CN110706760B - Method and system for optimizing parameters of fluid body material - Google Patents
Method and system for optimizing parameters of fluid body material Download PDFInfo
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Abstract
The invention discloses a method and a system for optimizing parameters of a fluid material. Wherein, the method comprises the following steps: detecting construction quality parameters of the constructed fluid material; feeding back and optimizing the material parameter of the fluid material according to the construction quality parameter and a first incidence relation between the material parameter of the fluid material and the construction quality parameter, wherein the first incidence relation is determined by training a first neural network according to a historical material parameter and a historical construction quality parameter, and the historical material parameter is the material parameter of the fluid material prepared by a preparation device of the fluid material; the historical construction quality parameters are construction quality parameters of the construction robot after construction through the fluid material is completed. The invention solves the technical problems of poor effect and low efficiency caused by manual operation of preparation and construction of the fluid material in the related technology.
Description
Technical Field
The invention relates to the field of materials, in particular to a method and a system for optimizing parameters of a fluid material.
Background
Fluid materials, such as caulks, putties, mortar, and the like, are common construction materials in the construction industry. At present, the fluid body management in construction sites of the building industry is basically performed in an extensive way. Raw materials are randomly placed, the usage amount is determined according to the habit of constructors, the whole consumption amount of the materials cannot be finely calculated and quantified, and the waste or shortage of construction materials is easily caused. The existing preparation process is manual on-site allocation, the quality of a finished product is judged by depending on the experience of workers, and effective quality management cannot be formed.
Along with the gradual use of the construction robot, the construction site provides new requirements for the current traditional construction process, including how to meet the material supply of the quick consumption of the construction robot, how to realize the effective transition from the traditional construction process to the use of the novel construction robot, and the research and development of the fluid material stirring equipment and the carrying robot are developed. At present, the intelligent fluid material supply system is urgently needed to realize the lifting of the stirring process level and solve the problems of receiving, sending, scheduling and dispatching management of the feeding requirements of a construction robot, a fluid material stirring device and a carrying robot, so that the interconnection, high efficiency and high quality operation of the final site field devices is realized.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a system for optimizing parameters of a fluid material, which are used for at least solving the technical problems of poor effect and low efficiency caused by manual operation of preparation and construction of the fluid material in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a method for optimizing parameters of a fluid material, including: detecting construction quality parameters of the constructed fluid material; feeding back and optimizing the material parameters of the fluid material according to the construction quality parameters and a first incidence relation between the material parameters of the fluid material and the construction quality parameters, wherein the first incidence relation is determined by training a first neural network according to historical material parameters and historical construction quality parameters, and the historical material parameters are the material parameters of the fluid material prepared by the preparation device of the fluid material; the historical construction quality parameters are construction quality parameters of the construction robot after the construction of the fluid material is completed.
Optionally, after optimizing the material parameters of the fluidized material, the method further includes: feeding back and optimizing the device parameters of the preparation device of the fluid material through the optimized material parameters and a second incidence relation between the device parameters of the preparation device and the material parameters, wherein the second incidence relation is determined by training a second neural network through historical device parameters and historical material parameters, and the historical device parameters are historical device parameters of the preparation device of the fluid material.
Optionally, the material parameters of the fluidized material include at least one of: a concentration value of the fluid material, a viscosity value of the fluid material, a particle size value of the fluid material; the construction quality parameters at least comprise one of the following parameters: flatness and chromatic aberration of the working surface; the device parameters include at least one of: current value, ash ratio value, pressure value, temperature value and humidity value.
Optionally, the first neural network is a multilayer neural network, and before the material parameter of the fluid material is fed back and optimized through the construction quality parameter and the first incidence relation between the material parameter of the fluid material and the construction quality parameter, the method further includes: determining a first transition array through the intermediate parameters of each layer of neural network; determining a first calculation function of an input layer, an intermediate layer and an output layer of the first neural network according to the first transition array, wherein the input layer of the first neural network corresponds to the device parameter, and the output layer of the first neural network corresponds to the material parameter; and taking the first calculation function as the first association relation.
