhen Jiajia Classification Technology Co ltd
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Abstract

The embodiment of the application provides a garbage delivery monitoring method based on a list grabbing mode and a related product, wherein the method comprises the following steps: the method comprises the steps of acquiring a garbage delivery image shot by a delivery user in an appointed area in real time, analyzing the garbage delivery image, generating a delivery event, sending the delivery event to electronic equipment of a supervision user in the appointed area, determining a target supervision user who successfully performs order grabbing according to a preset order grabbing algorithm, acquiring a garbage delivery place reporting the delivery event to a server, pushing the garbage delivery place to the electronic equipment of the target supervision user, generating a supervision record according to the uploading state of supervision data of the target supervision user, and determining the score of each target supervision user in the target supervision user according to the supervision data and the supervision record of the target supervision user and based on a preset scoring algorithm.

Description

Garbage delivery monitoring method based on order grabbing mode and related products
Technical Field
The application relates to the technical field of garbage delivery, in particular to a garbage delivery monitoring method based on a bill grabbing mode and a related product.
Background
Due to the rapid growth of urban population, daily output of garbage is increased sharply, people are encouraged to pay more and more attention to garbage treatment fluid, in a community, garbage is generally thrown into a garbage station by environmental protection personnel or residents, generally, the garbage station or a garbage can be divided into recyclable matters or non-recyclable matters, and throwing personnel may throw errors in the process of throwing the garbage, in which case, if the garbage collecting personnel are hired to classify different garbage, personnel waste is caused, and the personnel throwing the garbage may throw the errors again, and labor force of addressees is increased.
Disclosure of Invention
The embodiment of the application provides a garbage delivery monitoring method based on a list grabbing mode and a related product, delivery behaviors of delivery users can be monitored by supervising users, garbage delivery awareness of users in a designated area is improved, and manual waste is avoided.
In a first aspect, an embodiment of the present application provides a method for monitoring spam delivery based on a list preemption mode, including:
acquiring R junk delivery images shot by at least one delivery user in a designated area in real time, wherein R is a positive integer;
analyzing the R garbage delivery images, determining the delivery behavior of the at least one delivery user, and generating R delivery events;
sending the R delivery events to electronic equipment of M supervising users in the designated area, wherein M is a positive integer;
determining S target supervisory users who successfully preempt the order according to a preset order preemption algorithm, wherein S is a positive integer less than or equal to M;
acquiring R garbage delivery places reporting the R delivery events to the server, and pushing the R garbage delivery places to the S target supervision users;
monitoring the uploading states of S pieces of supervision data of the S target supervision users within a preset time threshold interval;
generating S monitoring records according to the uploading states of S monitoring data of S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record;
and determining the score of each target supervising user in the S target supervising users based on a preset scoring algorithm according to the S supervising data and the S supervising records of the S target supervising users.
In a second aspect, an embodiment of the present application provides a garbage delivery monitoring device based on a list grabbing mode, including:
the system comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring at least one R junk delivery images which are shot aiming at least one delivery user in a specified area in real time;
the analysis unit is used for analyzing the R garbage delivery images, determining the delivery behavior of the at least one delivery user and generating R delivery events;
a sending unit, configured to send the R delivery events to electronic devices of M supervising users in the designated area;
the determining unit is used for determining S target supervision users who successfully preempt the order according to a preset order-preempting algorithm;
the acquisition unit is further configured to acquire R spam delivery sites that report the R delivery events to the server, and push the R spam delivery sites to the S target supervisory users;
the monitoring unit is used for monitoring the uploading state of the supervision data of the at least one S target supervision users within a preset time threshold interval;
the generation unit is used for generating at least one S monitoring records according to the uploading state of the monitoring data of the at least one S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record;
the determining unit is further configured to determine, based on a preset scoring algorithm, a score of each of the S target supervisory users according to the supervisory data of the S target supervisory users and the S supervisory records.
In a third aspect, an embodiment of the present application provides a server, including: a processor and a memory; and one or more programs stored in the memory and configured to be executed by the processor, the programs including instructions for some or all of the steps as described in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, where the computer program is used to make a computer execute some or all of the steps described in the first aspect of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product comprises a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has the following beneficial effects:
it can be seen that R garbage delivery images shot aiming at least one delivery user in an appointed area are obtained in real time, the R garbage delivery images are analyzed, delivery behaviors of the R delivery users are determined, R delivery events are generated, the R delivery events are sent to electronic equipment of M supervisory users in the appointed area, S target supervisory users who successfully preempt the order are determined according to a preset order preemption algorithm, R garbage delivery places which report the R delivery events to a server are obtained, the R garbage delivery places are pushed to the electronic equipment of the S target supervisory users, the uploading states of supervisory data of the S target supervisory users are monitored in a preset time threshold interval, S supervisory records are generated according to the uploading states of S supervisory data of the S target supervisory users, wherein the supervisory data uploaded by each target supervisory user corresponds to one supervisory record, and determining the score of each target supervisory user in the S target supervisory users based on a preset scoring algorithm according to the supervisory data and S supervisory records of the S target supervisory users, so that the supervisory users can be dispatched based on a list-robbing mode, the delivery behavior of delivery users is monitored, the garbage delivery consciousness of users in the designated area is improved, and the manual waste is avoided.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1A is a system architecture diagram of a spam delivery monitoring method based on a single robbing mode according to an embodiment of the present application;
fig. 1B is a schematic flowchart of an embodiment of a method for monitoring spam delivery based on a single robbing mode according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an embodiment of a method for monitoring spam delivery based on a single robbing mode according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a garbage delivery monitoring device based on a single robbing mode according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an embodiment of a garbage delivery monitoring device based on a single robbing mode according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to better understand the spam delivery monitoring method based on the order-grabbing mode and related products provided by the embodiment of the present application, a system architecture of the spam delivery monitoring method based on the order-grabbing mode applied to the embodiment of the present application is described first, the system architecture can be applied to a garbage source classification and decrement comprehensive service platform, the platform can be applied to a cell, an office building, a school, etc., and is applicable to any user located in a designated area of the cell or the office building, for example, if the platform is applied to the cell, it is applicable to a cell resident, a merchant, a cleaning staff, a delivery user, a supervision user, etc., and is not limited herein, wherein any user in the cell can be a delivery user or a supervision user, and the above users can load a client or an APP, etc. in an electronic device, and is not limited herein, and the online and offline linkage is realized.
Referring to fig. 1A, fig. 1A is a schematic diagram of a system architecture of a spam delivery monitoring method based on a single robbing mode according to an embodiment of the present application. As shown in fig. 1A, the system architecture may include one or more servers and a plurality of electronic devices, wherein:
the server may include, but is not limited to, a background server, a component server, a spam delivery system server, or a spam delivery monitoring software server, and the server may communicate with a plurality of electronic devices, and clients applied to spam source classification reduction may be loaded in the electronic devices. The server may generate a delivery event and transmit the delivery event to the electronic device.
The spam delivery monitoring device or the electronic device based on the order grabbing mode described in the embodiment of the present application may include a smart Phone (such as an Android Phone, an iOS Phone, a Windows Phone, etc.), a tablet computer, a palm computer, a notebook computer, a Mobile Internet device (MID, Mobile Internet Devices), a wearable device, etc., which are merely examples, but not exhaustive, and include but are not limited to the above Devices, and of course, the spam delivery monitoring device based on the order grabbing mode may also be a server.
