CN111814518B

CN111814518B - Garbage delivery monitoring method based on robbery mode and related products

Info

Publication number: CN111814518B
Application number: CN201910290939.9A
Authority: CN
Inventors: 李涛
Original assignee: Shenzhen Jiajia Classification Technology Co ltd
Current assignee: Shenzhen Jiajia Classification Technology Co ltd
Priority date: 2019-04-11
Filing date: 2019-04-11
Publication date: 2024-05-14
Anticipated expiration: 2039-04-11
Also published as: CN111814518A

Abstract

The embodiment of the application provides a garbage delivery monitoring method based on a robbery mode and a related product, wherein the method comprises the following steps: the method comprises the steps of acquiring a junk delivery image shot for a delivery user in a designated area in real time, analyzing the junk delivery image, generating delivery events, sending the delivery events to electronic equipment of a supervision user in the designated area, determining target supervision users with successful delivery according to a preset delivery algorithm, acquiring junk delivery sites reporting the delivery events to a server, pushing the junk delivery sites to the electronic equipment of the target supervision users, generating supervision records according to uploading states of supervision data of the target supervision users, determining scores of each target supervision user in the target supervision users according to the supervision data and the supervision records of the target supervision users, and accordingly, dispatching the supervision users based on the delivery modes and monitoring delivery behaviors of the delivery users.

Description

Garbage delivery monitoring method based on robbery 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 robbery mode and related products.

Background

Due to rapid increase of urban population, daily yield of garbage is rapidly increased, people are promoted to pay attention to the treatment fluid of garbage, in a district, garbage is usually thrown into a garbage station by environmental protection personnel or residents, generally, the garbage station or garbage can be divided into recyclable matters or non-recyclable matters, and throwing personnel can throw errors in the garbage throwing process, in this case, if the garbage collection personnel are hired to sort different garbage, personnel waste can be caused, and the personnel throwing garbage can throw errors again, so that labor force of a receiver is increased.

Disclosure of Invention

The embodiment of the application provides a garbage delivery monitoring method based on a robbery mode and a related product, which can monitor the delivery behavior of a delivery user by supervising the user, improve the garbage delivery consciousness of the user in a designated area and avoid the waste of manpower.

In a first aspect, an embodiment of the present application provides a method for monitoring delivery of garbage based on a robbery mode, including:

Acquiring R garbage delivery images of a designated area, which are shot by at least one delivery user, in real time, wherein R is a positive integer;

Analyzing the R garbage delivery images, determining delivery behaviors of the at least one delivery user, and generating R delivery events;

The R delivery events are sent to electronic equipment of M supervision users in the appointed area, wherein M is a positive integer;

determining S target supervision users with successful robbing according to a preset robbing algorithm, wherein S is a positive integer less than or equal to M;

acquiring R garbage delivery sites for reporting the R delivery events to the server, and pushing the R garbage delivery sites to the S target monitoring users;

monitoring the uploading states of S pieces of supervision data of the S target supervision users in a preset time threshold interval;

Generating S supervision records according to the uploading states of S supervision data of the S target supervision users, wherein the supervision data uploaded by each target supervision user corresponds to one supervision record;

And determining the score of each target supervision user in the S target supervision users based on a preset scoring algorithm according to the S supervision data and the S supervision records of the S target supervision users.

In a second aspect, an embodiment of the present application provides a garbage delivery monitoring device based on a robbery mode, including:

The acquisition unit is used for acquiring at least one R rubbish delivery images of the designated area, which are shot by at least one delivery user in real time;

The analysis unit is used for analyzing the R garbage delivery images, determining the delivery behaviors of the at least one delivery user and generating R delivery events;

the sending unit is used for sending the R delivery events to the electronic equipment of M supervision users in the appointed area;

The determining unit is used for determining S target supervision users who successfully rob the bill according to a preset bill robbing algorithm;

The acquisition unit is further used for acquiring R garbage delivery sites for reporting the R delivery events to the server and pushing the R garbage delivery sites to the S target supervision users;

the monitoring unit is used for monitoring the uploading state of the supervision data of the at least one S target supervision users in a preset time threshold interval;

the generation unit is used for generating at least one S supervision records according to the uploading state of the supervision data of the at least one S target supervision users, wherein the supervision data uploaded by each target supervision user corresponds to one supervision record;

the determining unit is further configured to determine a score of each of the S target supervising users based on a preset scoring algorithm according to the supervision data of the S target supervising users and the S supervision 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, embodiments of the present application provide a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program, where the computer program causes a computer to execute instructions of some or all of the steps as described in the first aspect of the embodiments of the present application.

In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of the 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:

The method comprises the steps of acquiring R junk delivery images of a designated area, shooting at least one delivery user, analyzing the R junk delivery images, determining delivery behaviors of the R delivery users, generating R delivery events, sending the R delivery events to M supervision users 'electronic equipment of the designated area, determining S target supervision users who successfully rob a bill according to a preset robber algorithm, acquiring R junk delivery sites for reporting the R delivery events to a server, pushing the R junk delivery sites to the S target supervision users' electronic equipment, monitoring uploading states of supervision data of the S target supervision users within a preset time threshold interval, generating S supervision records according to uploading states of S supervision data of the S target supervision users, wherein the supervision data uploaded by each target supervision user corresponds to one supervision record, determining scores of each target supervision user in the S target supervision users based on the preset scoring algorithm according to the supervision data of the S target supervision users, and the S supervision records, and therefore, the single delivery behavior of the user can be controlled based on the robber mode, the manual delivery of the designated delivery users is avoided, and the manual delivery of the designated delivery area is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1A is a schematic diagram of a system architecture of a method for monitoring delivery of garbage based on a robbed order mode according to an embodiment of the present application;

FIG. 1B is a schematic flow diagram of an embodiment of a method for monitoring delivery of garbage based on a robbed order mode according to an embodiment of the present application;

fig. 2 is a schematic flow chart of an embodiment of a method for monitoring delivery of garbage based on a robbery mode according to the embodiment of the present application;

Fig. 3 is a schematic structural diagram of a garbage delivery monitoring device based on a robbery 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 robbery mode according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. 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.

