CN112596457B - Intelligent control method and system for kitchen disposal, collection and transportation - Google Patents
Intelligent control method and system for kitchen disposal, collection and transportation Download PDFInfo
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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- G06F18/00—Pattern recognition
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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Abstract
The invention discloses an intelligent control method and system for kitchen disposal, collection and transportation, which calculate the residual disposal amount when returning to a factory and when returning to the factory by acquiring data in a kitchen collection and transportation system; comparing the residual handling capacity with the best working condition handling capacity, sending a first instruction for increasing or reducing the speed of the kitchen receiving and transporting system to a factory returning vehicle at a receiving and transporting end and/or sending a second instruction for increasing or reducing the handling speed to a handling device at a handling end according to a comparison result, adjusting parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, comparing a handling result parameter with a predicted result parameter, predicting through machine learning to obtain a verification result parameter, sending a third instruction for increasing or reducing the speed of the kitchen returning vehicle at the receiving and transporting end and/or sending a fourth instruction for increasing or reducing the handling speed to the handling device at the handling end according to the verification result parameter; and circulating until the residual treatment amount approaches the optimal working condition treatment amount. The equipment management efficiency can be improved, and the equipment maintenance cost can be reduced.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to a kitchen disposal receiving and transporting intelligent control method and system.
Background
With the development of industry 4.0, the traditional industrial control mainly based on human participation control cannot meet the requirement of industrial development. In addition, cost reduction and efficiency improvement by an information technology means are always sought for kitchen waste disposal enterprises in recent years. In the current intelligent control of kitchen waste disposal, the intelligent control is mainly realized by judging the normal state of parameters according to various parameters of the technological process or by performing machine learning through historical data. The cost reduction and the efficiency improvement are mainly realized by adjusting a process route, optimizing a process method or adopting some energy-saving equipment. The kitchen disposal process is extremely complex, so that the intelligent control realized by applying machine learning needs a large amount of data and scenes to be more suitable for practical application, and the process route adjustment and process method optimization in the aspects of cost reduction and efficiency improvement need more project experience or longer period to be better verified. Therefore, in view of the above situation, it is necessary to consider how to achieve the object in a better manner and efficiency in the technology of realizing intelligence and cost reduction and efficiency improvement.
The existing kitchen disposal control system is mainly designed and adjusted according to a process flow and real-site production, an operator controls a site through the system at an operation station, the technology is more controlled by depending on the experience of site personnel, but kitchen disposal data is changed in real time and needs to control the process according to the change, and excessive manual intervention causes certain hysteresis, so that production resource waste is caused or some emergencies are possibly caused by manual carelessness.
And the fluctuation degree of the kitchen collection and transportation amount is large, the time for collecting and transporting the kitchen is indefinite, and the quality for collecting and transporting is different, so that the kitchen disposal production line needs to continuously adjust the equipment parameters of each link according to the actual situation, and the purposes of reducing equipment loss and reducing power consumption are achieved. The machine learning control system needs to use massive historical data to complete machine learning, long historical accumulation is needed in period and data processing, algorithm model universality is poor according to adjustment of a process flow, in addition, the current machine learning data is basically process data monitored on line and has certain practicability, but for the kitchen disposal field with strong dependence on process environment, machine learning industrial control is carried out on the process data and is feasible theoretically, but various complex environments need to be considered in realization, and the machine learning is relatively difficult, and partial energy conservation is realized by reducing artificial participation mainly through optimization control at present, so that cost reduction is realized.
In view of this, it is very significant to establish an intelligent control method and system for kitchen disposal, collection and transportation.
Disclosure of Invention
The problems that the existing kitchen collecting and transporting is high in cost, low in efficiency, complex in environment, multiple in human interference factors and the like are solved. An embodiment of the present application aims to provide an intelligent control method and system for kitchen disposal, collection and transportation to solve the technical problems mentioned in the background art section above.
In a first aspect, an embodiment of the present application provides an intelligent control method for kitchen disposal, collection and transportation, including the following steps:
s1: acquiring data in the kitchen collecting and transporting system, calculating time spent on returning to a factory according to the data, and calculating the residual disposal quantity when returning to the factory according to the time spent on returning to the factory;
s2: comparing the residual treatment amount with the treatment amount under the optimal working condition to obtain a first comparison result, sending a first instruction for increasing or reducing the speed of the kitchen vehicle to the factory returning vehicle at the receiving and transporting end and/or sending a second instruction for increasing or reducing the treatment speed to the treatment equipment at the treatment end according to the first comparison result, adjusting parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtaining treatment result parameters after adjustment;
s3: comparing the disposal result parameters with the predicted result parameters to obtain a second comparison result, predicting through machine learning based on the second comparison result to obtain verification result parameters, and sending a third instruction for increasing or decreasing the vehicle speed to the return vehicle at the receiving and transporting end and/or sending a fourth instruction for increasing or decreasing the disposal speed to the disposal equipment at the disposed end according to the verification result parameters; and
s4: and repeating the steps S1-S3 until the residual treatment amount approaches the optimal working condition treatment amount.