It should be noted that the system architecture of the spam delivery monitoring method based on the order grabbing mode provided by the present application is not limited to that shown in fig. 1A.
Please refer to fig. 1B, which is a flowchart illustrating an embodiment of a method for monitoring spam delivery based on a single robbing mode according to the present application. The garbage delivery monitoring method based on the order grabbing mode described in the embodiment comprises the following steps:
101. and acquiring R junk delivery images of a designated area, which are shot aiming at least one delivery user in real time, wherein R is a positive integer.
The designated area may be designated by a user, or may default to a specific spatial range, for example, may be within the same cell, office building, or street, which is not limited herein; the delivery user can refer to a user group for throwing garbage in a designated area, one or more cameras or other shooting devices can be arranged at the garbage throwing place of the designated area, each camera can capture images, when the delivery user delivers the garbage, the cameras can capture the images to obtain a plurality of delivery images, the one or more cameras can be connected with the server, in the specific implementation, the server can obtain delivery images or video images and the like of R delivery users captured by the one or more cameras of the designated area in real time, wherein R is a positive integer.
Optionally, since the capacity of each trash can of the trash station is limited, for resource balancing, the server associates all the trash cans in the designated area according to the client of the user, so as to monitor the actual load conditions of the multiple trash cans, where the trash can load conditions of the trash station may include at least one of the following: space overload, about half of the remaining space, about one third of the remaining space, and the like, without limitation; the delivery status may include at least one of: delivery behavior is accurate, delivery behavior is incorrect, garbage bin load conditions of the garbage station, and the like, which are not limited herein; the act of delivering may include at least one of: the garbage bag is completely packaged, the garbage bag is correctly subpackaged and the like, and the packaging method is not limited herein; the delivery behavior error may include at least one of: a garbage bag packaging error, a garbage bag packaging damage, and the like, which are not limited herein; in the specific implementation, the delivery user can also take a picture of the delivery rubbish and upload the taken picture to the client, the server can acquire the picture uploaded by the delivery user through the client, and the delivery state in the picture uploaded by the delivery user can be obtained based on a preset neural network model, so that the load condition of the dustbin is obtained on line.
Optionally, when the delivery user needs to deliver the spam, the server may obtain an actual load condition of the current trash bin, and the trash bin load condition may include at least one of the following: space overload, about half of the remaining space, about one third of the remaining space, and the like, without limitation; if the actual load condition of the current dustbin is space overload, the delivery reminding can be pushed to the delivery user through the client, other dustbin closest to the current dustbin position can be pushed to the delivery user, and a path planning path is provided, and the delivery reminding can comprise at least one of the following: the trash bin is full, the trash bin is not capable of delivering trash, etc., and is not limited herein.
102. And analyzing the R garbage delivery images, determining the delivery behavior of the at least one delivery user, and generating R delivery events.
The server can analyze the R junk delivery images, so that R delivery behaviors of different delivery users can be obtained, and the delivery behaviors can comprise at least one of the following behaviors: a drop station, an undelivered trash bag, a delivery trash bag, etc., but not limited thereto, the server may automatically generate a delivery event for each delivery action according to the delivery actions, and the delivery event may be transmitted between the server and the electronic device or the client.
Optionally, in the step 102, the analyzing the R spam delivery images to determine the delivery behavior of the at least one delivery user may include the following steps:
21. performing image segmentation on the R garbage delivery images to obtain at least one target image, wherein each target image corresponds to a human body image or a garbage bag image of a target delivery user;
22. performing face recognition on at least one human body image in the at least one target image to obtain at least one face image;
23. carrying out duplication removal processing on the at least one face image to obtain at least one target human body image;
24. performing behavior recognition according to the at least one target human body image to obtain target limb behaviors;
25. and determining the delivery behavior of the delivery user according to the target limb behavior, the at least one target face image and the at least one garbage bag image.
The system comprises a server and one or more cameras, wherein the server comprises a plurality of delivery users, wherein the delivery users can send delivery images to the server, the delivery users can send the delivery images to the server, the servers can capture or shoot one or more cameras in a designated range to obtain R junk delivery images, the R junk delivery images can contain face images, character images or scene images of different delivery users, and when the delivery users send junk, the delivery users can carry trash bags or other pockets.
In specific implementation, framing or marking can be performed on the figure foreground(s) in each spam delivery image, and if the figure foreground images do not exist in the spam delivery images, the spam delivery images can be directly removed; if the figure foreground image exists in the garbage delivery image, modeling can be respectively carried out on the figure foreground and the figure background in order to identify the human body image and the garbage bag image, each pixel in the garbage delivery image can be connected with a figure foreground or background node, and if two adjacent nodes do not belong to the same figure foreground or background, the edge between the two nodes can be cut off, so that the figure foreground image and the figure background image can be distinguished; and modeling the background image and the garbage bag image, wherein each pixel in the garbage delivery image can be connected with a garbage bag foreground or background node, and if two adjacent nodes do not belong to the same person garbage bag foreground or background, the edge between the two nodes can be cut off, so that the garbage bag foreground image and the garbage bag background image are distinguished, and thus, the image segmentation method can be used for eliminating the interference of background information in the garbage delivery image, and the human body identification efficiency is improved.
In addition, in order to distinguish the identities of different people in the human body images, the human face recognition can be carried out on at least one human body image in the at least one target image to obtain at least one human face image, the human face image can be the human face image of the same person or different people, therefore, the at least one human face image can be subjected to the duplication elimination processing to obtain at least one human face image of different delivery users, and the human face image can be matched with the human face images prestored in the database, so that at least one target human body image of different delivery users can be obtained.
Furthermore, each target human body image may include a human body image of the user, and the human body image may be subjected to behavior recognition to obtain a target limb behavior, in this embodiment, the target limb behavior may include at least one of the following: walking, jumping, squatting, throwing, picking up objects, etc., without limitation. In specific implementation, at least one target human body image can be input into a preset neural network model to obtain at least one limb behavior, the preset neural network model can be set by a user or defaulted by a system, such as a convolutional neural network model, and finally, the delivery behavior of a delivery user can be judged through a garbage bag image, a target limb behavior and a face image, for example, the delivery behavior of the delivery user can be determined to be a user a through face recognition, the behavior of the user a throwing a garbage bag can be recognized through the target limb behavior and the garbage bag image, and the garbage bag is finally combined to be the user a throwing the garbage bag, so that the delivery behavior of the delivery user can be determined through the method.
103. And sending the R delivery events to electronic equipment of M supervising users in the specified area, wherein M is a positive integer.
The supervising user can be understood as a user group which installs and downloads the supervising constraint of the client in the same cell or a designated area, the supervising user can be any user who downloads the client, that is, the supervising user can also be a delivery user, the delivery user can also be a supervising user, so that the garbage delivery behavior of the delivery user can be monitored in real time, and after the server generates the delivery event of the delivery user, the server can send the generated delivery event to the electronic equipment or the client of the supervising user in the same cell or the designated area in a broadcast form group.
