KR20240053761A - Food waste collection clustering method - Google Patents

Abstract

The food waste collection method collects analysis data on the food waste generation status in a specific area and determines the amount of food waste for each building by collecting analysis data on the food waste emissions analysis unit and using the legal area information from the analysis data to determine the specific area at the legal dong boundary. Accordingly, it is divided into a plurality of zones and includes a zone-specific collection difficulty calculation unit that calculates the food waste collection difficulty for each zone.

Description

Food waste collection method{FOOD WASTE COLLECTION CLUSTERING METHOD}

The present invention relates to a method of collecting food waste, and more specifically, to reduce the driving distance for food waste collection by about half and to operate efficiently in terms of time and economy when collection is delayed due to a lack of collection workers. It's about collection method.

Conventional technologies related to the present invention include route optimization, collection service, address-based collection, and collection dispatch. Patent Document 1 Path optimization system and method calculates the optimal path based on passage time information and collection time information for each area.

However, the prior art has the problem of not being able to input new food waste collection, confirm specific food waste collection, calculate the shortest circuit route, and complete the end input.

Registered Patent Publication No. 10-2316149 Path optimization system and method

The purpose of the present invention is to provide a food waste collection method that inputs new food waste collection, confirms a specific food waste collection, calculates the shortest circuit route, and ends input.

Another object of the present invention is to provide a food waste collection method that clusters food waste collection and optimizes the collection route to shorten the collection distance.

In the food waste collection method according to a preferred embodiment of the present invention, the real-time food waste collection device includes a food waste discharge analysis unit that collects analysis data on the food waste generation status in a specific area and determines the food waste discharge amount for each building; It includes a zone-specific collection difficulty calculation unit that divides the specific area into a plurality of zones according to the legal district boundary using the legal area information of the analysis data and calculates the food waste collection difficulty for each zone, and the control unit 5 is configured to collect new food waste. A new food waste collection input step for inputting collection (S21); A specific food waste collection confirmation step (S22) in which specific food waste collection is confirmed; Shortest traversal path calculation step (S23) for calculating the shortest traversal path; and an end input termination step (S24) that ends when the end is input.

In addition, in step S21, the control unit 5, when a new collection order is entered, receives all data of the new collection order; Storing the two-way distance data obtained through the pathfinding algorithm and traversing n clusters that already exist; For each cluster, calculating the length of the “shortest traversal path inside the cluster” when a new collection order is included in the cluster minus the existing collection distance, and storing the maximum of these values in max_effective_distance; If max_effective_distance is more than strictness * (existing distance) + bias, including the new collection order in a cluster with max_cluster_id and recording max_cluster_id in the new collection order data; The “shortest traversal path inside the cluster” of the cluster is confirmed, and if the “immediate matching condition function” that takes the cluster as an input returns true, all orders included in the cluster are confirmed and all collection identification values of the cluster are confirmed. outputting them in order of the shortest traversal path within the cluster; Deleting all orders and clusters included in the cluster, and deleting two-way distance data with all order collection points included in the cluster; And if max_effective_distance is less than strictness * (existing distance) + bias, create a cluster containing only new collection orders, save the cluster, record the identification value of the cluster in the new collection order data, and enter the standby state. It is characterized by performing a return step.

In addition, in step S22, the control unit 5 includes the following steps: when a specific collection order needs to be confirmed, inputting an identification value of the collection order to be confirmed; For a cluster containing a collection order with a corresponding identification value, confirming all orders included in the cluster and outputting all collection identification values of the cluster in order of the shortest traversal path within the cluster; and deleting all orders and clusters included in the cluster, and deleting two-way distance data with all order collection points included in the cluster.

In addition, in the above step (S23), the control unit 5 performs the “shortest traversal path inside the cluster” calculation method, sets the collection point shared by the collection orders of the cluster as the starting vertex, and creates a complete graph using the distance data. , using the DP bitmasking used to solve the Travel Salesman's Problem, but pre-marking the departure point bit of the visit list so as not to return to the departure point; and backtracking the path based on the shortest distance information stored in the DP array, and finding the shortest traversal path and its length through this.