In some embodiments, the data includes collected data from a vehicle module and collected data from an industrial control module, the vehicle module is an integrated module of a sensor and/or a communication module in a vehicle of the kitchen collection and transportation system, the collected data includes a vehicle ID, an address, a longitude, a latitude and a current vehicle speed, the industrial control module refers to an integrated module that performs read-write operation on PLC parameters through an intelligent gateway, and the processed data includes a current processing amount and processing speed of each link.
In some embodiments, step S1 further includes: and carrying out data cleaning on the collected and transported data according to the average garbage generation amount in the past day, the load capacity of the vehicles and the operation area of the collected and transported fleet. The point location of the GPS in the received and transported data usually has an error, and in order to avoid the error point location from affecting the calculation result, the error point location needs to be screened out and cleaned. Cleaning is carried out by calculating whether the point position distance of two adjacent time sequences can be realized in the same time in reality.
In some embodiments, the remaining amount of treatment in step S1 is calculated by:
K1＝Kgeneral assembly-(m+nV1T)
Wherein, KGeneral assemblyAs a total amount of disposal, K1M and n are constants for the residual treatment amount, and the treatment speed V of the current treatment end is obtained from the industrial control module1The current time is T0The time required for returning the vehicle to the factory is known as T1If T is equal to T0+T1。
The kitchen waste disposal, collection and transportation processes can be intelligently controlled through the residual disposal quantity and the factory returning time.
In some embodiments, at the receiving and transporting end, the total amount of kitchen waste to be received in the same day is set as K3The load capacity of the single vehicle is assigned to K on the same day4The amount of the kitchen waste collected by a single vehicle on the same day is K5When K is5Approaches to K4Sending a factory return instruction to the vehicle and starting to calculate the factory return time T1When Σ K5<∑K4，∑K5≧K3Then, a factory return instruction is sent to all vehicles participating in the calculation, and the factory return time T of each vehicle is calculated1. When the receiving and transporting end is used for returning to the factory according to the amount calculation of the kitchen waste, a factory returning instruction is sent to the vehicle, and the vehicle is further conveniently controlled.
In some embodiments, step S2 specifically includes: and when the residual treatment amount is smaller than the treatment amount under the optimal working condition, reducing the treatment speed and/or the vehicle speed, when the residual treatment amount is larger than the treatment amount under the optimal working condition, improving the treatment speed and/or the vehicle speed, repeatedly calculating the time of returning to the factory and predicting the residual treatment amount when returning to the factory until the residual treatment amount approaches to the treatment amount under the optimal working condition. In order to realize that the residual treatment amount is close to the treatment amount under the optimal working condition, the treatment speed and/or the vehicle speed can be adjusted, and the treatment, collection and transportation efficiency of the kitchen can be effectively improved.
In some embodiments, the verification result parameters are predicted by a decision tree. The decision tree can predict and evaluate the treatment efficiency, and finally, the process is adjusted to achieve the best effect.
In some embodiments, the first, second, third, and/or fourth instructions are transmitted to the vehicle module and/or the industrial control module, respectively, using an edge calculation. The edge calculation can convert the data format of the instruction, and is convenient to control.