Optionally, the second neural network is a multilayer neural network, and before the feeding back and optimizing the device parameter through the material parameter and the second association relationship between the material parameter and the device parameter, the method further includes: determining a second transition array through the intermediate parameters of each layer of neural network; determining a second calculation function of an input layer, a middle layer and an output layer of the second neural network according to the second transition array, wherein the input layer of the second neural network corresponds to the device parameter, and the output layer of the second neural network corresponds to the material parameter; and taking the second calculation function as the second association relation.
Optionally, the detecting of the construction quality parameters of the constructed fluid material includes: detecting construction quality parameters of the constructed fluid material through an operation system of a construction site; wherein the operating system comprises at least one of: the system comprises a construction robot, a material robot, a detection robot and a cloud service system which is in communication connection with the construction robot, the material robot and the detection robot.
According to another aspect of the embodiments of the present invention, there is also provided a device for optimizing parameters of a fluid material, including: the detection module is used for detecting construction quality parameters of the constructed fluid material; the first optimization module is used for feeding back and optimizing the material parameters of the fluid material according to the construction quality parameters and a first incidence relation between the material parameters of the fluid material and the construction quality parameters, wherein the first incidence relation is determined by training a first neural network through historical material parameters and historical construction quality parameters, and the historical material parameters are the material parameters of the fluid material prepared by the fluid material preparation device; the historical construction quality parameters are construction quality parameters of the construction robot after the construction of the fluid material is completed.
Optionally, the method further includes: and a second optimization module, configured to feed back and optimize the device parameter of the preparation device of the fluid material according to the optimized material parameter and a second association relationship between the device parameter of the preparation device and the material parameter, where the second association relationship is determined by training a second neural network according to a historical device parameter and a historical material parameter, and the historical device parameter is a historical device parameter of the preparation device of the fluid material.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, a device in which the storage medium is located is controlled to execute any one of the above methods.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including at least one processor, and at least one memory and a bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to call program instructions in the memory to perform a method of parameter optimization of a fluid material as described in any one of the above.
According to another aspect of the embodiments of the present invention, there is also provided a system for optimizing parameters of a fluid material, including: a fluid material site construction device, the fluid material parameter optimization device of any one of claims 7 to 8; the fluid material field construction device is used for constructing the fluid material, collecting construction quality parameters of the fluid material field construction device, and optimizing and adjusting the material parameters of the fluid material input into the fluid material field construction device according to the first incidence relation determined by the fluid material parameter optimization device, so that the material parameters of the fluid material meet the requirements of the construction quality parameters.
Optionally, the method further includes: a fluid material preparation device; the fluid material preparation device is used for preparing the fluid material, collecting device parameters of the fluid material preparation device, and optimizing and adjusting the operation parameters of the fluid material preparation device according to a second incidence relation determined by the fluid material parameter optimization device, so that the material parameters of the fluid material output by the fluid material preparation device meet the requirements.
Optionally, the apparatus for on-site construction of a fluidized material comprises: a construction robot, a material robot, a construction site detection robot deployed on a construction site; and the building site construction monitoring cloud processing system is in communication connection with the building construction robot, the material robot and the construction site detection robot.
Optionally, the apparatus for preparing a fluid material includes: a container, a motor, an actuating tip, and a detection mechanism; the container is used for containing raw materials for preparing the fluid material; the motor provides power for the execution tail end, and the execution tail end stirs the raw materials in the container; the detection mechanism comprises a fluid body characteristic sensor and is used for collecting material parameters of the stirred fluid body material.
Optionally, the container is further provided with a temperature sensor, a humidity sensor, a pressure sensor and a weight sensor; the humidity sensor and the temperature sensor are arranged on the rotating shaft at the execution tail end and are used for collecting the temperature and the humidity of the raw materials which are being stirred; the pressure sensors are arranged on the inner wall of the container and used for collecting the pressure of the raw materials on the inner wall of the container; the weight sensor is arranged at the bottom of the container and used for collecting the weight of the raw material to the container.