Additionally, in step S24, the control unit 5 repeats step S22 for all clusters upon receiving an END input while waiting to confirm all clusters and delete all data; and reaching the waiting time within a finite time and terminating the process of step S24 within a finite time.

The present invention can have the effect of reducing the driving distance by about half, unlike the conventional 1:1 collection, by inputting new food waste collection, confirming specific food waste collection, calculating the shortest circuit route, and terminating the end input.

In addition, the present invention clusters food waste collection and optimizes the collection route to shorten the collection distance, so that when collection is delayed due to a shortage of collection technicians, it can operate efficiently in terms of time and economy.

1 is a flowchart showing the operation of the real-time food waste collection method of the present invention.
Figure 2 is a flowchart showing the main operation of the present invention.
Figure 3 is a flowchart showing the pop-all operation of the present invention.
Figure 4 is a flowchart showing the pop operation of the present invention.
Figure 5 is an exemplary diagram showing a cluster of the present invention.
Figure 6 is an exemplary diagram illustrating a configuration for verifying data errors for illustrating the present invention.
Figure 7 is an example diagram illustrating the hardware resources, operating system, operation of the core control unit, and system authentication configuration that grants authority to execute the control unit operation to explain the present invention.

Hereinafter, a food waste collection method according to an embodiment of the present invention will be described in detail with reference to the drawings. Below, descriptions of previously known matters are omitted or simplified to clarify the gist of the present invention. The components included in the description of the present invention operate individually or in combination.

Figure 1 is a flow chart showing the operation of the real-time food waste collection method of the present invention. Referring to Figure 1, the control unit 5 performs a new food waste collection input step (S21) of inputting a new food waste collection; A specific food waste collection confirmation step (S22) in which specific food waste collection is confirmed; Shortest traversal path calculation step (S23) for calculating the shortest traversal path; An end input termination step (S24), which ends when the end is input, is performed.

The real-time food waste collection device collects analysis data on the food waste generation status in a specific area and determines the amount of food waste for each building by collecting analysis data on the food waste emissions analysis unit, and uses the legal area information of the analysis data to demarcate the specific area as a legal dong. It may be divided into a plurality of zones and include a zone-specific collection difficulty calculation unit that calculates the food waste collection difficulty for each zone.

In step S21, when a new collection order is entered, the control unit 5 receives all data of the new collection order. At this time, the distance between the collection point of the new collection order and the delivery point already known by the algorithm, and the distance between the collection point of m collection orders are obtained through the route finding algorithm.

The control unit 5 stores two-way distance data obtained through a route finding algorithm. The control unit 5 iterates over n clusters that already exist. A cluster is an object that contains one or more collection orders with the same shipping address.

For each cluster, the control unit 5 calculates the length of the “shortest circulation path inside the cluster” when a new collection order is included in the cluster minus the existing collection distance, and stores the maximum of these values in max_effective_distance. At this time, the identification value of the cluster that maximizes max_effective_distance is also stored in max_cluster_id.

If max_effective_distance is more than strictness * (existing distance) + bias, the control unit 5 includes the new collection order in the cluster with max_cluster_id and records max_cluster_id in the new collection order data.

The control unit 5 determines the “shortest traversal path within the cluster” of the corresponding cluster. If the “immediate matching condition function” that takes the corresponding cluster as an input value returns true, the control unit 5 determines all orders included in the corresponding cluster and outputs all collection identification values of the cluster in order of the shortest traversal path within the cluster. do.

Afterwards, the control unit 5 deletes all orders and clusters included in the cluster, and deletes two-way distance data with all order collection points included in the cluster. Return to standby state.

If max_effective_distance is smaller than strictness * (existing distance) + bias, the control unit 5 creates a cluster containing only new collection orders, stores the cluster, and records the identification value of the cluster in the new collection order data. Then returns to standby state.

In step S22, when a specific collection order needs to be confirmed, the control unit 5 inputs the identification value of the collection order to be confirmed. The control unit 5 confirms all orders included in a cluster containing a collection order with the corresponding identification value, and outputs all collection identification values of the cluster in order of the shortest traversal path within the cluster.

The control unit 5 deletes all orders and clusters included in the cluster, and deletes two-way distance data with all order collection points included in the cluster. Returns to standby state.