The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may 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 may 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to better understand the method for monitoring the delivery of the garbage based on the robbery mode and the related products provided by the embodiments of the present application, a system architecture of the method for monitoring the delivery of the garbage based on the robbery mode, which is applicable to the embodiments of the present application, is described below, and the system architecture can be applied to a garbage source classification and decrement integrated service platform, which can be applied to a cell, an office building, a school, etc., and is applicable to any user in a designated area of the cell or the office building, etc., for example, if the platform is applied to the cell, the platform is applicable to users of residents, merchants, cleaning staff, delivery users, supervision users, etc., without limitation, any user in the cell can be a delivery user or a supervision user, and any user can load a client or an APP in an electronic device, etc., without limitation, thereby implementing online offline linkage.

Referring to fig. 1A, fig. 1A is a schematic diagram of a system architecture of a method for monitoring delivery of garbage based on a robbery 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, a spam delivery monitoring software server, etc., and may be in communication with a plurality of electronic devices in which clients applied to the reduction of spam source classifications may be loaded. The server may generate a delivery event and send the delivery event to the electronic device.

The garbage delivery monitoring device or the electronic device based on the robbery mode described in the embodiments of the present application may include a smart Phone (such as an Android Mobile Phone, an iOS Mobile Phone, a Windows Phone Mobile 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, including but not limited to the above device, and of course, the garbage delivery monitoring device based on the robbery mode may also be a server.

It should be noted that, the system architecture of the method for monitoring delivery of garbage based on the robbery mode provided by the application is not limited to the one shown in fig. 1A.

Referring to fig. 1B, a flow chart of an embodiment of a method for monitoring delivery of garbage based on a robbery mode according to an embodiment of the present application is shown. The garbage delivery monitoring method based on the robbery mode described in the embodiment comprises the following steps:

101. r rubbish delivery images of a designated area, which are shot by at least one delivery user, are acquired in real time, wherein R is a positive integer.

The designated area may be designated by the user, or may default to a specific spatial range, for example, may be in the same district, office building, or the same street range, which is not limited herein; the delivering users 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 a garbage throwing place in the designated area, each camera can capture images, when the delivering users deliver garbage, the cameras can capture images to obtain a plurality of delivering images, the one or more cameras can be connected with a server, in particular, the server can acquire the delivering images or video images of R delivering users captured by the one or more cameras in the designated area in real time, and the like, wherein R is a positive integer.

Optionally, because the capacity of each dustbin of the dustbin station is limited, for resource balancing, the server monitors actual load conditions of a plurality of dustbin according to the fact that the client of the user is associated to all the dustbin in the designated area, and the dustbin load conditions of the dustbin station can comprise at least one of the following: space overload, about half of the space remaining, about one third of the space remaining, etc., without limitation; the delivery status may include at least one of: delivery behavior is accurate, delivery behavior is wrong, dustbin load conditions of the dustbin are not limited herein; delivery behavior accuracy may include at least one of: the garbage bags are completely packaged, correctly packaged and the like, and are not limited herein; the delivery performance error may include at least one of: the error of the garbage bag packaging, the damage of the garbage bag packaging and the like are not limited herein; in a specific implementation, the delivery user can photograph the delivery garbage and upload the photographed picture to the client, the server can obtain 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 online.

Optionally, when the delivering user needs to deliver the garbage, the server may obtain an actual load condition of the current garbage can, where the garbage can load condition may include at least one of: space overload, about half of the space remaining, about one third of the space remaining, etc., without limitation; if the actual load condition of the current dustbin is space overload, the client can push a delivery reminder to a delivery user, push other dustbin closest to the current dustbin in position to the delivery user, and provide a path planning path, wherein the delivery reminder can comprise at least one of the following: the dustbin is full, the dustbin is not deliverable, etc., and is not limited in this regard.

102. And analyzing the R garbage delivery images, determining the delivery behaviors of the at least one delivery user, and generating R delivery events.

Wherein the server can analyze the R spam images to obtain R delivery behaviors of different delivery users, the delivery behaviors can include at least one of: the trash station, undelivered trash bags, delivered trash bags, etc., are not limited herein, and the server may automatically generate a delivery event for each delivery event based on the delivery events described above, which may be communicated between the server and the electronic device or client.

Optionally, in the step 102, the analyzing the R garbage delivery images to determine the delivery behavior of the at least one delivery user may include the following steps:

21. Image segmentation is carried out 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 human face image;

23. Performing de-duplication 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.

After capturing or shooting by one or more cameras in a designated range, R garbage delivery images can be obtained, and the R garbage delivery images may contain face images, character images or scene images of different delivery users, and the delivery users may be provided with garbage bags or other pockets when delivering garbage, so that after the server obtains R garbage delivery images of a designated area, which are shot by at least one delivery user, the R garbage delivery images can be subjected to image segmentation to obtain at least one target image, wherein the target image may comprise a human body image or a garbage bag image.