In the embodiment of the invention, the construction quality parameters of the constructed fluid material are detected; feeding back and optimizing the material parameter of the fluid material according to the construction quality parameter and a first incidence relation between the material parameter of the fluid material and the construction quality parameter, wherein the first incidence relation is determined by training a first neural network according to a historical material parameter and a historical construction quality parameter, and the historical material parameter is the material parameter of the fluid material prepared by a preparation device of the fluid material; the historical construction quality parameters are the construction quality parameters after the construction robot finishes the construction through the fluid material, the material parameters are fed back by detecting the construction quality through the first incidence relation between the material parameters and the construction quality parameters, and the purpose of optimizing the material parameters of the fluid material is achieved, so that the automatic construction and automatic feedback of the fluid material are realized, the preparation effect of the fluid material is improved, the technical effect of optimizing efficiency is improved, and the technical problems that the preparation and the construction of the fluid material in the related technology are manual operation, the effect is poor and the efficiency is low are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for optimizing parameters of a fluent material in accordance with an embodiment of the present invention;
FIG. 2 is a schematic view of a fluid preparation apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a device parameter and material parameter neural network model according to an embodiment of the present invention;
FIG. 4 is a schematic view of a fluid material field installation according to an embodiment of the present invention;
FIG. 5 is a schematic illustration of a material parameter and construction quality parameter neural network model according to an embodiment of the present invention;
FIG. 6 is a flow chart of a method of optimization of a parameter of a fluent material in accordance with an embodiment of the present invention;
FIG. 7 is a schematic illustration of a device parameter, material parameter and construction quality parameter neural network model according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a device for optimizing parameters of a fluid material according to an embodiment of the present invention.
The reference numbers of the above figures are as follows:
1-fluid material preparation device container; 2, feeding a pipe; 3-a current sensor; 4-fluid material preparing device motor; 5, a flow control device; 6, a discharge pipe; 7-fluid material detection device container; 8-a fluid property sensor; 9-pressure strain sensors (arrays); 10-a weight sensor; 11-fluid material preparation device execution end; 12-a temperature sensor; 13-a humidity sensor; 14, building site construction monitoring cloud processing system; 15-construction site; 16-a fluid material working area; 17-a building construction robot; 18-a material robot; 19-detection robot on construction site.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a method for parameter optimization of a fluid material, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flow chart of a method for optimizing parameters of a fluid material according to an embodiment of the present invention, as shown in fig. 1, the method comprising the steps of:
step S102, detecting construction quality parameters of the constructed fluid material;
step S104, feeding back and optimizing the material parameters of the fluid material according to the construction quality parameters and a first incidence relation between the material parameters of the fluid material and the construction quality parameters, wherein the first incidence relation is determined by training the first neural network according to historical material parameters and historical construction quality parameters, and the historical material parameters are the material parameters of the fluid material prepared by the fluid material preparation device; the historical construction quality parameters are construction quality parameters of the construction robot after construction through the fluid material is completed.
Through the steps, detecting the construction quality parameters of the constructed fluid material; feeding back and optimizing the material parameter of the fluid material according to the construction quality parameter and a first incidence relation between the material parameter of the fluid material and the construction quality parameter, wherein the first incidence relation is determined by training a first neural network according to a historical material parameter and a historical construction quality parameter, and the historical material parameter is the material parameter of the fluid material prepared by a preparation device of the fluid material; the historical construction quality parameters are the construction quality parameters after the construction robot finishes the construction through the fluid material, the material parameters are fed back by detecting the construction quality through the first incidence relation between the material parameters and the construction quality parameters, and the purpose of optimizing the material parameters of the fluid material is achieved, so that the automatic construction and automatic feedback of the fluid material are realized, the preparation effect of the fluid material is improved, the technical effect of optimizing efficiency is improved, and the technical problems that the preparation and the construction of the fluid material in the related technology are manual operation, the effect is poor and the efficiency is low are solved.
The fluid material can be concrete, namely a mixture of lime powder, stones and water, can be mortar, lime powder, a mixture of sand and water, can be a caulking agent, can be putty, namely a mixture of lime powder and water, and can also be various coatings and the like. The above-mentioned parameter optimization method for the fluidized material may be to optimize material parameters of the fluidized material, for example, a mixing ratio of a mixture of the fluidized material, a kind of the mixture, a viscosity, a particle value, a concentration, and the like.