In a second aspect, an embodiment of the present application further provides an intelligent control system for kitchen disposal, collection and transportation, including:
the residual disposal quantity prediction module is configured to acquire data in the kitchen collecting and transporting system, calculate the time spent on returning to the factory according to the data and calculate the residual disposal quantity when returning to the factory according to the time spent on returning to the factory;
the treatment adjusting module is configured to compare the residual treatment amount with the treatment amount under the optimal working condition to obtain a first comparison result, send a first instruction for increasing the speed of the kitchen or reducing the speed of the kitchen to a factory vehicle at the receiving and transporting end and/or send a second instruction for increasing the treatment speed or reducing the treatment speed to treatment equipment at the treatment end according to the first comparison result, adjust parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtain treatment result parameters after adjustment;
the verification adjusting module is configured to compare the disposal result parameters with the predicted result parameters to obtain a second comparison result, obtain verification result parameters through machine learning prediction based on the second comparison result, and send a third instruction for increasing or reducing the vehicle speed to a factory returning vehicle at the receiving and transporting end and/or send a fourth instruction for increasing or reducing the disposal speed to disposal equipment at the disposed end according to the verification result parameters; and
and the circulating module is configured to repeatedly execute the residual handling capacity predicting module to the verification adjusting module until the residual handling capacity approaches to the optimal working condition handling capacity.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; storage means for storing one or more programs which, when executed by one or more processors, cause the one or more processors to carry out a method as described in any one of the implementations of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
The invention provides an intelligent control method and system for kitchen disposal, collection and transportation, which aim at collecting data generated in the working process of a sanitation collection and transportation system. The data acquisition process in the method can acquire the data of multiple dimensions reflecting the running state of the equipment, the data of actual production can be effectively utilized, meanwhile, the heterogeneous data of multiple dimensions can extract richer features, and therefore the running state of the equipment can be comprehensively and accurately reflected. And pre-scheduling the sanitation system and the disposal equipment based on the machine learning model. The model can effectively perform dimension reduction processing on the features, and meanwhile, the model can reduce the complexity of the model under the condition of ensuring higher accuracy, and is very suitable for industrial internet of things scenes with higher real-time requirements. The invention aims to effectively combine the system and the algorithm to build a set of complete industrial Internet of things intelligent scheduling system, has the functions of collecting equipment production data, monitoring the running state of the equipment in real time and intelligently controlling the equipment, improves the equipment management efficiency to a certain extent, enhances the equipment production safety, reduces the equipment maintenance cost and has certain practical application value.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is an exemplary device architecture diagram in which one embodiment of the present application may be applied;
fig. 2 is a schematic flow chart of an intelligent control method for kitchen disposal collection and transportation according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a database of the intelligent kitchen disposal receiving and transporting control method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a kitchen disposal receiving and transporting intelligent control system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device suitable for implementing an electronic apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 illustrates an exemplary device architecture 100 to which the kitchen disposal receiving and transporting intelligent control method or the kitchen disposal receiving and transporting intelligent control system according to the embodiment of the present application may be applied.
As shown in fig. 1, the apparatus architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various applications, such as data processing type applications, file processing type applications, etc., may be installed on the terminal apparatuses 101, 102, 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as a background data processing server that processes files or data uploaded by the terminal devices 101, 102, 103. The background data processing server can process the acquired file or data to generate a processing result.
It should be noted that the intelligent kitchen disposal receiving and transporting control method provided in the embodiment of the present application may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103, and accordingly, the intelligent kitchen disposal receiving and transporting control system may be disposed in the server 105, or may be disposed in the terminal devices 101, 102, and 103.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where the processed data does not need to be acquired from a remote location, the above device architecture may not include a network, but only a server or a terminal device.
The relevant acquisition techniques involved in the device data acquisition process and the important devices used will be described below, and the relevant machine learning algorithms involved in the algorithm model will be described in detail.
The data acquisition technology comprises related contents such as an industrial internet of things transmission protocol, an industrial sensing technology, a big data technology and the like. The related contents of the algorithm model mainly comprise a lifting algorithm and a recurrent neural network algorithm.
Fig. 2 shows that an embodiment of the present application discloses and provides an intelligent control method for kitchen disposal, collection and transportation, which includes the following steps:
s1: acquiring data in the kitchen collecting and transporting system, calculating time spent on returning to a factory according to the data, and calculating the residual disposal quantity when returning to the factory according to the time spent on returning to the factory;
s2: comparing the residual treatment amount with the treatment amount under the optimal working condition to obtain a first comparison result, sending a first instruction for increasing or reducing the speed of the kitchen vehicle to the factory returning vehicle at the receiving and transporting end and/or sending a second instruction for increasing or reducing the treatment speed to the treatment equipment at the treatment end according to the first comparison result, adjusting parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtaining treatment result parameters after adjustment;
s3: comparing the disposal result parameters with the predicted result parameters to obtain a second comparison result, predicting through machine learning based on the second comparison result to obtain verification result parameters, and sending a third instruction for increasing or decreasing the vehicle speed to the return vehicle at the receiving and transporting end and/or sending a fourth instruction for increasing or decreasing the disposal speed to the disposal equipment at the disposed end according to the verification result parameters; and
s4: and repeating the steps S1-S3 until the residual treatment amount approaches the optimal working condition treatment amount.