In the specific implementation, the method can be used for framing or marking the (one or more) person foreground in each rubbish delivery image, and if the person foreground image does not exist in the rubbish delivery image, the rubbish delivery image can be directly removed; if a person foreground image exists in the rubbish delivery image, in order to identify a human body image and a rubbish bag image, modeling can be carried out on the person foreground and the background respectively, each pixel in the rubbish delivery image can be connected with a person foreground or background node, and if two adjacent nodes do not belong to the same person foreground or background, edges between the two nodes can be cut off, so that the person foreground image and the 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 personal 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 therefore, the image segmentation method can be used for eliminating the interference of the background information in the garbage delivery image, and the human body recognition efficiency is improved.

In addition, in order to distinguish the identities of different people in the human body images, at least one human body image in the at least one target image can be subjected to face recognition to obtain at least one human face image, and the human face image can be the same person or different human face images, so that the at least one human face image can be subjected to de-duplication processing to obtain at least one human face image of different delivery users, and 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 is obtained.

Furthermore, each target human body image may include a human body image of a user, and the human body image may be identified to obtain a target limb behavior, where in this embodiment, the target limb behavior may include at least one of the following: walking, jumping, squatting, throwing, picking up things, etc., without limitation. In a specific implementation, at least one target human body image may be input into a preset neural network model to obtain at least one limb behavior, the preset neural network model may be set by a user or default by the system, for example, a convolutional neural network model, and finally, the delivery behavior of the delivery user may be judged through the garbage bag image, the target limb behavior and the face image, for example, the delivery behavior of the delivery user may be determined as the user a through face recognition, the behavior of the user a throwing the garbage bag may be identified through the target limb behavior and the garbage bag image, and finally, the delivery behavior of the delivery user may be determined through the above method.

103. And sending the R delivery events to the electronic equipment of M supervision users in the appointed area, wherein M is a positive integer.

The supervision user can be understood as a user group in which the supervision constraint of the client is installed and downloaded in the same cell or a designated area, and the supervision user can be any user in which the client is downloaded, that is, the supervision user can also be a delivery user, and the delivery user can also be a supervision user, so that the garbage delivery behavior of the delivery user can be monitored in real time, and after the server generates a delivery event of the delivery user, the server can send the generated delivery event to the electronic device or the client of the supervision user in the same cell or the designated area in a broadcast mode.

104. And determining S target supervision users with successful robbing according to a preset robbing algorithm, wherein S is a positive integer less than or equal to M.

The preset order-robbing algorithm can be set by a user or defaulted by the system, the preset order-robbing algorithm can be changed at any time, the server can send delivery events to M supervision users in the same cell or a designated area in a broadcasting mode, after receiving the delivery events, the M supervision users can rob orders in a client, the order-robbing algorithm can be understood as the free order-robbing delivery events of the supervision users, if the order-robbing is successful, the order-robbing algorithm is considered to be successful, after the order-robbing is successful, S target supervision users with the success of the order-robbing can be determined, in particular implementation, the same order can be robbed by a plurality of supervision users, or the order-robbing is carried out by a plurality of supervision users, for example, when the supervision user B and the supervision user C rob orders, if the response time of the supervision user B robbing the order-robbing is shortest, the order-robbing of the supervision user B is considered to be successful, and the entness of a supervision person or a delivery person can be increased when the garbage is processed.

Optionally, in step 104, the determining S target supervising users who successfully rob a bill according to a preset bill robbing algorithm may include the following steps:

411. acquiring the order-robbing influence factors of the M supervising users, wherein the order-robbing influence factors comprise response time X for successful order-robbing, position parameters Y and number Z of received orders of the supervising users, and the position parameters Y are distances between the current positions of the supervising users and the garbage delivery sites;

412. Acquiring a weight factor corresponding to the robbery factor, wherein the weight factor of response time is a, the weight factor of the position parameter is b, the weight factor of the number Z of received monomers is c, wherein a+b+c=1, and the values of a, b and c are all 0-1;

413. obtaining the order taking value of the supervising user according to a preset weighted calculation formula, wherein the order taking value is as follows:

wherein Z _max is the maximum value of the number of the supervising user connected, and Z _max is a positive integer;

414. And determining S target supervision users with successful robbery according to the robbery values of the M supervision users, wherein the higher the robbery value is, the higher the probability of becoming the target supervision user is.

When a supervising user robs a bill, a plurality of robbery bill influencing factors can influence the robbery bill condition of the supervising user, the robbery bill influencing factors can be set by the user or default by the system, and the robbery bill influencing factors can comprise at least one of the following components: presetting the time of order taking, the response time of order taking success, position parameters (the distance between the current position of a supervising user and a garbage delivery site or garbage station), prescribing the time of order taking, prescribing the maximum number of available orders, the number of ordered orders and the like, wherein the number of ordered orders can be understood as the number of ordered orders which are successful, and the order taking success indicates the success of order taking; in specific implementation, weight factors can be preset for different order-taking influence factors, the magnitude of the weight factors can be understood as the magnitude of importance of the order-taking influence factors, the larger the weight factors are, the larger the influence of order-taking success can be understood as, the possibility of order-taking success of different supervision users can be determined through the order-taking influence factors and the weight values of the order-taking influence factors, the possibility or probability of order-taking success of the supervision users (target supervision users) can be represented by the order-taking values, the larger the order-taking values are, the probability or probability of order-taking success of the target supervision users is determined according to the order-taking values of the supervision users.