104. And determining S target supervision users successfully performing order grabbing according to a preset order grabbing algorithm, wherein S is a positive integer less than or equal to M.
Wherein, the preset order grabbing algorithm can be set by the user or defaulted by the system, and can be changed at any time, the server can send the delivery event to M supervising users in the same cell or the designated area in a group mode through broadcasting, after the electronic devices of the M supervising users receive the delivery event, the M supervising users can carry out order grabbing in the client, the order grabbing can be understood as freely grabbing the delivery event by the supervising users, if the order grabbing succeeds, the order grabbing is considered to succeed, after the order grabbing succeeds, S target supervising users which succeed in the order grabbing are determined, in the concrete implementation, a plurality of supervising users can grab the same order, or a plurality of supervising users grab a plurality of orders, for example, when the supervising users B and C grab the order, if the response time of the order grabbing success of the supervising users B is shortest, the order grabbing success is considered to succeed, therefore, when the delivery garbage is treated, the enthusiasm of supervisors or delivery personnel can be increased.
Optionally, in the step 104, the determining S target supervisory users who successfully preempt the order according to the preset order preemption algorithm may include the following steps:
411. acquiring order grabbing influence factors of the M supervising users, wherein the order grabbing influence factors comprise response time X of successful order grabbing, a position parameter Y and the number Z of the accepted orders of the supervising users, and the position parameter Y is the distance between the current position of the supervising user and a garbage delivery place;
412. acquiring weight factors corresponding to the order grabbing influence factors, wherein the weight factor of response time is a, the weight factor of position parameters is b, and the weight factor of the number Z of the connected orders is c, wherein a + b + c is 1, and the values of a, b and c are all 0-1;
413. obtaining the order grabbing value of the supervising user according to a preset weighting calculation formula as follows:
Figure BDA0002024890000000081
wherein Z ismaxIs the maximum value of the number of orders taken by the supervising user, ZmaxIs a positive integer;
414. and determining S target supervisory users which succeed in the order grabbing according to the order grabbing values of the M supervisory users, wherein the higher the order grabbing value is, the higher the probability of becoming the target supervisory users is.
When a supervising user performs order grabbing, the order grabbing condition of the supervising user can be influenced by a plurality of order grabbing influence factors, the order grabbing influence factors can be set by the user or defaulted by the system, and the order grabbing influence factors can comprise at least one of the following factors: presetting order grabbing time, response time of order grabbing success, position parameters (the distance between the current position of a monitoring user and a garbage delivery place or a garbage station), stipulating order grabbing time, stipulating maximum number of orders capable of being grabbed, stipulating the number of orders picked and the like, wherein the number of orders picked can be understood as the number of successful orders picked, and the successful order grabbing indicates the success of order picking; in specific implementation, weighting factors can be preset aiming at different order-grabbing influence factors, the weighting factors can be understood as the importance of the order-grabbing influence factors, the greater the weighting factors are, the greater the influence of the order-grabbing success is, the probability of order-grabbing success of different monitoring users can be determined through the order-grabbing influence factors and the weights of the order-grabbing influence factors, the probability or probability of a monitoring user (target monitoring user) with successful order-grabbing can be represented by an order-grabbing value, the greater the order-grabbing value is, the greater the probability or probability of being the target monitoring user is, and the target monitoring user with successful order-grabbing can be determined according to the order-grabbing value of the monitoring user.
For example, the preset order grabbing influence factors include response time X of successful order grabbing, a position parameter Y and a number Z of orders taken, a weight factor of the response time of successful order grabbing is preset as a, a weight factor of the position parameter is preset as b, and a weight factor of the number Z of orders taken is preset as c, where a + b + c is 1, values of a, b and c are all 0-1, and a weighting calculation formula can be preset to obtain an order grabbing value of the supervising user:
Figure BDA0002024890000000091
wherein Z ismaxA maximum value, Z, which can be expressed as the number of orders taken by the supervising usermaxThe order taking value is positive integer, the higher the order taking value is, the higher the probability of being the target supervision user is, the formula shows that the order taking influence factor with the maximum influence is the number of the accepted orders, the maximum number of the accepted orders can be preset, if the maximum number of the accepted orders is exceeded, the supervisor can not accept the orders or the order taking fails, the other influence factor is a position parameter, if the position parameter is smaller, the closer the supervisor is to the garbage delivery site, and therefore the closer the supervisor is to the garbage delivery site, the more the order taking influence factor is, the higher the probability of supervising the delivery personnel is, and the higher the order takingEfficiency, and the opportunity of order grabbing can be distributed to different supervisors in a maximized mode.
Optionally, in the step 104, the determining, according to the preset order grabbing algorithm, S target supervisory users who successfully grab orders may include the following steps:
421. obtaining M response times X of successful order grabbing of the M supervising usersiEach supervising user corresponds to one order grabbing response time, i is a positive integer, and i is smaller than or equal to M;
422 according to the M response times XiDetermining the response time XiN supervising users within a first preset threshold interval, wherein N is a positive integer less than or equal to M;
423. if N is 1, determining the N supervising users as target supervising users;
424. if N is larger than 1, determining the number Z of the accepted orders in the N supervised usersjAt [0, Zmax) Q supervision users in the interval, wherein j is a positive integer and is less than or equal to N;
425. obtaining Q position parameters Y of the Q supervising userskAnd the number Z of accepted orders of said Q supervising userskWherein k is a positive integer, and k is less than or equal to N;
426. according to a preset order grabbing algorithm, determining the order grabbing value of each of Q supervised users as follows:
Pk＝a*Xk+b*Yk+c*Zk；
427. and determining S supervised users of the order grabbing values of the Q supervised users in a second preset threshold interval as target supervised users, wherein S is a positive integer less than or equal to Q.
The first preset threshold interval may be set by the user or default by the system, and may be understood as the response time of the maximum allowed order grabbing success, for example, may be set to [2s, 100s ]; the second preset threshold interval is set by the user or the system is defaulted, the order grabbing value of the supervising user can be the target supervising user only when the order grabbing value is in the second preset threshold interval, at least one target supervising user with successful order grabbing can be determined according to the preset order grabbing algorithm, and the target supervising user is the supervising personnel with successful order grabbing.
In specific implementation, M response times X of successful order grabbing of M supervised users can be obtainediWherein each supervising user corresponds to a single order grabbing response time, i is a positive integer, i is less than or equal to M, M is a positive integer, XiCan be expressed as the response time of the order grabbing success of the ith supervisory user, and can be according to the M response times XiDetermining the response time XiN supervising users in a first preset threshold interval, wherein N is a positive integer less than or equal to M, if N is 1, the result can be that the order grabbing successful response time of only one supervising user is in the preset first threshold interval, and in order to not waste the supervising opportunity, the supervising user can be determined to be a target supervising user; if N is larger than 1, judging whether the supervising user can continue to receive orders according to the number of the received orders of the N users, and determining the number Z of the received orders in the N supervising usersjAt [0, Zmax) Q supervising users in the interval, wherein j is a positive integer, j is less than or equal to N, ZjCan be expressed as the number of the received orders of the jth supervising user, then the user which is possibly the target supervising user (successfully robbed orders) can be determined according to the positions of the Q supervising users away from the garbage throwing place, and the Q position parameters Y of the Q supervising users can be obtainedkAnd the number Z of accepted orders of said Q supervising userskWherein k is a positive integer, k is less than or equal to N, YkThe position parameter of the kth supervising user can be expressed, and finally, the order grabbing value of each supervising user in the Q supervising users can be determined as follows according to the preset order grabbing algorithm:
Pk＝a*Xk+b*Yk+c*Zk；
finally, S supervised users with order grabbing values in the Q supervised users within a second preset threshold interval can be determined as target supervised users, wherein S is a positive integer less than or equal to Q, and P iskCan be expressed as a preemptive value for the kth supervising user, and thus, can be expressed by the above methodAnd determining a target supervision user with maximum utilization or successfully preempting the order.