For example, in the case that the fluid material is mortar, if the proportion of lime powder in a mixture of the mortar is too small, the hardness of the solidified mortar after construction is insufficient, which seriously affects the construction quality, and if the proportion of lime powder in the mortar is too high, the solidification time is prolonged, and the cost is increased. The construction quality parameters can be hardness, density, operation flatness, chromatic aberration and the like of the fluid after construction and coagulation for a certain time.
The above-mentioned association relationship may be determined by mathematical modeling, may be a functional relationship, and may also be determined by machine learning. In this embodiment, a machine learning manner is adopted to implement the first association relationship between the fluid material parameter and the construction quality parameter. Specifically, the first neural network is trained and determined according to historical material parameters and historical construction quality parameters, the historical material parameters and the historical construction quality parameters are training data of the first neural network, the first neural network is trained through multiple groups of training data until the first neural network converges, an input array, an output array and multiple intermediate layer transition arrays of the first neural network are determined, and then calculation functions of an input layer, an intermediate layer and an output layer are determined according to the input array, the output array and the transition arrays, namely the first incidence relation.
The historical material parameters and the historical construction quality parameters are grouped, specifically, each group of the historical material parameters and the historical construction quality parameters are in the previous construction records, and under the historical material parameters, after the construction is completed, the construction quality parameters are measured, so that the corresponding historical construction quality parameters are determined. The historical material parameters are material parameters of the fluid material prepared by the fluid material preparation device; the historical construction quality parameters are construction quality parameters of the construction robot after construction through the fluid material is completed.
Optionally, after optimizing the material parameters of the fluid material, the method further includes: and feeding back and optimizing the device parameters of the fluid material preparation device through the optimized material parameters and a second incidence relation between the device parameters of the preparation device and the material parameters, wherein the second incidence relation is determined by training a second neural network through historical device parameters and historical material parameters, and the historical device parameters are historical device parameters of the fluid material preparation device.
The material parameters of the fluid body may cause the construction quality parameters to differ, so that a first correlation exists between the material parameters and the construction quality parameters. However, from another perspective, the material parameter may also be determined by the device parameter, and there is also a correlation between the material parameter and the device parameter.
The device parameter is a device parameter of a preparation device for preparing the fluid body, and the device parameter can be a current value, an ash ratio value, a pressure value, a temperature value, a humidity value, a vibration frequency, a vibration amplitude and the like.
The correlation between the material parameter and the device parameter may be determined by mathematical modeling, may be a functional relationship, or may be determined by machine learning. In this embodiment, a machine learning manner is adopted to implement the second association relationship between the fluid material parameter and the device parameter. Specifically, the second neural network is trained and determined according to historical material parameters and historical device parameters, the historical material parameters and the historical device parameters are training data of the second neural network, the second neural network is trained through multiple groups of training data until the second neural network converges, an input array, an output array and a plurality of intermediate layer transition arrays of the second neural network are determined, and then calculation functions of the input layer, the intermediate layer and the output layer are determined according to the input array, the output array and the transition arrays, namely the second association relationship.
Specifically, each set of the historical material parameters and the historical device parameters is in a previous construction record, the preparation device prepares the fluid body under the historical device parameters, prepares the obtained fluid body, and determines the detected material parameters to be the historical material parameters corresponding to the historical device parameters by detecting the material parameters of the fluid body. The historical apparatus parameters are historical apparatus parameters of a preparation apparatus for the fluid material.
Optionally, the material parameters of the fluid material include at least one of: a concentration value of the fluid material, a viscosity value of the fluid material, a particle size value of the fluid material; the construction quality parameters at least comprise one of the following parameters: flatness of the working surface and chromatic aberration of the working surface.
Optionally, the device parameter includes at least one of: current value, ash ratio value, pressure value, temperature value and humidity value.
The calculation functions of the input layer, the intermediate layer and the output layer are determined according to the input array, the output array and the transition array, namely the first association relationship. Specifically, the first neural network is a multilayer neural network, and before the material parameter of the fluid material is fed back and optimized through the construction quality parameter and the first incidence relation between the material parameter of the fluid material and the construction quality parameter, the method further comprises: determining a first transition array through the intermediate parameters of each layer of neural network; determining a first calculation function of an input layer, an intermediate layer and an output layer of the first neural network according to the first transition array, wherein the input layer of the first neural network corresponds to device parameters, and the output layer of the first neural network corresponds to material parameters; and taking the first calculation function as the first association relation.