For example, the preset order-taking influencing factors include response time X of order-taking success, a position parameter Y and an accepted number Z, the preset weight factor of the response time of order-taking success is a, the weight factor of the position parameter is b, the weight factor of the accepted number Z is c, wherein a+b+c=1, the values of a, b and c are all 0-1, and the preset weight calculation formula can obtain the order-taking value of the supervising user as follows:

The maximum number of orders received by the supervising user can be indicated as Z _max, Z _max is a positive integer, the higher the order-picking value is, the larger the probability of the supervising user is, the formula shows that the order-picking influencing factor with the maximum influence is the number of orders received, the maximum number of orders can be preset, if the maximum number of orders received is exceeded, the supervising person cannot receive the orders again or fails to pick up the orders, and the other influencing factor is a position parameter, if the position parameter is smaller, the supervising person is closer to the garbage delivery place, so that the efficiency of the supervising delivery person can be improved, and the order-picking machine can be maximally distributed to different supervising persons.

421. obtaining M response times X _i of successful robbery of the M supervising users, wherein each supervising user corresponds to one robbery response time, i is a positive integer, and i is smaller than or equal to M;

422 determining N supervising users with response time X _i within a first preset threshold interval according to the M response times X _i, 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 greater than 1, determining Q supervising users with the number Z _j of received orders in the interval [0, Z _max), wherein j is a positive integer, and j is less than or equal to N;

425. Acquiring Q position parameters Y _k of the Q supervising users and the number Z _k of received orders of the Q supervising users, wherein k is a positive integer and is smaller than or equal to N;

426. according to a preset order-taking algorithm, determining the order-taking value of each supervision user in the Q supervision users as follows:

P_k＝a*X_k+b*Y_k+c*Z_k；

427. s monitoring users with the order taking values within a second preset threshold interval in the Q monitoring users are determined to be target monitoring users, wherein S is a positive integer smaller 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 allowed response time of maximum successful preemption, for example, may be set to [2s,100s ]; the second preset threshold interval is set by the user or the system defaults, the order taking value of the supervision user is within the second preset threshold interval and can become the target supervision user, and at least one target supervision user with successful order taking can be determined according to the preset order taking algorithm, and the target supervision user is the supervision personnel with successful order taking.

In a specific implementation, M response times X _i of the robbery of M supervising users can be obtained, wherein each supervising user corresponds to one robbery response time, i is a positive integer, i is less than or equal to M, M is a positive integer, X _i can be expressed as the robbery response time of the ith supervising user, N supervising users with the response time X _i within a first preset threshold interval can be determined according to the M response times X _i, N is a positive integer less than or equal to M, if N is 1, it can be understood that only the robbery response time of one supervising user is within the preset first threshold interval, and in order not to waste the supervising opportunity, the supervising user can be determined as a target supervising user; if N is greater than 1, determining whether the supervising user can continue to order according to the number of ordered sheets of N users, determining Q supervising users of the N supervising users having the number Z _j of ordered sheets in the interval [0, Z _max ], where j is a positive integer, j is less than or equal to N, Z _j may be represented as the number of ordered sheets of the j supervising user, determining a user that may become a target supervising user (success of ordering) according to the positions of the Q supervising users from the garbage placement location, acquiring Q position parameters Y _k of the Q supervising users and the number Z _k of ordered sheets of the Q supervising users, where k is a positive integer, k is less than or equal to N, Y _k may be represented as the position parameter of the k supervising user, and finally determining that the order value of each of the Q supervising users is:

P_k＝a*X_k+b*Y_k+c*Z_k；

And finally, determining S supervision users with the order taking values within a second preset threshold interval from the Q supervision users as target supervision users, wherein S is a positive integer less than or equal to Q, and P _k can be expressed as the order taking value of the kth supervision user.

105. And acquiring R garbage delivery sites for reporting the R delivery events to the server, and pushing the R garbage delivery sites to the S target monitoring users.

The server can acquire R garbage throwing places where the R delivery events are reported by cameras or other shooting devices, and send the R garbage throwing places to the electronic equipment of the target supervision user, so that the target supervision user can find the garbage throwing place of the throwing user according to the garbage throwing places, and supervision measures can be conveniently taken.

Optionally, the supervising user or the delivering user may load a client or APP for garbage classification decrement in the electronic device, so as to realize online-offline linkage, in a specific implementation, the client may obtain a picture taken by the delivering user before garbage delivery, and upload the picture to a server, the server may obtain a picture uploaded by the delivering user, perform feature extraction on the picture, 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 by himself or default, and the preset feature points may be extracted from a sampling picture taken in a preset designated area, the preset designated area may be set by the user by himself or default by default, after the matching, obtain a matching degree between the feature points and the preset feature points, and if the matching degree exceeds a preset threshold, consider that a photographing place of the picture is consistent with a photographing place in the sampling picture, and also understand that a location parameter is consistent, so as to determine a specific location of the garbage throwing place of the user, and send the specific feature point to a preset target line, and obtain a detailed garbage throwing place by the client, and the specific point may also take a detailed measure by the user's place.

Optionally, in step 105, the pushing the R garbage delivery points to the S target supervising users may include the following steps:

51. Acquiring the number Z _w of received orders of the S target supervision users, wherein w is a positive integer and is smaller than S, and Z _w represents the number of received orders of the w target supervision users;

52. According to the number Z _w of received orders and the maximum number Z _max of received orders of the S target monitoring users, the remaining number of received orders of the w monitoring users in the S target monitoring users is determined as follows: z _max-Z_w;

53. Obtaining the R garbage delivery sites, wherein R is a positive integer;

54. Determining the priorities of the S target supervision users according to the order taking values of the S target supervision users, wherein the higher the order taking value is, the higher the priority is;

55. if it is If the number is greater than or equal to R, pushing the R garbage delivery sites according to the priorities of the S target supervision users and the residual order receiving numbers of the S target supervision users;

56. If it is If the number is smaller than R, pushing/>, according to the priorities of the S target supervision users and the residual order receiving numbers of the S target supervision usersAnd (5) a garbage delivery site.