In a specific embodiment, a circulation process is set in the intelligent control method for kitchen disposal, collection and transportation, so that the input and output results of each prediction can be better judged by the lifting tree model. And various kinds of original data of the kitchen collecting and transporting system, such as the position of a vehicle from a factory, the kitchen waste generation amount of a merchant, the vehicle load amount and the like, are acquired through a vehicle-mounted scale, a GPS, an RFID and the like, and all the data are transmitted to the next process in a general message mode. The main sources of data acquisition are two, the vehicle module and the industrial control module. The data includes shipping data collected from the vehicle module and disposition data collected from the industrial control module. The vehicle module is an integrated module of a sensor and/or a communication module in a vehicle of the kitchen collecting and transporting system, and in a specific embodiment, the vehicle module refers to a series of integrated modules of universal technologies in the industry, such as a sensor of the vehicle, a vehicle-mounted scale sensor, an RFID sub-module, an intelligent rearview mirror manual interaction sub-module and the like. The industrial control module is an integrated module for performing read-write operation on the PLC parameters through the intelligent gateway, and in a specific embodiment, the industrial control module is an integrated module for directly performing read-write operation on the PLC parameters through the intelligent gateway, so that the state reading and parameter modification of the disposal device are realized. The receiving and transporting data comprise vehicle ID, address, longitude, latitude and current vehicle speed, and the handling data comprise current handling amount and handling speed of each link. In a preferred embodiment, configuration information such as a vehicle ID, an address block, an address, a longitude, a latitude, uploading time, a current vehicle speed and the like is acquired through an intelligent gateway, a vehicle-mounted scale, an RFID module, a GPS module and other internet-of-things components to a cloud, and is correspondingly stored in a database according to a set table structure. Information such as current handling capacity, handling speed of each link and the like is acquired to a cloud end through a PLC in an industrial control module and industrial equipment and a sensor connected with the PLC, and is correspondingly stored in a database according to a set table structure. A database table structural representation is shown in fig. 3.
In a specific embodiment, step S1 further includes: and carrying out data cleaning on the collected and transported data according to the average garbage generation amount in the past day, the load capacity of the vehicles and the operation area of the collected and transported fleet. The point location of the GPS in the received and transported data usually has an error, and in order to avoid the error point location from affecting the calculation result, the error point location needs to be screened out and cleaned. In the following, cleaning is performed by calculating whether the point location distance of two adjacent time sequences can be realized in the same time period in reality as an example, and other data are cleaned on the basis of the same principle.
The point location data cleaning process is divided into two steps, firstly, the driving mileage between two points is calculated, and the straight line distance between the two points is calculated according to the longitude and latitude of the two points:
c2＝(Ja-Jb)2+(Wa-Wb)2；
wherein c is the chord length between two points, namely the straight-line distance between the two points; j. the design is a squarea、WaRespectively the longitude and latitude of the point a; j. the design is a squareb、WbThe longitude and latitude of point b are shown respectively.
Now, knowing that the radius from the point a to the center of the earth is nearly a fixed value R, the chord length c is obtained by the above formula, and the arc length R between the points a and b can be obtained:
α＝arcsin(c/2/R)；
r＝2α·R；
and then judging whether the vehicle can finish the mileage at the highest speed, and freezing the data strip of the next point when the relationship between the distance between two adjacent GPS points and the uploading time meets the following formula.
r/(Ta-Tb)>Vset；
Wherein, TaRepresents the upload time of point a; t isbRepresenting the upload time of point B; vsetRepresenting the maximum speed per hour that the vehicle is designed to achieve.
When the calculated speed per hour of the two point locations exceeds the speed per hour which can be reached by the vehicle in reality, the point location measurement is indicated to have deviation, and the next point location is cleaned. And finally, synchronously transmitting the cleaned data to an edge computing end and a cloud end for computing of the computing end.
Data related to calculation including, but not limited to, all the parameters mentioned above are extracted, and the time required for the vehicle to return to the factory is calculated by setting constants in advance, such as the distance R from the center of the earth in the area, the geographic coordinates of the disposal factory, and some of the parameters extracted from the cloud and edge calculation terminals. Setting the current vehicle point position as point c and the average vehicle speed as VcThe point location of the treatment plant is fixed to a point G, and the arc length r of the two points is obtainedGWhen returning to factory, T1The calculation is made by the following formula:
T1＝rG/Vc。
and obtaining the rasterized parameters and the operation state of each equipment in the PLC through each equipment PLC connected with the industrial control module, calculating the disposal speed, and calculating the residual disposal amount when the vehicle arrives at the factory according to the current disposal speed and the time spent on returning to the factory.
In a specific embodiment, the remaining amount of treatment in step S1 is calculated by the following equation:
K1＝Kgeneral assembly-(m+nV1T)
Wherein, KGeneral assemblyAs a total amount of disposal, K1M and n are constants for the residual treatment amount, and the treatment speed V of the current treatment end is obtained from the industrial control module1The current time is T0The time required for returning the vehicle to the factory is known as T1If T is equal to T0+T1。
Comparing the optimal working condition disposal quantity preset by the equipment with the residual disposal quantity, reversely transmitting an instruction to the front end according to the difference value of the optimal working condition disposal quantity and the residual disposal quantity, and finally, calculating the T + T value1And (4) the handling condition of the points, the factory return time of the vehicle and the handling speed of the handling equipment are reversely adjusted and optimized.