And determining a calculation function of the input layer, the intermediate layer and the output layer according to the input array, the output array and the transition array, namely the second association relation. Specifically, the second neural network is a multilayer neural network, and before the device parameters are fed back and optimized through the material parameters and the second incidence relation between the material parameters and the device parameters, the method further includes: determining a second transition array through the intermediate parameters of each layer of neural network; determining a second calculation function of an input layer, a middle layer and an output layer of the second neural network according to the second transition array, wherein the input layer of the second neural network corresponds to device parameters, and the output layer of the second neural network corresponds to material parameters; and taking the second calculation function as a second association relation.
Optionally, the detecting of the construction quality parameters of the constructed fluid material includes: detecting construction quality parameters of the constructed fluid material through an operation system on a construction site; wherein the operating system comprises at least one of: the system comprises a construction robot, a material robot, a detection robot and a cloud service system which is in communication connection with the construction robot, the material robot and the detection robot.
It should be noted that this embodiment also provides an optional implementation, and details of this implementation are described below.
The implementation mode faces the main problems that the sensor network building and multi-sensor fusion technology is applied to a building site; and an autonomous deep learning algorithm and optimization based on the multilayer neural network. Deployment of wireless networks (WiFi, 4G, 5G, etc.) at a construction site. The deployment of building construction robots, building site automation equipment and building site logistics equipment on a construction site.
The embodiment can provide the supply of raw materials, particularly fluid materials for the automatic, intelligent and unmanned construction of the construction robot on the construction site. The defect that the current preparation process of the fluid material depends on manual experience is overcome, the digitization and the automation of the preparation of the fluid material are realized, and the fluid material is flexible and controllable according to the construction environment and can be independently and deeply learned to understand the scene of a building site. The defect that the current fluid material construction site depends on manual experience is overcome, the digitization and the automation of the fluid material site operation are realized, the operation is flexible and controllable according to the construction environment, and the construction site scene is understood through autonomous deep learning.
Fig. 2 is a schematic view of a fluid material preparation apparatus according to an embodiment of the present invention, and as shown in fig. 2, the fluid material preparation and detection integrated system includes two large sub-modules: a fluid material preparation device and a fluid material characteristic detection device.
The fluid material preparation device comprises and is not limited to the relevant equipment: the device comprises a fluid material preparation device container 1, a feeding pipe 2, a current inductor 3, a fluid material preparation device motor 4, a flow control device discharging pipe 5, a discharging pipe 6, a pressure strain sensor 9, a weight sensor 10, a fluid material preparation device execution tail end 11, a temperature sensor 12 and a humidity sensor 13.
Fluid material characteristic detection device's pan feeding pipe and above-mentioned discharging pipe 6 intercommunication, fluid material characteristic detection device includes: a fluid material detecting device container 7 and a fluid characteristic sensor 8.
The fluid material preparation device can complete the preparation work of the fluid material and simultaneously complete the data acquisition in the preparation process, and comprises and is not limited to: the method comprises the steps of obtaining a current value (I) by using a current sensor, obtaining a water-ash ratio value (r) by using a flow control device and a weight sensor, obtaining a pressure value (P) by using pressure strain sensors distributed in an array, obtaining a temperature value (T) by using a temperature sensor, and obtaining a humidity value (H) by using a humidity sensor.
The fluid material characteristic detection device can complete the detection of the characteristics of the finished fluid material, and the fluid characteristic sensor is used for obtaining relevant characteristic data, including and not limited to: a concentration value (c) of the finished fluid material, a viscosity value (eta) of the finished fluid material and a particle value phi of the finished fluid material.
FIG. 3 is a schematic diagram of a device parameter and material parameter neural network model according to an embodiment of the present invention, as shown in FIG. 3, with parameters obtained by a fluid material preparation device as input layers, including but not limited to: the current value (I), ash ratio value (r), pressure value (P), temperature value (T), humidity value (H) and the like form an output array xm.