105. And acquiring R garbage delivery places reporting the R delivery events to the server, and pushing the R garbage delivery places to the S target supervision users.
The server can acquire R garbage throwing places where the cameras or other shooting devices reporting the R delivery events are located, and send the R garbage throwing places to the electronic device of the target supervising user, so that the target supervising user can find the garbage throwing place of the throwing user according to the garbage throwing place, and supervision measures are convenient to take.
Optionally, the supervising user or the delivery user may load a client or APP for sorting and reducing garbage in the electronic device, so as to implement online-offline linkage, in a specific implementation, the client may obtain a picture taken by the delivery user before garbage delivery and upload the picture to the server, the server may obtain an image uploaded by the delivery user, perform feature extraction on the picture to obtain a plurality of feature points, and match the feature points with preset feature points, where the preset feature points may be set by the user or default by the system, the preset feature points may be extracted from a sampled picture taken in a preset designated area, the preset designated area may be set by the user or default by the system, after matching, obtain a matching degree between the feature points and the preset feature points, and if the matching degree exceeds a preset threshold value, the shooting place of the photo is considered to be consistent with the shooting place in the sampling picture, and the shooting place of the photo can also be understood as consistent with the position parameters, so that the specific position of the garbage throwing place of the throwing user can be determined, and the specific position of the garbage throwing place is sent to a client of a target supervision user, therefore, the detailed specific place of the garbage throwing place can be obtained through online and offline linkage, the target supervision user can find the garbage throwing place of the throwing user according to the garbage throwing place, and therefore, the cost is saved and supervision measures are more conveniently taken through online and offline linkage.
Optionally, in step 105, the pushing the R spam delivery locations to the S target supervisory users may include the following steps:
51. obtaining the number Z of the received orders of the S target supervision userswWherein w is a positive integer and less than S, ZwRepresenting the number of accepted orders of the w-th target supervisory user;
52. according to the number Z of the accepted orders of the S target supervision userswAnd the maximum number of orders ZmaxDetermining the residual order receiving number of the w-th supervising user in the S target supervising users as follows: zmax-Zw；
53. Obtaining the R garbage delivery places, wherein R is a positive integer;
54. determining the priorities of the S target supervisory users according to the order grabbing values of the S target supervisory users, wherein the higher the order grabbing value is, the higher the priority is;
55. if it is
Figure BDA0002024890000000111
If the number of the garbage delivery places is larger than or equal to R, pushing the R garbage delivery places according to the priorities of the S target supervision users and the residual order receiving number of the S target supervision users;
56. if it is
Figure BDA0002024890000000112
If the number of the target supervisory users is less than R, pushing according to the priorities of the S target supervisory users and the residual order receiving number of the S target supervisory users
Figure BDA0002024890000000121
A garbage delivery site.
The garbage delivery method comprises the steps of receiving a garbage delivery request sent by a target supervision user, receiving a garbage delivery request sent by the target supervision user, and pushing the garbage delivery site according to the number of the received garbage delivery request.
For example, the number of orders Z taken for S target supervisory users may be obtainedsWherein S is a positive integer and smaller than S, and the number Z of the received orders of the S target supervisory userswAnd the maximum number of orders ZmaxDetermining the residual order receiving number of the w-th supervising user in the S target supervising users as follows: zmax-ZwThen, R garbage delivery places can be obtained, wherein R is 0 or a positive integer, and the priority of the S target supervisory users is determined according to the order grabbing values of the S target supervisory users, wherein the higher the order grabbing value is, the higher the priority is; if it is
Figure BDA0002024890000000122
If the priority is greater than or equal to R, pushing R garbage delivery places according to the priorities of the S target supervision users and the residual order receiving number of the S target supervision users, namely, the priorities are high and low, and preferentially pushing the garbage delivery places according to the residual order receiving number of each supervision user until the pushing is finished; if it is
Figure BDA0002024890000000123
If the number of the target supervisory users is less than R, pushing can be carried out according to the priorities of the S target supervisory users and the residual order receiving number of the S target supervisory users
Figure BDA0002024890000000124
A trash delivery location; therefore, the garbage can be pushed to the largest extent to put in places, and the supervision effect of a supervision user is improved.
Optionally, in step 56, the pushing is performed according to the priorities of the S target supervisory users and the remaining order receiving numbers of the S target supervisory users
Figure BDA0002024890000000125
After each garbage delivery site, the method also comprises the following steps:
57. monitoring the uploading state of the supervision data of the S target supervision users;
58. when monitoring any one of S target supervisory usersIf the uploading state is the uploaded state, pushing the rest
Figure BDA0002024890000000126
One of the individual trash delivery locations until the remaining trash delivery locations are pushed.
Wherein in the pushing
Figure BDA0002024890000000127
After a garbage delivery site, the garbage remains
Figure BDA0002024890000000128
Figure BDA0002024890000000129
And at each garbage delivery site, the server can monitor the uploading state of the supervision data of the S target supervision users, wherein the supervision data can comprise at least one of the following data: text, image, etc., and when any target supervising user is monitored to have uploaded the supervising data, the system continues to push a message to the target supervising user
Figure BDA00020248900000001210
And (5) the garbage throwing places are determined, and the rest garbage throwing places are known after being pushed in the circulation mode.
106. And monitoring the uploading states of S pieces of supervision data of the S target supervision users within a preset time threshold interval.
The preset time threshold interval can be set by a user or defaulted by a system, for example, the server can monitor the electronic equipment of the target monitoring user in real time within [1h, 15h ] after the monitoring user successfully performs order grabbing, and acquire the uploading state of the monitoring data of the target monitoring user; the monitoring data in the embodiment of the application may be understood as pictures or characters and the like taken by a monitoring user, and the monitoring user may upload the monitoring data to the server through the client after taking the pictures, wherein the characters may include at least one of the following: the delivery behavior is accurate, the delivery behavior is wrong, the packaging integrity of the garbage bags, the subpackaging of the garbage bags is wrong, the subpackaging of the garbage bags is correct, the loading condition of a garbage can of a garbage station, the packaging damage of the garbage bags and the like, and the method is not limited herein; the upload status may include at least one of: an uploaded state, an un-uploaded state, uploading in progress, etc., which are not limited herein, so that the supervising user may be given a certain time space to photograph and upload the supervising data and acquire the uploaded state in real time.