The method comprises the steps of pushing garbage places according to the number of the remaining orders of target supervision users, so that the maximum utilization supervision effect can be achieved, and in particular implementation, the number of the remaining orders of each target supervision user can be calculated through the number of the ordered orders, and then the garbage delivery places are pushed according to the number of the remaining orders.

For example, the number Z _s of received orders for S target supervising users may be obtained, where S is a positive integer and is less than S, and the remaining number of received orders for the w-th supervising user in the S target supervising users is determined according to the number Z _w of received orders for the S target supervising users and the maximum number Z _max of received orders: z _max-Z_w, then, R garbage delivery sites can be obtained, wherein R is 0 or a positive integer, and the priorities of the S target supervision users are determined according to the order taking values of the S target supervision users, wherein the larger the order taking value is, the higher the priority is can be set; if it isIf the number is greater than or equal to R, pushing R rubbish delivery sites according to the priorities of S target supervision users and the number of the residual orders received by the S target supervision users, namely, the priority is high or low, and pushing rubbish delivery sites according to the number of the residual orders received by each supervision user preferentially until pushing is completed; if it isIf the number is smaller than R, pushing/>, according to the priorities of S target supervision users and the number of the residual orders received by S target supervision usersA separate rubbish delivery site; therefore, the garbage throwing place can be pushed to the maximum utilization, and the supervision effect of a supervision user is improved.

Optionally, in step 56, pushing is performed according to the priorities of the S target supervisory users and the remaining order receiving numbers of the S target supervisory usersAfter the individual refuse delivery sites, the method further comprises the following steps:

57. Monitoring the uploading state of the supervision data of the S target supervision users;

58. When the uploading state of any one target supervision user in the S target supervision users is monitored to be the uploaded state, pushing the rest One of the rubbish delivery sites until the remaining rubbish delivery sites are pushed.

Wherein, in pushingAfter the individual garbage delivery sites, there remains/> The server may monitor the upload status of the supervision data of the S target supervision users, where the supervision data may include at least one of the following: text, images, etc., not limited herein, and when any one target supervising user is monitored to have uploaded the supervising data, it continues to push a/>, theretoAnd (3) circulating the garbage throwing places to know that the rest garbage throwing places are pushed.

106. And monitoring the uploading states of the S pieces of supervision data of the S target supervision users in a preset time threshold interval.

The preset time threshold interval can be set by a user or default by a system, for example, the server can monitor the electronic equipment of the target supervision user in real time within [1h,15h ] after the supervision user successfully robs a bill, and acquire the uploading state of the supervision data of the target supervision user; the supervision data in the embodiment of the present application may be understood as a picture or text shot by a supervision user, and after the supervision user shoots the picture, the supervision data may be uploaded to a server through a client, where the text may include at least one of the following: the delivery behavior is accurate, the delivery behavior is wrong, the package integrity of the garbage bag, the split charging of the garbage bag is wrong, the split charging of the garbage bag is correct, the load condition of the garbage bin of the garbage station, the package damage of the garbage bag and the like are not limited herein; the upload status may include at least one of: the uploaded state, the not uploaded state, being uploaded, etc., are not limited herein, and thus, a supervising user may be given a certain time space to take a picture of and upload the supervising data, and acquire its uploaded state in real time.

Optionally, because the capacity of each dustbin of the dustbin station is limited, for resource balancing, the server monitors actual load conditions of a plurality of dustbin according to the fact that the client of the user is associated to all the dustbin in the designated area, and the dustbin load conditions of the dustbin station can comprise at least one of the following: the space overload, about half of the space remaining, about one third of the space remaining, and the like, which are not limited herein, in a specific implementation, after the at least one supervising user uploads the supervising data, the actual load condition of the at least one dustbin in the designated area can be obtained according to the load condition of the dustbin station in the at least one supervising data, thereby realizing on-line and off-line monitoring of a plurality of the dustbin in the designated area.

107. And generating S supervision records according to the uploading states of the S supervision data of the S target supervision users, wherein the supervision data uploaded by each target supervision user corresponds to one supervision record.

After the server acquires the upload state of the supervision data of any one of the supervision users, a supervision record may be generated, and the supervision record may include the supervised or unsupervised, for example, the supervision user may understand that the supervision user has uploaded the supervision data, thus playing a role in supervising the supervision work of the supervision user.

108. And determining the score of each target supervision user in the S target supervision users based on a preset scoring algorithm according to the S supervision data of the S target supervision users and the S supervision records.

The score of each target supervision user in the S target supervision users can be determined based on a preset scoring algorithm according to the supervision data of the S target supervision users and the R supervision records, so that the supervision work of the supervision users can be better evaluated.

Optionally, after determining the score of each of the S target supervisory users, the score obtained by any one of the S target supervisory users may be directly uploaded to a task record of the supervisory user, and a certain score may be added each time a task record is added, and the final score may be used for exchanging different gifts or for consumption, so that the enthusiasm of the supervisory user may be improved.

Optionally, in step 108, the determining the score of each of the S target supervising users based on the preset scoring algorithm according to the S supervising data of the S target supervising users and the S supervising records may include the following steps:

81. the S pieces of supervision data uploaded by the S target supervision users are obtained, wherein the supervision data comprises: the system comprises characters and images, wherein the characters comprise accurate delivery behaviors, delivery behavior errors and garbage bag packaging integrity;

82. Performing image recognition on the image to obtain a delivery state of the delivery user, wherein the delivery state comprises accurate delivery behavior, delivery behavior errors 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 degree in different preset threshold intervals and second weight factors corresponding to supervision records of the target supervision users, wherein the supervision records comprise supervised and unsupervised;

85. And multiplying according to the first weight factor and the second weight factor to obtain the score of each target supervision user in the S target supervision users.