The parameters obtained by the fluid material characteristic detection device are used as output layers, including but not limited to: the concentration value (c) of the finished product of the fluid material, the viscosity value (eta) of the finished product of the fluid material, the particle value (phi) of the finished product of the fluid material and the like form an output array y[m]。
The intermediate layer is a deep learning algorithm based on a multilayer neural network, and intermediate parameters of each layer can form a transition array a1 [m]，a2 [m]……an [m]。
Therefore, the calculation functions of the output layer, the intermediate layer and the output layer can be obtained:
y[m]＝f(x[m]·a1 [m]·a2 [m]·…·an [m])
through the continuous change of the values of the input layer and the output layer, the whole deep learning algorithm can be trained, and the mapping relation between the input layer parameters and the output layer parameters is realized.
Fig. 4 is a schematic diagram of a fluid material on-site working apparatus according to an embodiment of the present invention, in which, as shown in fig. 4, a construction robot 17, a material robot 18, and a construction site inspection robot 19 are disposed at a construction site 15, and a fluid material preparation inspection integrated system is disposed at the material robot.
The construction robot 17 completes construction related to the construction site fluid material operation area 16, the material robot 18 completes preparation and detection of the fluid material and timely supplies the fluid material to the construction robot, and the construction site detection robot 19 completes real-time detection and key index verification of construction quality of the fluid material operation area.
The building construction robot 17, the material robot 18 and the construction site detection robot 19 are connected with the building site construction monitoring cloud processing system 14 through a wireless network, and intercommunication interconnection and data sharing of the whole building site are achieved. The building site construction monitoring cloud processing system can uniformly collect, process and issue the data of each robot, and can finish centralized storage and analysis of data of a plurality of construction buildings and a plurality of construction projects.
Construction site inspection robot 19 includes and not only is limited to visual sensor, ultrasonic sensor, laser sensor etc. through the detecting system who carries by oneself, can realize acquireing to the regional key index parameter of matter material operation, include and not only be limited to: work surface flatness (σ), work surface color difference (E), and the like.
Fig. 5 is a schematic diagram of a neural network model of material parameters and construction quality parameters according to an embodiment of the present invention, and as shown in fig. 5, the parameters obtained by the fluid material property detection device are used as input layers, including but not limited to: the concentration value (c) of the finished product of the fluid material, the viscosity value (eta) of the finished product of the fluid material, the particle value (phi) of the finished product of the fluid material and the like form an input array y[m]。
The parameters obtained by the construction site detection robot are used as an output layer, and the method comprises the following steps: flatness of working surface(σ), working plane chromatic aberration (E), etc., to form an output array z[m]。
The intermediate layer is a deep learning algorithm based on a multilayer neural network, and intermediate parameters of each layer can form a transition array b1 [m]，b2 [m]……bn [m]。
Therefore, the calculation functions of the output layer, the intermediate layer and the output layer can be obtained:
z[m]＝g(y[m]·b1 [m]·b2 [m]·…·bn [m])
through the continuous change of the values of the input layer and the output layer, the whole deep learning algorithm can be trained, and the mapping relation between the parameters of the input layer and the parameters of the output layer is realized.
Fig. 6 is a flowchart of a method for optimizing parameters of a fluid material according to an embodiment of the present invention, fig. 7 is a schematic diagram of a neural network model of device parameters, material parameters and construction quality parameters according to an embodiment of the present invention, and as shown in fig. 6 and 7, the system for optimizing parameters of a fluid material is a system having two levels of a multilayer neural network and capable of performing algorithm function and middle layer parameter array optimization through deep learning.
The first-stage process is that the preparation parameters of the fluid material pass through a first-stage multilayer neural network, are mapped with the parameters of the finished product of the fluid material, deep learning training is continuously carried out, and a first-stage function and an intermediate layer parameter array are continuously optimized.
The second-stage flow is that the parameters of the finished fluid material pass through a second-stage multilayer neural network, are mapped with the construction quality parameters of the fluid material, deep learning training is continuously carried out, and a second-stage function and an intermediate layer parameter array are continuously optimized.