Optionally, since the capacity of each trash can of the trash station is limited, for resource balancing, the server associates all the trash cans in the designated area according to the client of the user, so as to monitor the actual load conditions of the multiple trash cans, where the trash can load conditions of the trash station may include at least one of the following: the space is overloaded, about half of the space remains, about one third of the space remains, and the like, and in a specific implementation, after the at least one supervising user uploads the supervising data, the actual load condition of the at least one garbage can in the designated area can be obtained according to the load condition of the garbage can of the garbage station in the at least one supervising data, so that online and offline monitoring of the plurality of garbage cans in the designated area is realized.
107. And generating S monitoring records according to the uploading states of S monitoring data of the S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record.
After acquiring the uploading state of the supervision data of any supervision user, the server can generate a supervision record, and the supervision record can include supervised or unsupervised, for example, the supervised user can understand that the supervision user uploads the supervision data, so that a certain supervision effect is achieved on the supervision work of the supervision user.
108. And determining the score of each target supervising user in the S target supervising users based on a preset scoring algorithm according to the S supervising data of the S target supervising users and the S supervising records.
The scores of the supervision users can be scored, the scores of all the S target supervision users can be determined based on a preset scoring algorithm according to the supervision data and the R supervision records of the S target supervision users, and therefore supervision work of the supervision users can be better evaluated.
Optionally, after determining the score of each of the S target supervising users, the score obtained by any one of the S target supervising users may be directly uploaded to the task record of the supervising user, and when adding one task record, a certain score may be added, and the final score may be used for exchanging different gifts or for consumption, etc., so that the enthusiasm of the supervising user may be improved.
Optionally, in step 108, the determining, according to the S pieces of surveillance data of the S target surveillance users and the S pieces of surveillance records, a score of each target surveillance user of the S target surveillance users based on a preset scoring algorithm may include the following steps:
81. acquiring the S pieces of supervision data uploaded by the S target supervision users, wherein the supervision data comprises: the system comprises characters and images, wherein the characters comprise accurate delivery behaviors, wrong delivery behaviors and garbage bag packaging integrity;
82. carrying out image recognition on the image to obtain the delivery state of the delivery user, wherein the delivery state comprises accurate delivery behavior, wrong delivery behavior and garbage bag packaging integrity;
83. determining the matching degree between the delivery state of the delivery user and the characters;
84. acquiring first weight factors of the matching degrees in different preset threshold intervals and second weight factors corresponding to supervision records of the target supervision user, wherein the supervision records comprise supervised records and unsupervised records;
85. and performing multiplication operation according to the first weight factor and the second weight factor to obtain the score of each target supervising user in the S target supervising users.
Wherein, after the supervising user uploads the supervising data, the supervising data may include images and texts, and the texts may be understood as the evaluation description of the supervising user on the delivery behavior of the delivery user, for example, the texts may include at least one of the following: the delivery behavior is accurate, the delivery behavior is wrong, the packaging integrity of the garbage bags, the subpackaging of the garbage bags is wrong, the subpackaging of the garbage bags is correct, the loading condition of a garbage can of a garbage station, the packaging damage of the garbage bags and the like, and the method is not limited herein; the server may obtain the supervision data, and perform image recognition on the image in the supervision data, and in a specific implementation, the server may obtain a delivery state of a delivery user in the image based on a preset neural network model, where the delivery state may include at least one of: delivery behavior is accurate, delivery behavior is incorrect, garbage bin load conditions of the garbage station, and the like, which are not limited herein; the act of delivering may include at least one of: the garbage bag is completely packaged, the garbage bag is correctly subpackaged and the like, and the packaging method is not limited herein; the delivery behavior error may include at least one of: a garbage bag packaging error, a garbage bag packaging damage, and the like, which are not limited herein; then, the delivery status of the delivery user identified by the image can be matched with the text of the supervising user to obtain the matching degree between the delivery status of the delivery user and the text, a first weight factor with the matching degree in different preset threshold intervals can be preset, the preset threshold intervals can be set by the user or default by the system, for example, the weight factor with the matching degree in [ 60%, 70% ] can be set to be 0.2, the weight factor with the matching degree in [ 80%, 90% ] can be 0.5, a second weight factor corresponding to different supervising records can be preset, the sum of the supervised and unsupervised weight factors is 1, for example, the supervised weight is 0.8, the unsupervised weight factor is 0.2, and finally, the first weight factor and the second weight factor are multiplied to obtain the score of each target supervising user in the S target supervising users, a score may be determined for each of the S target supervisory users.
Optionally, the server may obtain a delivery status entered by at least one delivery user or a picture uploaded by the delivery user, obtain the delivery status in the picture uploaded by the delivery user through a preset neural network model, match the delivery status with a delivery status of a supervising user to obtain a plurality of matching degrees, obtain scores of the delivery user and the supervising user according to scores corresponding to preset different matching degree intervals, for example, the score of the matching degree [ 60%, 70% ] may be set to 1 score, the score of the matching degree [ 80%, 90% ] may be set to 2 score, if the obtained matching degree is 65%, the supervising user and the delivery user add one score respectively, and simultaneously synchronize the obtained scores into task records in clients of the supervising user and the delivery user, and the final score may be used for exchanging different gifts or for consumption, etc., therefore, the enthusiasm of garbage delivery and garbage monitoring of the user can be improved.
It can be seen that, through the embodiment of the application, R garbage delivery images shot aiming at least one delivery user in a designated area are obtained in real time, the R garbage delivery images are analyzed, the delivery behaviors of the R delivery users are determined, R delivery events are generated, the R delivery events are sent to electronic devices of M supervisory users in the designated area, S target supervisory users who successfully preempt the order are determined according to a preset preempt algorithm, R garbage delivery places which report the R delivery events to a server are obtained, the R garbage delivery places are pushed to the electronic devices of the S target supervisory users, the uploading states of the supervisory data of the S target supervisory users are monitored in a preset time threshold interval, S supervisory records are generated according to the uploading states of the S supervisory data of the S target supervisory users, wherein, the method comprises the steps that supervision data uploaded by each target supervision user corresponds to one supervision record, and the score of each target supervision user in S target supervision users is determined based on a preset scoring algorithm according to the supervision data of the S target supervision users and the S supervision records.
In accordance with the above, please refer to fig. 2, which is a flowchart illustrating an embodiment of a method for monitoring spam delivery based on a list grabbing mode according to an embodiment of the present application. The garbage delivery monitoring method based on the order grabbing mode described in the embodiment comprises the following steps:
201. and carrying out image segmentation on the R garbage delivery images to obtain at least one target image, wherein each target image corresponds to a human body image or a garbage bag image of a target delivery user.
202. And performing face recognition on at least one human body image in the at least one target image to obtain at least one face image.
203. And carrying out duplication removal processing on the at least one face image to obtain a plurality of target human body images.
204. And performing behavior recognition according to the at least one target human body image to obtain target limb behaviors.
205. And determining the delivery behavior of the at least one delivery user according to the target limb behavior, the at least one target face image and the garbage bag image, and generating the R delivery events.
206. And sending the R delivery events to electronic equipment of M supervising users in the specified area, wherein M is a positive integer.
207. And determining S target supervision users successfully performing order grabbing according to a preset order grabbing algorithm, wherein S is a positive integer less than or equal to M.