In step S23, the control unit 5 performs the “shortest traversal path inside the cluster” calculation method, sets the collection point shared by the collection orders of the cluster as the starting vertex, creates a complete graph using the distance data, and solves the Travel Salesman's Problem. Use DP (Dynamic Programming) bitmasking, which was used to solve The rest of the implementation is the same as TSP's DP bitmasking solution.

Afterwards, the control unit 5 can trace the path back based on the shortest distance information stored in the DP array. Through this, the shortest traversal path and its length can be obtained.

In step S24, upon receiving the END input while waiting, the control unit 5 repeats the action of step S22 for all clusters to confirm all clusters and delete all data. (FLUSH operation) After this, the operation of the algorithm ends.

The operation of steps S21-S23 is guaranteed to be completed within a finite time. Therefore, the waiting time is reached within a finite time, and the control unit 5 can complete the process of step S24 within the finite time. The control unit 5 terminates the operation within a finite time in response to an input with END.

Figure 2 is a flowchart showing the main operation of the present invention. Referring to Figure 2, the control unit 5 performs an initialization step (S201); Query reading step (S202); Flush comparison step (S203); Pop All Step (S204); Pop comparison step (S205); Pop step (S206); Add comparison step (S207); Add step (S208); End comparison step (S209); Pop All Step (S210); Perform the throw step (S211).

Figure 3 is a flowchart showing the pop-all operation of the present invention. Referring to Figure 3, in the pop-all step, the control unit 5 performs an initialization step (S301); Comparison step (S302); Cluster step (S303); Next step (S304); Cluster clear step (S305); Cluster count step (S306); Order count step (S307); Cluster map clear step (S308) is performed.

Figure 4 is a flowchart showing the pop operation of the present invention. Referring to Figure 4, the control unit 5 performs an initialization step (S401); Order reading step (S402); Cluster map step (S403); Cluster data output step (S404); Cluster size step (S405); Delete step (S406); Cluster list deletion step (S407); Perform the next step (S408).

Figure 5 is an exemplary diagram showing a cluster of the present invention. Referring to Figure 5, received orders 21 are grouped into clusters and output the shortest path, new orders 22 output the updated cluster shortest path, and new orders 21 are grouped into clusters to output the shortest path. Order (23) prints the new cluster path.

Figure 6 is an exemplary diagram illustrating a configuration for verifying data errors for illustrating the present invention.

Referring to FIG. 6, the control unit 5 stores sampling data, calculates a probability distribution by accumulating the number of occurrences for each size of the sampling data over a certain period of time, calculates a probability distribution for another period of time, and calculates two probabilities. By calculating the distribution difference, area difference, and difference distance accumulation (S101), sampling circuit abnormalities, data errors, and data changes can be predicted and responded to (S102). The control unit 5 notifies the user of the prediction result so that the user can respond, or the control unit 5 can respond to hardware failure, data error, or data change.

Sampling data includes cluster, order, shortest path, and collection distance, and the control unit 5 responds to hardware failure, data error, and data change based on the sampling data.

The control unit 5 monitors the trend of each probability distribution for a certain period of time, predicts abnormalities in the probability distribution, responds to abnormal accidents, and reports normal operation to the outside if the data change in the probability distribution is constant. Additionally, the control unit 5 feeds back the data change rate to adjust a certain time interval. For example, if the data change rate is large, the certain time interval is increased, and if the data change rate is small, the certain time interval is shortened.

FIG. 7 is an exemplary diagram illustrating hardware resources and an operating system, operations of a core control unit, and a system authentication configuration that grants authority to execute control unit operations for explaining the present invention. Referring to FIG. 7, the present invention is a processor (1). ), memory (2), input/output device (3), operating system (4), and control unit (5).

The processor (1) is a CPU (Central Processing Unit), GPU (Graphic Processing Unit), FPGA (Field Programmable Gate Array), and NPU (Neural Processing Unit), and the operating system (4) and control unit (5) mounted on the memory (2) ) executes the execution code.

Memory (2) includes permanent mass storage devices such as random access memory (RAM), read only memory (ROM), disk drives, solid state drives (SSD), and flash memory. can do.