Wherein, after the supervision user uploads the supervision data, the supervision data may include images and text, and the text may be understood as an evaluation description of the delivery behavior of the delivery user by the supervision user, for example, the text may include at least one of the following: the delivery behavior is accurate, the delivery behavior is wrong, the package integrity of the garbage bag, the split charging of the garbage bag is wrong, the split charging of the garbage bag is correct, the load condition of the garbage bin of the garbage station, the package damage of the garbage bag and the like are not limited herein; the server may acquire the supervision data and perform image recognition on the image in the supervision data, and in a specific implementation, may obtain a delivery status of the delivery user in the image based on a preset neural network model, where the delivery status may include at least one of the following: delivery behavior is accurate, delivery behavior is wrong, dustbin load conditions of the dustbin are not limited herein; delivery behavior accuracy may include at least one of: the garbage bags are completely packaged, correctly packaged and the like, and are not limited herein; the delivery performance error may include at least one of: the error of the garbage bag packaging, the damage of the garbage bag packaging and the like are not limited herein; then, the delivery state of the delivery user identified by the image and the text of the supervising user can be matched to obtain the matching degree between the delivery state 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 of 60 percent and 70 percent is set to be 0.2, the weight factor with the matching degree of 80 percent and 90 percent is set to be 0.5, a second weight factor corresponding to different supervising records can be preset, the sum of the supervised weight factors and the unsupervised weight factor is 1, for example, the supervised weight is 0.8, the unsupervised weight factor is 0.2, and finally, the score of each target supervising user in S target supervising users can be obtained by multiplying the first weight factor and the second weight factor.

Optionally, the server may obtain a delivery status entered by at least one delivery user or a picture uploaded by the delivery user, obtain a 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 multiple matching degrees, and obtain scores of the delivery user and the supervising user according to scores corresponding to preset different matching degree intervals, for example, the matching degree may be set to be 1 score for [60%,70% ] and 2 score for [80%,90% ], and if the obtained matching degree is 65%, the supervising user and the delivery user add a score respectively, and at the same time, synchronize the obtained scores to task records in clients of the supervising user and the delivery user, and the final score may be used for different gifts or consumption, so as to improve the enthusiasm of the user for spam delivery and spam monitoring.

According to the embodiment of the application, R junk delivery images shot for at least one delivery user in a designated area can be obtained in real time, the R junk 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 M pieces of electronic equipment of the designated area, S pieces of target supervision users with successful robbing are determined according to a preset robbing algorithm, R junk delivery sites reporting the R delivery events to a server are obtained, R junk delivery sites are pushed to the S pieces of electronic equipment of the target supervision users, the uploading state of the S pieces of supervision data of the S pieces of target supervision users is monitored within a preset time threshold interval, S pieces of supervision records are generated according to the uploading state of the S pieces of supervision data of the S pieces of target supervision users, the supervision data uploaded by each target supervision user corresponds to one supervision record, the supervision data of the S pieces of target supervision users and the S pieces of supervision records are determined based on the preset scoring algorithm, and therefore, the waste of the monitoring data of the S pieces of target supervision users can be avoided based on the single delivery modes of the designated delivery users.

In accordance with the foregoing, please refer to fig. 2, which is a schematic flow chart of an embodiment of a method for monitoring delivery of garbage based on a robbery-to-order mode according to an embodiment of the present application. The garbage delivery monitoring method based on the robbery 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 carrying out face recognition on at least one human body image in the at least one target image to obtain at least one human face image.

203. And performing de-duplication 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 behaviors of the at least one delivery user according to the target limb behaviors, 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 the electronic equipment of M supervision users in the appointed area, wherein M is a positive integer.

207. And determining S target supervision users with successful robbing according to a preset robbing algorithm, wherein S is a positive integer less than or equal to M.

208. And acquiring R garbage delivery sites for reporting the R delivery events to the server, and pushing the R garbage delivery sites to the S target monitoring users.

209. And monitoring the uploading state of the supervision data of the S target supervision users in a preset time threshold interval.

210. And generating S supervision records according to the uploading states of the S supervision data of the S target supervision users, wherein the supervision data uploaded by each target supervision user corresponds to one supervision record.

211. And determining the score of each target supervision user in the S target supervision users based on a preset scoring algorithm according to the S supervision data of the S target supervision users and the S supervision records.

Optionally, the specific description of the above steps 201 to 211 may refer to the corresponding steps of steps 101 to 108 of the robbery-mode-based rubbish delivery monitoring method described in fig. 1B, which are not described herein.

It can be seen that, according to the embodiment of the application, image segmentation is performed on 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 at least one target image to obtain at least one human face image; performing de-duplication 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 delivery behaviors of at least one delivery user according to the target limb behaviors, at least one target face image and the garbage bag image, and generating R delivery events; the R delivery events are sent to the electronic equipment of M supervision users in the appointed area; s target supervision users with successful robbery are determined according to a preset robbery algorithm; r delivery events are reported to R rubbish delivery sites of a server, R rubbish delivery sites are pushed to electronic equipment of S target supervision users, and uploading states of supervision data of the S target supervision users are monitored in a preset time threshold interval; generating S supervision records according to the uploading states of the supervision data of the S target supervision users; according to the supervision data and the S supervision records of the S target supervision users, the score of each target supervision user in the S target supervision users is determined based on a preset scoring algorithm, so that the interaction between the server and the electronic equipment and between the server and the users can be realized through the robbery mode while the delivery behavior of the delivery users is determined through the method, and finally, the monitoring consciousness of the supervision users is improved through the robbery mode.