208. And acquiring R garbage delivery places reporting the R delivery events to the server, and pushing the R garbage delivery places to the S target supervision users.
209. And monitoring the uploading state of the supervision data of the S target supervision users within a preset time threshold interval.
210. And generating S monitoring records according to the uploading states of S monitoring data of the S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record.
211. And determining the score of each target supervising user in the S target supervising users based on a preset scoring algorithm according to the S supervising data of the S target supervising users and the S supervising records.
Optionally, the detailed description of the steps 201 to 211 may refer to corresponding steps from step 101 to step 108 of the spam delivery monitoring method based on the order grabbing mode described in fig. 1B, and will not be described herein again.
It can be seen that, through the embodiment of the application, the R garbage delivery images are subjected to image segmentation to obtain at least one target image, wherein each target image corresponds to a human body image or a garbage bag image of a target delivery user; performing face recognition on at least one human body image in at least one target image to obtain at least one face image; carrying out duplication removal processing on at least one face image to obtain a plurality of target human body images; performing behavior recognition according to at least one target human body image to obtain target limb behaviors; determining the delivery behavior of at least one delivery user according to the target limb behavior, at least one target face image and the garbage bag image, and generating R delivery events; sending the R delivery events to electronic equipment of M supervision users in a specified area; determining S target supervision users who successfully preempt the order according to a preset order preemption algorithm; acquiring R garbage delivery places reporting the R delivery events to a server, pushing the R garbage delivery places to electronic equipment of S target supervisory users, and monitoring the uploading state of supervisory data of the S target supervisory users within a preset time threshold interval; generating S supervision records according to the uploading state of the supervision data of S target supervision users; according to the monitoring data and the S monitoring records of the S target monitoring users, the score of each target monitoring user in the S target monitoring users is determined based on a preset scoring algorithm, so that the interaction between the server and the electronic equipment and among the users can be realized through the order grabbing mode while the delivery behavior of the delivery user is determined through the method, and finally, the monitoring consciousness of the monitoring user is improved through the order grabbing mode.
In accordance with the above, the following is a device for implementing the above garbage delivery monitoring method based on the order grabbing mode, specifically as follows:
please refer to fig. 3, which is a schematic structural diagram of an embodiment of a garbage delivery monitoring device based on a list robbing mode according to an embodiment of the present application. The garbage delivery monitoring device based on the order grabbing mode described in this embodiment includes: the acquiring unit 301, the analyzing unit 302, the sending unit 303, the determining unit 304, the monitoring unit 305, and the generating unit 306 are specifically as follows:
an acquisition unit 301, configured to acquire, in real time, R spam images of a designated area, which are shot for at least one delivery user;
an analyzing unit 302, configured to analyze the R spam delivery images, determine delivery behaviors of the at least one delivery user, and generate R delivery events;
a sending unit 303, configured to send the R delivery events to electronic devices of M supervising users in the designated area;
the determining unit 304 is configured to determine, according to a preset order grabbing algorithm, S target supervisory users who successfully grab orders;
the obtaining unit 301 is further configured to obtain R spam delivery locations that report the R delivery events to the server, and push the R spam delivery locations to the S target supervisory users;
the monitoring unit 305 is configured to monitor the uploading states of the S pieces of supervision data of the S target supervision users within a preset time threshold interval;
the generating unit 306 is configured to generate S monitoring records according to the uploading states of S monitoring data of the S target monitoring users, where the monitoring data uploaded by each target monitoring user corresponds to one monitoring record;
the determining unit 304 is further configured to determine, based on a preset scoring algorithm, a score of each of the S target supervising users according to the S supervising data and the S supervising records of the S target supervising users.
It can be seen that, by the garbage delivery monitoring device based on the order grabbing mode described in the embodiment of the present application, R garbage delivery images shot for at least one delivery user in an assigned area are obtained in real time, the R garbage delivery images are analyzed to determine delivery behaviors of the R delivery users, generate R delivery events, send the R delivery events to electronic devices of M supervisory users in the assigned area, determine S target supervisory users who successfully grab orders according to a preset order grabbing algorithm, obtain R garbage delivery places reporting the R delivery events to a server, push the R garbage delivery places to electronic devices of the S target supervisory users, monitor upload states of supervisory data of the S target supervisory users within a preset time threshold interval, and according to upload states of the S supervisory data of the S target supervisory users, and generating S monitoring records, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record, and the score of each target monitoring user in the S target monitoring users is determined based on a preset scoring algorithm according to the monitoring data of the S target monitoring users and the S monitoring records, so that the monitoring users can be dispatched based on an order grabbing mode, the delivery behavior of delivery users is monitored, the garbage delivery consciousness of users in a specified area is improved, and the manual waste is avoided.
It can be understood that the functions of each program unit of the spam delivery monitoring device based on the order grabbing mode in this embodiment can be specifically implemented according to the method in the above method embodiment, and the specific implementation process thereof may refer to the related description of the above method embodiment, which is not described herein again.
In accordance with the above, please refer to fig. 4, which is a schematic structural diagram of an embodiment of a server according to an embodiment of the present disclosure. The server described in this embodiment includes: at least one input device 1000; at least one output device 2000; at least one processor 3000, e.g., a CPU; and a memory 4000, the input device 1000, the output device 2000, the processor 3000, and the memory 4000 being connected by a bus 5000.
The input device 1000 may be a touch panel, a physical button, or a mouse.
The output device 2000 may be a display screen.
The memory 4000 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 4000 is used for storing a set of program codes, and the input device 1000, the output device 2000 and the processor 3000 are used for calling the program codes stored in the memory 4000 to execute the following operations:
the processor 3000 is configured to:
acquiring at least one R junk delivery images, which are shot by at least one delivery user, of a designated area in real time, wherein R is a positive integer;
analyzing the at least one R garbage delivery image, determining delivery behaviors of the at least one delivery user, and generating at least one R delivery event;
sending the at least one R delivery events to electronic equipment of at least one M supervising users in the designated area, wherein M is a positive integer;
determining at least one S target supervisory users which successfully preempt the order according to a preset order preemption algorithm, wherein S is a positive integer less than or equal to M;
acquiring at least one R garbage delivery places reporting the at least one R delivery events to the server, and pushing the at least one R garbage delivery places to the electronic equipment of the at least one S target supervisory users;
monitoring the uploading states of S pieces of supervision data of the S target supervision users within a preset time threshold interval;
generating S monitoring records according to the uploading states of S monitoring data of S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record;
and determining the score of each target supervising user in the S target supervising users based on a preset scoring algorithm according to the S supervising data of the S target supervising users and the S supervising records.
In one possible example, the processor 3000, in analyzing the R spam delivery images to determine the delivery behavior of the at least one delivery user, is specifically configured to:
performing image segmentation on the R garbage delivery images to obtain at least one target image, wherein each target image corresponds to a human body image or a garbage bag image of a target delivery user;
performing face recognition on at least one human body image in the at least one target image to obtain at least one face image;
carrying out duplication removal processing on the at least one face image to obtain at least one target human body image;
performing behavior recognition according to the at least one target human body image to obtain target limb behaviors;
and determining the delivery behavior of the at least one delivery user according to the target limb behavior, the at least one target face image and the garbage bag image.