The input/output device 3 is an input device, such as a camera, keyboard, microphone, mouse, etc. including an audio sensor and/or image sensor, and an output device such as a display, speaker, haptic feedback device, etc. May include devices.

The operating system 4 may include Windows, Linux, IOS, virtual machines, web browsers, and interpreters, and supports tasks, threads, timer execution, scheduling, resource management, graphics, font processing, communication, etc.

The control unit 5 determines the state based on sensor, key, touch, and mouse input of the input/output device 3 with the support of the operating system 4 and performs operations according to the determined state. The control unit 5 performs job scheduling by timers and threads using parallel execution routines.

The control unit 5 determines the state using the sensor value of the input/output device 3 and performs an algorithm according to the determined state.

Referring to Figure 7, the system authentication configuration includes a terminal 6 including a control unit 5, and an authentication server 7.

The terminal 6 duplicates the data channel, receives the key value and biometric information of the terminal 6, and requests user authentication through the first data channel to the authentication server 7, and the terminal 6 receives the generated kit value. It is displayed on the display and transmitted to the authentication server (7).

The terminal 6 inputs the kit value displayed on the display of the terminal 6 and transmits it along with the user information to the authentication server 7 through the second data channel. The terminal 6 requests the authentication server 7 to authenticate the system mounted on the terminal 6 using the kit value and user information. The kit value of the terminal 6 can be generated from computer-specific information, such as the CPU manufacturing number and the Ethernet chip number. The terminal 6 can obtain user information through face recognition using a camera, voice recognition using a microphone, and handwriting recognition using a display, and use it for authentication.

The authentication server 7 receives the kit value from the terminal 6, receives the kit value and user information from the terminal 6 through a duplicated data channel, compares the kit value and the user information of the terminal 6, and By matching, authentication for use of the system of the terminal 6 is processed. The authentication server 7 transmits the authentication result to the terminal 6 to authorize the user's use of the system. Due to the dual data channels of the terminal 6, kit value loss can be minimized.

The authentication server 7 performs history analysis of user information and compares and determines consistency and changes in user information over time. In history analysis, if user information shows consistency, the user's use is permitted; if it shows changes, the user's use is not permitted. By allowing users to use the system when user information shows consistency, security is strengthened to prevent users with altered user information from accessing the system.

The authentication server 7 assigns weights to consistency, change, frequency, frequency trend, and high frequency, and blocks access by untrusted users using a combination of weights. For example, if the frequency threshold is exceeded, access by untrusted users can be blocked in proportion to the accumulated number of excesses, and users who attempt access over a long period of time can be authenticated. At this time, additional authentication is requested for untrusted users.

The terminal 6, which is a means of authenticating the use of the system, does not connect directly to the system, but forms a bypass route through the authentication server 7, so that the network that makes up the Internet network is composed of an internal network and an external network, and the IP address setting process There is an advantage that the authentication process using the terminal 6 is performed smoothly in this cumbersome time. At this time, the system is mounted on the terminal 6, the terminal 6 becomes an authentication terminal means, and the authentication server 7 becomes an authentication server means.

The cloud (12) is a modularization of the container (7) with the support of the operating system (4) that manages the processor (1), memory (2), input/output device (3), and communication unit (6), and the web (8) and DB ( 9), provides the services of the protocol 10 and library 11, and the control unit 5 executes a cloud application using the services of the container 7. A standard software package, called a container (7), bundles an application's code with associated configuration files, libraries (11), and dependencies needed to run the app.

The cloud 12 integrates control of multiple terminals 6, stores sensor values received from the terminal 6, monitors them over time, processes operation errors of the terminal 6, and sends error messages to other terminals. Notifies the terminal 6, and performs switching control on the terminal 6 that is the control target.

Neural network learning selects features from time series data collected from input devices such as temperature, altitude, fingerprints, various sensors, images, infrared cameras, and lidar, selects a model through algorithm selection, and repeats through the learning and performance verification process. Model selection is repeated through trial and error. After performance verification is completed, an artificial intelligence model is selected.

The control unit 5 performs a deep learning algorithm using a neural network to determine sensor values, uses training data to learn the neural network, and verifies the neural network performance with test data.