In accordance with the above, the following is a device for implementing the above-mentioned method for monitoring delivery of garbage based on robbery-ticket mode, which specifically comprises the following steps:

referring to fig. 3, an embodiment structure diagram of a garbage delivery monitoring device based on a robbery mode according to an embodiment of the present application is shown. The garbage delivery monitoring device based on the robbery mode described in the embodiment comprises: the acquisition unit 301, the analysis unit 302, the transmission unit 303, the determination unit 304, the monitoring unit 305, and the generation unit 306 are specifically as follows:

An acquiring unit 301, configured to acquire R rubbish delivery images of a designated area, which are shot for at least one delivery user in real time;

An analysis unit 302, configured to analyze the R garbage 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 specified area;

The determining unit 304 is configured to determine S target supervising users who successfully rob a bill according to a preset bill robbing algorithm;

The acquiring unit 301 is further configured to acquire R rubbish delivery sites that report the R delivery events to the server, and push the R rubbish delivery sites to the S target supervising users;

a monitoring unit 305, configured to monitor the uploading states of S pieces of supervision data of the S pieces of target supervision users within a preset time threshold interval;

The generating unit 306 is configured to generate S supervision records according to the upload states of the S supervision data of the S target supervision users, where the supervision data uploaded by each target supervision user corresponds to one supervision record;

The determining unit 304 is further configured to determine a score of each of 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.

It can be seen that, through the spammer monitoring device based on the robber mode described in the embodiments of the present application, R spammer images of the designated area for capturing at least one delivery user are obtained in real time, the R spammer images are analyzed, delivery behaviors of the R delivery users are determined, R delivery events are generated, the R delivery events are sent to M electronic devices of the designated area, S target monitoring users with successful robber form are determined according to a preset robber form algorithm, R spammer delivery sites for reporting the R delivery events to a server are obtained, the electronic devices of the S target monitoring users are pushed with R spammer delivery sites, the uploading state of the monitoring data of the S target monitoring users is monitored within a preset time threshold interval, S monitoring records are generated according to the uploading state of the S monitoring data of the S target monitoring users, each target monitoring user corresponds to one monitoring record, the data of the S target monitoring users and the S monitoring records are determined based on the preset robber form algorithm, and the post-conscious score of each target monitoring user can be avoided, and the spammer delivery modes can be avoided.

It can be understood that the functions of each program unit of the garbage delivery monitoring apparatus based on the robbery-to-order mode in this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the relevant description of the foregoing method embodiment, which is not repeated herein.

In accordance with the foregoing, please refer to fig. 4, which is a schematic diagram of an embodiment of a server according to an embodiment of the present application. The server described in the present embodiment includes: at least one input device 1000; at least one output device 2000; at least one processor 3000, such as a CPU; and a memory 4000, the above-described input device 1000, output device 2000, processor 3000, and memory 4000 being connected by a bus 5000.

The input device 1000 may be a touch panel, physical buttons, or a mouse.

The output device 2000 may be a display screen.

The memory 4000 may be a high-speed RAM memory or a nonvolatile memory (non-volatile memory), such as a disk memory. The memory 4000 is used to store a set of program codes, and the input device 1000, the output device 2000, and the processor 3000 are used to call the program codes stored in the memory 4000, performing the following operations:

The processor 3000 is configured to:

Acquiring at least one R rubbish delivery images of a designated area, which are shot by at least one delivery user, in real time, wherein R is a positive integer;

Analyzing the at least one R rubbish delivery images, determining delivery behaviors of the at least one delivery user, and generating at least one R delivery events;

transmitting the at least one R delivery events to at least one M supervision user's electronic device in the designated area, wherein M is a positive integer;

determining at least one S target supervision users with successful robbing according to a preset robbing algorithm, wherein S is a positive integer less than or equal to M;

Acquiring at least one R garbage delivery sites for reporting the at least one R delivery event to the server, and pushing the at least one R garbage delivery sites to the electronic equipment of the at least one S target supervising user;

And determining the score of each target supervision user in the S target supervision users based on a preset scoring algorithm according to the S supervision data of the S target supervision users and the S supervision records.

In one possible example, the processor 3000 is specifically configured to, in analyzing the R rubbish delivery images, determine delivery behaviors of the at least one delivery user, where:

Image segmentation is carried out 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 human face image;

performing de-duplication 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, the above processor 3000 is specifically configured to:

acquiring the order-robbing influence factors of the M supervising users, wherein the order-robbing influence factors comprise response time X for successful order-robbing, position parameters Y and number Z of received orders of the supervising users, and the position parameters Y are distances between the current positions of the supervising users and the garbage delivery sites;

acquiring a weight factor corresponding to the robbery factor, wherein the weight factor of response time is a, the weight factor of the position parameter is b, the weight factor of the number Z of received monomers is c, wherein a+b+c=1, and the values of a, b and c are all 0-1;

the order taking values of the M supervising users are obtained through calculation according to the following formula:

Wherein Z _max is the maximum value of the number of the received M supervising users, and Z _max is a positive integer;

And determining S target supervision users with successful robbery according to the robbery values of the M supervision users, wherein the higher the robbery value is, the higher the probability of becoming the target supervision user is.