In one possible example, in terms of determining S target supervisory users who successfully preempt the order according to a preset order preemption algorithm, the processor 3000 is specifically configured to:
acquiring order grabbing influence factors of the M supervising users, wherein the order grabbing influence factors comprise response time X of successful order grabbing, a position parameter Y and the number Z of the accepted orders of the supervising users, and the position parameter Y is the distance between the current position of the supervising user and a garbage delivery place;
acquiring weight factors corresponding to the order grabbing influence factors, wherein the weight factor of response time is a, the weight factor of position parameters is b, and the weight factor of the number Z of the connected orders is c, wherein a + b + c is 1, and the values of a, b and c are all 0-1;
and calculating according to the following formula to obtain the order grabbing values of the M supervised users as follows:
Figure BDA0002024890000000201
wherein Z ismaxFor the maximum number of orders taken by the M supervising users, ZmaxIs a positive integer;
and determining S target supervisory users which succeed in the order grabbing according to the order grabbing values of the M supervisory users, wherein the higher the order grabbing value is, the higher the probability of becoming the target supervisory users is.
In one possible example, in terms of determining S target supervisory users who successfully preempt the order according to a preset order preemption algorithm, the processor 3000 is specifically configured to:
obtaining M response times X of successful order grabbing of the M supervising usersiEach supervising user corresponds to one order grabbing response time, i is a positive integer, and i is smaller than or equal to M;
according to the M response times XiDetermining the response time XiN supervising users within a first preset threshold interval, wherein N is a positive integer less than or equal to M;
if N is 1, determining the N supervising users as target supervising users;
if N is larger than 1, determining the number Z of the accepted orders in the N supervised usersjAt [0, Zmax) Q supervision users in the interval, wherein j is a positive integer and is less than or equal to N;
obtaining Q position parameters Y of the Q supervising userskAnd the number Z of accepted orders of said Q supervising userskWherein k is a positive integer, and k is less than or equal to N;
according to a preset order grabbing algorithm, determining the order grabbing value of each of Q supervised users as follows:
Pk＝a*Xk+b*Yk+c*Zk；
and determining S supervised users of the order grabbing values of the Q supervised users in a second preset threshold interval as target supervised users, wherein S is a positive integer less than or equal to Q.
In one possible example, in said pushing the R spam delivery locations to the S target supervising users, the processor 3000 is specifically configured to:
obtaining the number Z of the received orders of the S target supervision userswWherein w is a positive integer and less than S, ZwRepresenting the number of accepted orders of the w-th target supervisory user;
according to the number Z of the accepted orders of the S target supervision userswAnd the maximum number of orders ZmaxAmong S target supervision usersThe remaining pick-up number of the w-th supervising user is: zmax-Zw；
Obtaining the R garbage delivery places, wherein R is a positive integer;
determining the priorities of the S target supervisory users according to the order grabbing values of the S target supervisory users, wherein the higher the order grabbing value is, the higher the priority is;
if it is
Figure BDA0002024890000000202
If the number of the garbage delivery places is larger than or equal to R, pushing the R garbage delivery places according to the priorities of the S target supervision users and the residual order receiving number of the S target supervision users;
if it is
Figure BDA0002024890000000203
If the number of the target supervisory users is less than R, pushing according to the priorities of the S target supervisory users and the residual order receiving number of the S target supervisory users
Figure BDA0002024890000000211
A garbage delivery site.
In one possible example, after determining the priorities of the S target supervisory users and the remaining number of orders for the S target supervisory users, the processor 3000 is further configured to:
monitoring the uploading state of the supervision data of the S target supervision users, wherein the uploading state comprises an uploaded state or an un-uploaded state;
when the uploading state of any one target supervisory user in the S target supervisory users is monitored to be the uploaded state, pushing the rest target supervisory users
Figure BDA0002024890000000212
One of the individual trash delivery locations until the remaining trash delivery locations are pushed.
In one possible example, in terms of determining the score of each of the S target supervisory users based on a preset scoring algorithm for the S supervisory data and the S supervisory records of the S target supervisory users, the processor 3000 is specifically configured to:
acquiring the S pieces of supervision data uploaded by the S target supervision users, wherein the supervision data comprises: the system comprises characters and images, wherein the characters comprise accurate delivery behaviors, wrong delivery behaviors and garbage bag packaging integrity;
carrying out image recognition on the image to obtain the delivery state of the delivery user, wherein the delivery state comprises accurate delivery behavior, wrong delivery behavior and garbage bag packaging integrity;
determining the matching degree between the delivery state of the delivery user and the characters;
acquiring first weight factors of the matching degrees in different preset threshold intervals and second weight factors corresponding to supervision records of the target supervision user, wherein the supervision records comprise supervised records and unsupervised records;
and performing multiplication operation according to the first weight factor and the second weight factor to obtain the score of each target supervising user in the S target supervising users.
The present application further provides a computer storage medium, where the computer storage medium may store a program, and when the program is executed, the program includes some or all of the steps of any one of the spam delivery monitoring methods based on the order grabbing mode described in the above method embodiments.
While the present application has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device), or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. A computer program stored/distributed on a suitable medium supplied together with or as part of other hardware, may also take other distributed forms, such as via the Internet or other wired or wireless telecommunication systems.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (10)

1. A garbage delivery monitoring method based on a list grabbing mode is applied to a server and is characterized by comprising the following steps:
acquiring R junk delivery images shot by at least one delivery user in a designated area in real time, wherein R is a positive integer;
analyzing the R garbage delivery images, determining the delivery behavior of the at least one delivery user, and generating R delivery events;
sending the R delivery events to electronic equipment of M supervising users in the designated area, wherein M is a positive integer;
determining S target supervisory users who successfully preempt the order according to a preset order preemption algorithm, wherein S is a positive integer less than or equal to M;
acquiring R garbage delivery places reporting the R delivery events to the server, and pushing the R garbage delivery places to the S target supervision users;
monitoring the uploading states of S pieces of supervision data of the S target supervision users within a preset time threshold interval;
generating S monitoring records according to the uploading states of S monitoring data of S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record;
and determining the score of each target supervising user in the S target supervising users based on a preset scoring algorithm according to the S supervising data and the S supervising records of the S target supervising users.
2. The method for monitoring spam delivery according to claim 1, wherein said analyzing the R spam delivery images to determine delivery behavior of the at least one delivery user comprises:
performing image segmentation on the R garbage delivery images to obtain at least one target image, wherein each target image corresponds to a human body image or a garbage bag image of a target delivery user;
performing face recognition on at least one human body image in the at least one target image to obtain at least one face image;
carrying out duplication removal processing on the at least one face image to obtain at least one target human body image;
performing behavior recognition according to the at least one target human body image to obtain target limb behaviors;
and determining the delivery behavior of the at least one delivery user according to the target limb behavior, the at least one target face image and the at least one garbage bag image.