Finally, the preparation parameters of the fluid material are directly mapped with the construction quality parameters of the fluid material through a complex multilayer neural network, so that the self-adaption and autonomous learning optimization of a complex system is completed.
Fig. 8 is a schematic view of a parameter optimizing apparatus for a fluid material according to an embodiment of the present invention, and as shown in fig. 8, according to another aspect of the embodiment of the present invention, there is also provided a parameter optimizing apparatus for a fluid material, including: a detection module 82 and a first optimization module 84, which are described in detail below.
A detection module 82 for detecting construction quality parameters of the constructed fluid material; a first optimization module 84, connected to the detection module 82, for feeding back and optimizing the material parameter of the fluid material according to the construction quality parameter and a first association relationship between the material parameter of the fluid material and the construction quality parameter, where the first association relationship is determined by training the first neural network according to a historical material parameter and a historical construction quality parameter, and the historical material parameter is the material parameter of the fluid material prepared by the fluid material preparation device; the historical construction quality parameters are construction quality parameters of the construction robot after construction through the fluid material is completed.
By the device, a detection module 82 is adopted to detect the construction quality parameters of the constructed fluid material; the first optimization module 84 feeds back and optimizes the material parameters of the fluid material according to the construction quality parameters and a first incidence relation between the material parameters of the fluid material and the construction quality parameters, wherein the first incidence relation is determined by training the first neural network through historical material parameters and historical construction quality parameters, and the historical material parameters are the material parameters of the fluid material prepared by the fluid material preparation device; the historical construction quality parameters are the construction quality parameters after the construction robot finishes the construction through the fluid material, the material parameters are fed back by detecting the construction quality through the first incidence relation between the material parameters and the construction quality parameters, and the purpose of optimizing the material parameters of the fluid material is achieved, so that the automatic construction and automatic feedback of the fluid material are realized, the preparation effect of the fluid material is improved, the technical effect of optimizing efficiency is improved, and the technical problems that the preparation and the construction of the fluid material in the related technology are manual operation, the effect is poor and the efficiency is low are solved.
Optionally, the method further includes: and the second optimization module is used for feeding back and optimizing the device parameters of the preparation device of the fluid material according to the optimized material parameters and a second incidence relation between the device parameters of the preparation device and the material parameters, wherein the second incidence relation is determined by training the second neural network through historical device parameters and historical material parameters, and the historical device parameters are historical device parameters of the preparation device of the fluid material.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and where the apparatus in which the storage medium is located is controlled to execute the method of any one of the above when the program is executed.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including at least one processor, and at least one memory and a bus connected to the processor; the processor and the memory complete mutual communication through a bus; the processor is adapted to call program instructions in the memory to perform a method of parameter optimization of a fluid material as described in any one of the above.
According to another aspect of the embodiments of the present invention, there is also provided a system for optimizing parameters of a fluid material, including: a fluid material site construction device, a parameter optimization device for any one of the above fluid materials; the fluid material on-site construction device is used for constructing the fluid material, collecting construction quality parameters of the fluid material on-site construction device, and optimizing and adjusting the material parameters of the fluid material input into the fluid material on-site construction device according to the first incidence relation determined by the fluid material parameter optimization device so as to enable the material parameters of the fluid material to meet the requirements of the construction quality parameters.
Optionally, the method further includes: a fluid material preparation device; the fluid material preparation device is used for preparing the fluid material, collecting device parameters of the fluid material preparation device, and optimizing and adjusting the operation parameters of the fluid material preparation device according to the second incidence relation determined by the fluid material parameter optimization device, so that the material parameters of the fluid material output by the fluid material preparation device meet the requirements.
As shown in fig. 3, the fluid material field construction apparatus may alternatively include: a construction robot, a material robot, a construction site detection robot deployed on a construction site; and the building site construction monitoring cloud processing system is in communication connection with the building construction robot, the material robot and the construction site detection robot.