In a specific embodiment, at the receiving and transporting end, the total amount of kitchen waste to be received in the same day is set to be K3The load capacity of the single vehicle is assigned to K on the same day4The amount of the kitchen waste collected by a single vehicle on the same day is K5When K is5Approaches to K4Sending a factory return instruction to the vehicle and starting to calculate the factory return time T1When Σ K5<∑K4，∑K5≧K3Then, a factory return instruction is sent to all vehicles participating in the calculation, and the factory return time T of each vehicle is calculated1。
In a specific embodiment, step S2 specifically includes: and when the residual treatment amount is smaller than the treatment amount under the optimal working condition, reducing the treatment speed and/or the vehicle speed, when the residual treatment amount is larger than the treatment amount under the optimal working condition, improving the treatment speed and/or the vehicle speed, repeatedly calculating the time of returning to the factory and predicting the residual treatment amount when returning to the factory until the residual treatment amount approaches to the treatment amount under the optimal working condition. In order to realize that the residual treatment amount is close to the treatment amount under the optimal working condition, the treatment speed and/or the vehicle speed can be adjusted, and the treatment, collection and transportation efficiency of the kitchen can be effectively improved.
When the receiving and transporting end is used for returning to the factory according to the amount calculation of the kitchen waste, a factory returning instruction is sent to the vehicle, and the vehicle is further conveniently controlled. To ensure K1Near to K2Ensuring that the speed per hour of the vehicle is not more than VsetIn the case of (2), the current vehicle speed is VaAnd calculating the time T for the vehicle to return to the factoryaWhen T isaTime K1<K2When the vehicle returns to the factory, a command for slowing down the vehicle speed is sent, and when T is reachedaTime K1>K2And then sending a factory returning instruction for increasing the vehicle speed. And obtaining a new vehicle speed Va1Post-recalculation of Ta。
Also, at the treatment end, when TaTime K1<K2When the processing is finished, rewriting the value in the PLC through the edge computing equipment, and sending an industrial control instruction for slowing down the processing speed; when T isaTime K1>K2When the processing speed is increased, an industrial control command is transmitted.
In addition, the preheating time of the treatment equipment is set as T2When K is1At 0, at T + T1-T2The disposal equipment is started all the time, and when the vehicle arrives at a factory, the disposal equipment is in a formal working state, so that the bad disposal garbage amount when the equipment is in a preheating state is reduced, and the idle time of the disposal equipment is reduced.
The instruction mentioned in step S2 cannot actually directly act on the vehicle module and the industrial control module, and the invention adopts an edge calculation mode to transmit the instruction to the vehicle and the handling equipment through the intelligent gateways respectively arranged in the handling workshop and the cab, thereby completing the function of the relevant instruction.
After finishing one major cycle, comparing the result parameters after finishing the correction cycle with the predicted result parameters without the correction cycle through the industrial control module, and reflecting the comparison result to the machine learning model for prediction. In a specific embodiment, the verification result parameters are predicted by a decision tree. And when gain exists after circulation, selecting the attribute with the maximum information gain to split, so that the decision tree is one layer longer. The decision tree can predict and evaluate the treatment efficiency, and finally, the process is adjusted to achieve the best effect. In the preferred embodiment, the decision tree employs the ID3 algorithm.
The information theory has the concept of entropy (entropy), which represents the degree of disorder of the state, and the greater the entropy, the more disorder. The change of entropy can be regarded as information gain, and the core idea of the decision tree ID3 algorithm is to select the attribute of information gain measurement and select the attribute with the maximum information gain after splitting to split.
Let D be the division of the training tuples by (output) class, the entropy of D is expressed as:
wherein pi represents the probability of the ith class appearing in the whole training tuple, and the proportion of the number of samples of the ith class to the total number is generally used as the probability estimation; the actual meaning of entropy represents the average amount of information needed for class labels of tuples in D.
If the training tuple D is divided according to the attribute A, the expected information of the division of the training tuple D by the A is as follows:
the information gain is then the difference between the two:
gain(A)＝info(D)-infoA(D)；
and selecting the attribute with the maximum information gain as the splitting attribute at each time. Each split will make the tree one level longer. Therefore, a decision tree can be built up after the production is carried out step by step.