3. The method for monitoring spam delivery according to claim 1, wherein the determining S target supervisory users who successfully preempt an order according to a preset order preemption algorithm comprises:
acquiring order grabbing influence factors of the M supervising users, wherein the order grabbing influence factors comprise response time X of successful order grabbing, a position parameter Y and the number Z of the accepted orders of the supervising users, and the position parameter Y is the distance between the current position of the supervising user and a garbage delivery place;
acquiring weight factors corresponding to the order grabbing influence factors, wherein the weight factor of response time is a, the weight factor of position parameters is b, and the weight factor of the number Z of the connected orders is c, wherein a + b + c is 1, and the values of a, b and c are all 0-1;
and calculating according to the following formula to obtain the order grabbing values of the M supervised users as follows:
Figure FDA0002024889990000021
wherein Z ismaxFor the maximum number of orders taken by the M supervising users, ZmaxIs a positive integer;
and determining S target supervisory users which succeed in the order grabbing according to the order grabbing values of the M supervisory users, wherein the higher the order grabbing value is, the higher the probability of becoming the target supervisory users is.
4. The method for monitoring spam delivery according to claim 1, wherein the step of determining S target supervisory users who successfully preempt an order according to a preset order preemption algorithm comprises:
obtaining M response times X of successful order grabbing of the M supervising usersiEach supervising user corresponds to one order grabbing response time, i is a positive integer, and i is smaller than or equal to M;
according to the M response times XiDetermining the response time XiN supervising users within a first preset threshold interval, wherein N is a positive integer less than or equal to M;
if N is 1, determining that the N supervising users are the S target supervising users;
if N is larger than 1, determining the number Z of the accepted orders in the N supervised usersjAt [0, Zmax) Q supervision users in the interval, wherein j is a positive integer and is less than or equal to N;
obtaining Q position parameters Y of the Q supervising userskAnd said Q supervising usersNumber of connected sheets ZkWherein k is a positive integer, and k is less than or equal to N;
according to a preset order grabbing algorithm, determining the order grabbing value of each of Q supervised users as follows:
Pk＝a*Xk+b*Yk+c*Zk；
and determining S supervised users of the order grabbing values of the Q supervised users in a second preset threshold interval as target supervised users, wherein S is a positive integer less than or equal to Q.
5. The method for monitoring spam delivery according to any of claims 1-4, wherein said pushing the R spam delivery sites to the S target supervisory users comprises:
obtaining the number Z of the received orders of the S target supervision userswWherein w is a positive integer and less than S, ZwRepresenting the number of accepted orders of the w-th target supervisory user;
according to the number Z of the accepted orders of the S target supervision userswAnd the maximum number of orders ZmaxDetermining the residual order receiving number of the w-th supervising user in the S target supervising users as follows: zmax-Zw；
Obtaining the R garbage delivery places, wherein R is a positive integer;
determining the priorities of the S target supervisory users according to the order grabbing values of the S target supervisory users, wherein the higher the order grabbing value is, the higher the priority is;
if it is
Figure FDA0002024889990000031
If the number of the garbage delivery places is larger than or equal to R, pushing the R garbage delivery places according to the priorities of the S target supervision users and the residual order receiving number of the S target supervision users;
if it is
Figure FDA0002024889990000032
If less than R, the system is used for supervision according to the S targetsThe priority of the users and the residual order receiving number of the S target supervision users are pushed
Figure FDA0002024889990000033
A garbage delivery site.
6. The method for monitoring spam delivery according to claim 5, wherein the pushing is performed according to the priorities of the S target users and the remaining order receiving numbers of the S target users
Figure FDA0002024889990000034
After each of the refuse delivery sites, the method further comprises:
monitoring the uploading state of the supervision data of the S target supervision users, wherein the uploading state comprises an uploaded state or an un-uploaded state;
when the uploading state of any one target supervisory user in the S target supervisory users is monitored to be the uploaded state, pushing the rest target supervisory users
Figure FDA0002024889990000035
One of the individual trash delivery locations until the remaining trash delivery locations are pushed.
7. The method for monitoring spam delivery according to claim 1, wherein the determining the score of each of the S target users based on a pre-established scoring algorithm according to the S surveillance data of the S target users and the S surveillance records comprises:
acquiring the S pieces of supervision data uploaded by the S target supervision users, wherein the supervision data comprises: the system comprises characters and images, wherein the characters comprise accurate delivery behaviors, wrong delivery behaviors and garbage bag packaging integrity;
carrying out image recognition on the image to obtain the delivery state of the delivery user, wherein the delivery state comprises accurate delivery behavior, wrong delivery behavior and garbage bag packaging integrity;
determining the matching degree between the delivery state of the delivery user and the characters;
acquiring first weight factors of the matching degrees in different preset threshold intervals and second weight factors corresponding to supervision records of the target supervision user, wherein the supervision records comprise supervised records and unsupervised records;
and performing multiplication operation according to the first weight factor and the second weight factor to obtain the score of each target supervising user in the S target supervising users.
8. A garbage delivery monitoring device based on a single-robbing mode is characterized by comprising:
the system comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring R garbage delivery images which are shot aiming at least one delivery user in a designated area in real time;
the analysis unit is used for analyzing the R garbage delivery images, determining the delivery behavior of the at least one delivery user and generating R delivery events;
a sending unit, configured to send the R delivery events to electronic devices of M supervising users in the designated area;
the determining unit is used for determining S target supervision users who successfully preempt the order according to a preset order-preempting algorithm;
the acquisition unit is further configured to acquire R spam delivery sites that report the R delivery events to the server, and push the R spam delivery sites to the S target supervisory users;
the monitoring unit is used for monitoring the uploading state of the supervision data of the S target supervision users within a preset time threshold interval;
the generation unit is used for generating S monitoring records according to the uploading states of the monitoring data of the S target monitoring users, wherein the monitoring data uploaded by each target monitoring user corresponds to one monitoring record;
the determining unit is further configured to determine, based on a preset scoring algorithm, a score of each of the S target supervisory users according to the supervisory data of the S target supervisory users and the S supervisory records.
9. A server, comprising a processor, a memory for storing one or more programs and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-7.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050193901A1 (en) * 2004-02-18 2005-09-08 Buehler David B. Food preparation system
CN107341907A (en) * 2017-08-16 2017-11-10 王修晖 A kind of municipal refuse intelligence recovery method, apparatus and system
CN108706241A (en) * 2018-05-25 2018-10-26 杭州鸣扬科技有限公司 Garbage retrieving system and method
CN108986001A (en) * 2018-07-26 2018-12-11 深圳市赛亿科技开发有限公司 A kind of rubbish is visited retrieval management method and system
CN109472447A (en) * 2018-09-30 2019-03-15 广东荟星阁网络科技有限公司 Hotel service intelligent management method, server, system and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050193901A1 (en) * 2004-02-18 2005-09-08 Buehler David B. Food preparation system
CN107341907A (en) * 2017-08-16 2017-11-10 王修晖 A kind of municipal refuse intelligence recovery method, apparatus and system
CN108706241A (en) * 2018-05-25 2018-10-26 杭州鸣扬科技有限公司 Garbage retrieving system and method
CN108986001A (en) * 2018-07-26 2018-12-11 深圳市赛亿科技开发有限公司 A kind of rubbish is visited retrieval management method and system
CN109472447A (en) * 2018-09-30 2019-03-15 广东荟星阁网络科技有限公司 Hotel service intelligent management method, server, system and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
浦灵敏: "基于互联网+社区垃圾智慧服务***设计的研究", 《电子技术》, pages 44 - 45 *

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