The present invention is not limited to the specific preferred embodiments described above, and various modifications can be made by anyone skilled in the art without departing from the gist of the invention as claimed in the claims. Of course, such changes are within the scope of the claims.

1: processor
2: memory
3: Input/output device
4: Operating system
5: Control unit
6: Terminal
7: Authentication server
8: web
9: DB
11: Library
12: Cloud
14: Container
16: Department of Communications

Claims (5)

Food Waste Emissions Analysis Department, which collects analysis data on the current status of food waste generation in a specific area and determines the amount of food waste generated by building; and
A collection difficulty calculation unit for each zone divides the specific area into a plurality of zones according to the legal district boundary using the legal zone information in the analysis data and calculates the difficulty of collecting food waste for each zone,
The control unit 5 performs a new food waste collection input step (S21) in which new food waste collection is input;
A specific food waste collection confirmation step (S22) in which specific food waste collection is confirmed;
Shortest traversal path calculation step (S23) for calculating the shortest traversal path; and
A food waste collection method, characterized in that performing an end input termination step (S24) that ends when the end is input. According to paragraph 1,
The food waste emissions analysis department
A first step is to purify the analysis data to collect collection amount, collection date, collection time, and collection location for food waste, and a second step is to match the road name, address, building, and serial cadastral map of the analysis data,
In step S21, the control unit 5,
When a new collection order is entered, receiving all data of the new collection order;
Storing the two-way distance data obtained through the pathfinding algorithm and traversing n clusters that already exist;
The “shortest traversal path inside the cluster” of the cluster is confirmed, and if the “immediate matching condition function” that takes the cluster as an input returns true, all orders included in the cluster are confirmed and all collection identification values of the cluster are confirmed. outputting them in order of the shortest traversal path within the cluster;
Deleting all orders and clusters included in the cluster, and deleting two-way distance data with all order collection points included in the cluster; and
If max_effective_distance is less than strictness * (existing distance) + bias, create a cluster containing only new collection orders, save the cluster, record the identification value of the cluster in the new collection order data, and return to the waiting state. A food waste collection method, characterized in that performing the following steps. According to paragraph 1,
The collection difficulty calculation for each zone is as follows:
For each of the plurality of zones, calculate the difficulty of food waste collection for each zone by applying weights according to the priority between key variables defined by collection points, discharge amount, road slope, travel distance, and road width,
In step S22, the control unit 5,
When a specific collection order needs to be confirmed, entering an identification value of the collection order to be confirmed;
For a cluster containing a collection order with a corresponding identification value, confirming all orders included in the cluster and outputting all collection identification values of the cluster in order of the shortest traversal path within the cluster; and
A food waste collection method comprising the steps of deleting all orders and clusters included in the cluster, and deleting two-way distance data with all order collection points included in the cluster. According to paragraph 1,
Further comprising a recommended collection route determination unit that determines a recommended collection route for food waste collection in the specific area through network analysis based on the food waste emissions for each building and the difficulty of collecting food waste for each zone;
In step S23, the control unit 5,
Perform the “shortest traversal path inside the cluster” calculation method, set the collection points shared by the cluster’s collection orders as the starting vertex, create a complete graph using distance data, and then use DP (Dynamic Programming) used to solve the Travel Salesman’s Problem. A step of using bitmasking, but pre-marking the departure point bit of the visit list so as not to return to the departure point; and
A method of collecting food waste, characterized in that the step of backtracking the route based on the shortest distance information stored in the DP array and calculating the shortest circuit path and its length through this. According to paragraph 4,
The collection difficulty calculation for each zone is as follows:
Depending on the type of housing in the specific area, for single-family homes, the above priorities are given in the order of collection points, emissions, road width, travel distance, and road slope, and for multi-family homes, the priorities are given in the order of collection points, travel distance, emissions, and road slope. The above priorities are given in the order of width and road slope,
In step S24, the control unit 5,
Upon receiving the END input while waiting, repeating the step (S22) for all clusters to confirm all clusters and delete all data; and
A method of collecting food waste, characterized in that the step of reaching the waiting time within a finite time and terminating the process of step (S24) within a finite time.