And when no gain exists after circulation, the node of the attribute is cut, so that overfitting is avoided, and decision efficiency is improved. To avoid the overfitting problem, we need to avoid the excessive noise data, and the invention adopts the mode of building branches and leaves to prevent overfitting. And after the decision tree is built, cutting is started. Two methods are used: 1) replacing the whole sub-tree with a single leaf node, the classification of the leaf node using the most dominant classification in the sub-tree; 2) one word count is substituted for the other sub-tree completely.
Assuming that the number of leaf nodes of the tree T is | T |, T is a leaf node of the tree T, the leaf node has Nt sample points, where k is Ntk sample points, k is 1, 2.. k, ht (T) is the empirical entropy on the leaf node T, and α > is 0 as a parameter, the loss function of the decision tree learning can be defined as:
wherein the empirical entropy is
In the loss function, the first term on the right-hand side is written as
At this time, there are
Cα(T)＝C(T)+α|T|；
Where c (T) represents the prediction error of the model for the training data, and | T | is the complexity. The loss function is a comprehensive evaluation of prediction error and complexity.
When alpha is determined, the model with the minimum loss function is selected, namely the subtree with the minimum loss function is selected for pruning.
The specific pruning step is as follows:
assuming the first leftmost branch is subtracted, C α is calculated once (T1); assuming the second leftmost branch is subtracted, C α is calculated once (T2); c α (Tn) is calculated once every subtraction of one upward recursion in the manner described above; finding the minimum value of C alpha (T1) to C alpha (Tn); and subtracting the branch to obtain the optimal decision tree. When the large loop is not completed, firstly, an instruction is sent to the edge computing equipment through the decision tree, and after the loop is completed, the decision tree is split or cut according to the maximum gain attribute obtained by the loop.
In a specific embodiment, the first command, the second command, the third command and/or the fourth command are transmitted to the vehicle module and/or the industrial control module in an edge calculation manner, respectively. The edge calculation can convert the data format of the instruction, and is convenient to control.
With further reference to fig. 4, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of an intelligent control system for kitchen disposal, collection and transportation, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
An intelligent control system that receives fortune is dealt with to meal kitchen in the embodiment of this application includes:
the residual treatment amount prediction module 1 is configured to acquire data of a vehicle module and an industrial control module in the kitchen collecting and transporting system, calculate the time spent on returning to a factory according to the data, and calculate the residual treatment amount when returning to the factory according to the time spent on returning to the factory;
the treatment adjusting module 2 is configured to compare the remaining treatment amount with the treatment amount under the optimal working condition to obtain a first comparison result, send a first instruction for increasing or decreasing the vehicle speed to a return vehicle at the receiving and transporting end and/or send a second instruction for increasing or decreasing the treatment speed to a treatment device at the treatment end according to the first comparison result, adjust parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtain treatment result parameters after adjustment;
the verification adjusting module 3 is configured to compare the disposal result parameters with the predicted result parameters to obtain a second comparison result, obtain verification result parameters through machine learning prediction based on the second comparison result, and send a third instruction for increasing or decreasing the vehicle speed to a factory returning vehicle at the receiving and transporting end and/or send a fourth instruction for increasing or decreasing the disposal speed to disposal equipment at the disposed end according to the verification result parameters; and
and the circulation module 4 is configured to repeatedly execute the residual treatment amount prediction module 1 to the verification adjustment module 3 until the residual treatment amount approaches the optimal working condition treatment amount.
The method is mainly applied to actual projects, environmental sanitation transport data, equipment operation data, yield data and optimal working condition indexes are adopted in data of a scheduling scheme, the optimal working condition indexes are derived based on the yield data, the data indexes applied according to the complexity of the projects are different, if the projects are relatively simple, the yield data can be directly used as the indexes for evaluation, and the method can be completely realized by means of the environmental sanitation transport data.
The invention relates to the best working condition judgment, and can apply a machine learning method to process monitoring data, but a large amount of historical data is needed as a basis, and meanwhile, a scheme and an algorithm principle at the later stage need to be similar to the scheme, so that the aim of the invention can be achieved.
Compared with the current industrial control and other intelligent scheduling schemes of the kitchen disposal industry, the technical scheme of the invention has at least the following beneficial effects:
1) by the control scheme, the operation cost can be effectively reduced under the condition of ensuring normal treatment of the kitchen treatment.
2) The intelligent control is enabled through the receiving and transporting ring section at the front end, the front end and the rear end are opened, the intelligent control system is particularly suitable for enterprises in integrated operation, and the enterprises can quickly apply the scheme to each project site.