Obtaining M response times X _i of successful robbery of the M supervising users, wherein each supervising user corresponds to one robbery response time, i is a positive integer, and i is smaller than or equal to M;

According to the M response times X _i, N supervising users of the response time X _i in a first preset threshold interval are determined, 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 greater than 1, determining Q supervising users with the number Z _j of received orders in the interval [0, Z _max), wherein j is a positive integer, and j is less than or equal to N;

Acquiring Q position parameters Y _k of the Q supervising users and the number Z _k of received orders of the Q supervising users, wherein k is a positive integer and is smaller than or equal to N;

According to a preset order-taking algorithm, determining the order-taking value of each supervision user in the Q supervision users as follows:

P_k＝a*X_k+b*Y_k+c*Z_k；

s monitoring users with the order taking values within a second preset threshold interval in the Q monitoring users are determined to be target monitoring users, wherein S is a positive integer smaller than or equal to Q.

Acquiring the number Z _w of received orders of the S target supervision users, wherein w is a positive integer and is smaller than S, and Z _w represents the number of received orders of the w target supervision users;

According to the number Z _w of received orders and the maximum number Z _max of received orders of the S target monitoring users, the remaining number of received orders of the w monitoring users in the S target monitoring users is determined as follows: z _max-Z_w;

obtaining the R garbage delivery sites, wherein R is a positive integer;

determining the priorities of the S target supervision users according to the order taking values of the S target supervision users, wherein the higher the order taking value is, the higher the priority is;

If it is If the number is greater than or equal to R, pushing the R garbage delivery sites according to the priorities of the S target supervision users and the residual order receiving numbers of the S target supervision users;

If it is If the number is smaller than R, pushing/>, according to the priorities of the S target supervision users and the residual order receiving numbers of the S target supervision usersAnd (5) a garbage delivery site.

In one possible example, after the priorities of the S target supervisory users and the remaining orders of the S target supervisory users, the above processor 3000 is specifically further configured to:

Monitoring uploading states of the supervision data of the S target supervision users, wherein the uploading states comprise uploaded states or non-uploaded states;

When the uploading state of any one target supervision user in the S target supervision users is monitored to be the uploaded state, pushing the rest One of the rubbish delivery sites until the remaining rubbish delivery sites are pushed.

In one possible example, in terms of S supervision data of the S target supervision users and the S supervision records, determining the score of each of the S target supervision users based on a preset scoring algorithm, the processor 3000 is specifically configured to:

the S pieces of supervision data uploaded by the S target supervision users are obtained, wherein the supervision data comprises: the system comprises characters and images, wherein the characters comprise accurate delivery behaviors, delivery behavior errors and garbage bag packaging integrity;

performing image recognition on the image to obtain a delivery state of the delivery user, wherein the delivery state comprises accurate delivery behavior, delivery behavior errors 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 degree in different preset threshold intervals and second weight factors corresponding to supervision records of the target supervision users, wherein the supervision records comprise supervised and unsupervised;

And multiplying according to the first weight factor and the second weight factor to obtain the score of each target supervision user in the S target supervision users.

The embodiment of the application also provides a computer storage medium, wherein the computer storage medium can store a program, and the program can be executed by the computer storage medium, and the program comprises part or all of the steps of any one of the garbage delivery monitoring methods based on the robbery bill mode.

Although the application is described herein 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 study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "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.

It will be apparent to those skilled in the art that 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 may be stored/distributed on a suitable medium supplied together with or as part of other hardware, but may also take other 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A rubbish delivery monitoring method based on a robbery list mode is applied to a server and is characterized by comprising the following steps:

2. The method of claim 1, wherein analyzing the R spam images to determine delivery behavior of the at least one delivery user comprises:

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 and controlling delivery of rubbish according to claim 1, wherein the step of determining S target monitoring users who successfully rob a bill according to a preset bill robbing algorithm comprises:

4. The method for monitoring delivery of spam according to claim 1, wherein determining S target supervising users who successfully rob a bill according to a preset bill robbing algorithm comprises:

if N is 1, determining the N supervising users as the S target supervising users;

P_k＝a*X_k+b*Y_k+c*Z_k；

the weight factor of response time X _k of the kth supervising user in the Q supervising users is a, the weight factor of position parameter Y _k of the kth supervising user is b, the weight factor of the number Z _k of received orders of the kth supervising user is c, wherein a+b+c=1, and the values of a, b and c are all 0-1;

5. The method of claim 1-4, wherein said pushing the R rubbish delivery sites to the S target supervising users comprises:

obtaining the R garbage delivery sites, wherein R is a positive integer;

6. The spam monitoring method of claim 5, wherein pushing occurs at the time of the step of receiving the S target supervisory users' remaining order number based on the priority of the S target supervisory usersAfter the individual rubbish delivery sites, the method further comprises:

7. The method for monitoring spam delivery according to claim 1, wherein determining the score of each of the S target supervising users based on a preset scoring algorithm based on the S supervising data of the S target supervising users and the S supervising records comprises:

8. The utility model provides a rubbish delivery monitoring device based on rob single mode which characterized in that includes:

The acquisition unit is used for acquiring R rubbish delivery images of the designated area, which are shot by at least one delivery user in real time;

The acquisition unit is further used for acquiring R garbage delivery sites for reporting the R delivery events to a server and pushing the R garbage delivery sites to the S target supervision users;

the monitoring unit is used for monitoring the uploading state of the supervision data of the S target supervision users in a preset time threshold interval;

The generation unit is used for generating S supervision records according to the uploading states of the supervision data of the S target supervision users, wherein the supervision data uploaded by each target supervision user corresponds to one supervision record;

The determining unit is further configured to determine a score of each of the S target supervising users based on a preset scoring algorithm according to the S target supervising users' supervising data and the S supervising 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.