3) The invention realizes intelligent control according to an intelligent algorithm, avoids transition human intervention, enables the kitchen treatment to always operate under a better working condition with the best production benefit, helps enterprises to really realize cost reduction and efficiency improvement, and simultaneously reduces the equipment damage rate to a certain extent.
The invention provides an intelligent control method and system for kitchen disposal, collection and transportation, which aim at collecting data generated in the working process of a sanitation collection and transportation system. The data acquisition process in the method can acquire the data of multiple dimensions reflecting the running state of the equipment, the data of actual production can be effectively utilized, meanwhile, the heterogeneous data of multiple dimensions can extract richer features, and therefore the running state of the equipment can be comprehensively and accurately reflected. And pre-scheduling the sanitation system and the disposal equipment based on the machine learning model. The model can effectively perform dimension reduction processing on the features, and meanwhile, the model can reduce the complexity of the model under the condition of ensuring higher accuracy, and is very suitable for industrial internet of things scenes with higher real-time requirements. The invention aims to effectively combine the system and the algorithm to build a set of complete industrial Internet of things intelligent scheduling system, has the functions of collecting equipment production data, monitoring the running state of the equipment in real time and intelligently controlling the equipment, improves the equipment management efficiency to a certain extent, enhances the equipment production safety, reduces the equipment maintenance cost and has certain practical application value.
Referring now to fig. 5, a schematic diagram of a computer apparatus 500 suitable for implementing an electronic device (e.g., the server or the terminal device shown in fig. 1) according to an embodiment of the present application is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 5, the computer apparatus 500 includes a Central Processing Unit (CPU)501 and a Graphics Processing Unit (GPU)502, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)503 or a program loaded from a storage section 509 into a Random Access Memory (RAM) 504. In the RAM504, various programs and data necessary for the operation of the apparatus 500 are also stored. The CPU501, GPU502, ROM503, and RAM504 are connected to each other via a bus 505. An input/output (I/O) interface 506 is also connected to bus 505.
The following components are connected to the I/O interface 506: an input portion 507 including a keyboard, a mouse, and the like; an output section 508 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 509 including a hard disk and the like; and a communication section 510 including a network interface card such as a LAN card, a modem, or the like. The communication section 510 performs communication processing via a network such as the internet. The driver 511 may also be connected to the I/O interface 506 as necessary. A removable medium 512 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 511 as necessary, so that a computer program read out therefrom is mounted into the storage section 509 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications section 510, and/or installed from removable media 512. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU)501 and a Graphics Processing Unit (GPU) 502.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. The computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The modules described may also be provided in a processor.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring data in the kitchen collecting and transporting system, calculating time spent on returning to a factory according to the data, and calculating the residual disposal quantity when returning to the factory according to the time spent on returning to the factory; comparing the residual treatment amount with the treatment amount under the optimal working condition to obtain a first comparison result, sending a first instruction for increasing or reducing the speed of the kitchen vehicle to the factory returning vehicle at the receiving and transporting end and/or sending a second instruction for increasing or reducing the treatment speed to the treatment equipment at the treatment end according to the first comparison result, adjusting parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtaining treatment result parameters after adjustment; comparing the disposal result parameters with the predicted result parameters to obtain a second comparison result, predicting through machine learning based on the second comparison result to obtain verification result parameters, and sending a third instruction for increasing or decreasing the vehicle speed to the return vehicle at the receiving and transporting end and/or sending a fourth instruction for increasing or decreasing the disposal speed to the disposal equipment at the disposed end according to the verification result parameters; and repeating the steps until the residual treatment amount approaches to the optimal working condition treatment amount.
It is to be understood that the scope of the present invention in the present application is not limited to the embodiments in which the above-described features are combined in specific combinations, and the present invention also covers other embodiments in which the above-described features or their equivalents are combined in arbitrary combinations without departing from the above-described inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. An intelligent control method for kitchen disposal, collection and transportation is characterized by comprising the following steps:
s1: acquiring data in a kitchen receiving and transporting system, calculating time spent on returning to a factory according to the data, and calculating the residual disposal quantity when returning to the factory according to the time spent on returning to the factory;
s2: comparing the residual disposal quantity with the disposal quantity under the optimal working condition to obtain a first comparison result, sending a first instruction for increasing or reducing the speed of the kitchen vehicle to a factory returning vehicle at a receiving and transporting end and/or sending a second instruction for increasing or reducing the disposal speed to disposal equipment at a disposal end according to the first comparison result, adjusting parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtaining disposal result parameters after adjustment;
the step S2 specifically includes: when the residual disposing amount is smaller than the disposing amount under the optimal working condition, reducing the disposing speed and/or increasing the vehicle speed, and when the residual disposing amount is larger than the disposing amount under the optimal working condition, increasing the disposing speed and/or reducing the vehicle speed;
s3: comparing the disposal result parameters with the predicted result parameters to obtain a second comparison result, predicting through machine learning based on the second comparison result to obtain verification result parameters, and sending a third instruction for increasing or reducing the vehicle speed to the factory returning vehicle of the receiving and transporting end and/or sending a fourth instruction for increasing or reducing the disposal speed to the disposal equipment of the disposal end according to the verification result parameters; and
s4: and repeating the steps S1-S3, and repeatedly calculating the time of returning to the factory and predicting the residual treatment amount when returning to the factory until the residual treatment amount approaches to the optimal working condition treatment amount.
2. The kitchen disposal, receiving and transporting intelligent control method as claimed in claim 1, wherein the data includes receiving and transporting data collected from a vehicle module and disposal data collected from an industrial control module, the vehicle module is an integrated module of a sensor and/or a communication module in a vehicle of the kitchen receiving and transporting system, the receiving and transporting data includes a vehicle ID, an address, a longitude, a latitude and a current vehicle speed, the industrial control module is an integrated module for performing read-write operation on PLC parameters through an intelligent gateway, and the disposal data includes a current disposal amount and disposal speeds of various links.
3. The intelligent control method for kitchen disposal, collection and transportation according to claim 2, wherein the step S1 further comprises: and carrying out data cleaning on the collected and transported data according to the average garbage generation amount in the past day, the load capacity of the vehicles and the operation area of the collected and transported fleet.
4. The intelligent kitchen disposal, collection and transportation control method according to claim 2, wherein the remaining disposal amount in step S1 is calculated by the following formula:
K1＝Kgeneral assembly-(m+nV1T)
Wherein, KGeneral assemblyAs a total amount of disposal, K1M and n are constants for the residual treatment amount, and the treatment speed V of the current treatment end is obtained from the industrial control module1The current time is T0The time required for returning the vehicle to the factory is known as T1If T is equal to T0+T1。
5. The intelligent control method for kitchen disposal, collection and transportation according to claim 4, wherein at the collection and transportation end, the total amount of kitchen waste to be collected in the same day is set as K3The load capacity of the single vehicle is assigned to K on the same day4And the amount of the kitchen waste collected by the single vehicle is K on the same day5When K is5Approaches to K4Sending a factory return instruction to the vehicle and starting to calculate the factory return time T1When Σ K5<∑K4，∑K5≧K3Then, a factory return instruction is sent to all vehicles participating in the calculation, and the factory return time T of each vehicle is calculated1。
6. The intelligent control method for kitchen disposal, collection and transportation according to any one of claims 1-5, wherein the verification result parameters are predicted through a decision tree.
7. The intelligent kitchen disposal, collection and transportation control method according to claim 2, wherein the first instruction, the second instruction, the third instruction and/or the fourth instruction are transmitted to the vehicle module and/or the industrial control module in an edge calculation manner, respectively.
8. The utility model provides a meal kitchen is dealt with and is received fortune intelligence control system which characterized in that includes:
the residual disposal quantity prediction module is configured to obtain data in the kitchen collecting and transporting system, calculate the time spent on returning to the factory according to the data and calculate the residual disposal quantity when returning to the factory according to the time spent on returning to the factory;
the treatment adjusting module is configured to compare the residual treatment amount with the treatment amount under the optimal working condition to obtain a first comparison result, send a first instruction for increasing or reducing the vehicle speed to a factory returning vehicle at the receiving and transporting end and/or send a second instruction for increasing or reducing the treatment speed to treatment equipment at the treatment end according to the first comparison result, adjust parameters in the kitchen receiving and transporting system according to the first instruction and/or the second instruction, and obtain treatment result parameters after adjustment;
when the residual disposing amount is smaller than the disposing amount under the optimal working condition, reducing the disposing speed and/or increasing the vehicle speed, and when the residual disposing amount is larger than the disposing amount under the optimal working condition, increasing the disposing speed and/or reducing the vehicle speed;
the verification adjusting module is configured to compare the disposal result parameters with the predicted result parameters to obtain a second comparison result, predict and obtain verification result parameters through machine learning based on the second comparison result, and send a third instruction for increasing or reducing the vehicle speed to the factory returning vehicle of the receiving and transporting end and/or send a fourth instruction for increasing or reducing the disposal speed to the disposal equipment of the disposed end according to the verification result parameters; and
and the circulation module is configured to repeatedly execute the residual treatment amount prediction module to the verification adjustment module, repeatedly calculate the time of returning to the factory and predict the residual treatment amount when returning to the factory until the residual treatment amount approaches the optimal working condition treatment amount.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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