The construction robot completes construction related to a construction site fluid material operation area, the material robot completes preparation and detection of the fluid material and timely supplies the material to the construction robot, and the construction site detection robot completes real-time detection and key index verification of construction quality of the fluid material operation area. The building site construction monitoring cloud processing system is used for achieving intercommunication interconnection and data sharing of the whole building site. The building site construction monitoring cloud processing system can uniformly collect, process and issue the data of each robot, and can finish centralized storage and analysis of data of a plurality of construction buildings and a plurality of construction projects. Construction site inspection robot includes and not only is limited to visual sensor, ultrasonic sensor, laser sensor etc. through the detecting system who self carries, can realize acquireing to the regional key index parameter of matter material operation, include and not only be limited to: flatness of work surface, color difference of work surface, etc.
As shown in fig. 2, the fluid material preparing apparatus may alternatively include: a container, a motor, an actuating tip, and a detection mechanism; the container is used for containing raw materials for preparing the fluid material; the motor provides power for the execution tail end, and the execution tail end stirs the raw materials in the container; the detection mechanism comprises a fluid body characteristic sensor for collecting the material parameters of the stirred fluid body material.
The container may further include a material inlet pipe for inputting the raw material into the container, and a material outlet pipe for extracting the stirred fluid material from the container, and the detecting mechanism may be the fluid material detecting device, and may include a fluid material detecting device container, and a fluid property sensor. The fluid property sensor may include a plurality of different kinds of sensors, for example, a hardness sensor, a particle size sensor, a concentration sensor, a viscosity sensor, and the like.
Optionally, the container is further provided with a temperature sensor, a humidity sensor, a pressure sensor and a weight sensor; the humidity sensor and the temperature sensor are arranged on a rotating shaft at the tail end of the execution unit and are used for collecting the temperature and the humidity of the raw materials which are being stirred; the pressure sensors are arranged on the inner wall of the container and used for collecting the pressure of the raw materials on the inner wall of the container; the weight sensor is arranged at the bottom of the container and is used for collecting the weight of the raw materials to the container.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (14)
1. A method for optimizing parameters of a fluid material, comprising:
detecting construction quality parameters of the constructed fluid material;
feeding back and optimizing the material parameters of the fluid material according to the construction quality parameters and a first incidence relation between the material parameters of the fluid material and the construction quality parameters, wherein the first incidence relation is determined by training a first neural network according to historical material parameters and historical construction quality parameters, and the historical material parameters are the material parameters of the fluid material prepared by the preparation device of the fluid material; the historical construction quality parameters are construction quality parameters of the construction robot after the construction of the fluid material is completed;
after optimizing the material parameters of the fluid material, the method further comprises the following steps:
feeding back and optimizing the device parameters of the preparation device of the fluid material through the optimized material parameters and a second incidence relation between the device parameters of the preparation device and the material parameters, wherein the second incidence relation is determined by training a second neural network through historical device parameters and historical material parameters, and the historical device parameters are historical device parameters of the preparation device of the fluid material.
2. The method of claim 1, wherein the material parameters of the fluidized material include at least one of: a concentration value of the fluid material, a viscosity value of the fluid material, a particle size value of the fluid material;
the construction quality parameters at least comprise one of the following parameters: flatness and chromatic aberration of the working surface;
the device parameter includes at least one of: current value, ash ratio value, pressure value, temperature value and humidity value.
3. The method of claim 1, wherein the first neural network is a multi-layer neural network, and wherein the method further comprises, before the feeding back and optimizing the material parameter of the fluid material through the construction quality parameter and the first correlation between the material parameter of the fluid material and the construction quality parameter:
determining a first transition array through the intermediate parameters of each layer of neural network;
determining a first calculation function of an input layer, an intermediate layer and an output layer of the first neural network according to the first transition array, wherein the construction quality parameters are correspondingly input into the input layer of the first neural network, and the material parameters are correspondingly output from the output layer of the first neural network;
and taking the first calculation function as the first association relation.
4. The method of claim 1, wherein before the second neural network is a multi-layer neural network and the device parameters are fed back and optimized through the material parameters and the second correlations of the material parameters and the device parameters, the method further comprises:
determining a second transition array through the intermediate parameters of each layer of neural network;
determining a second calculation function of an input layer, a middle layer and an output layer of the second neural network according to the second transition array, wherein the input layer of the second neural network corresponds to inputting the device parameter, and the output layer of the second neural network corresponds to outputting the material parameter;
and taking the second calculation function as the second association relation.
5. The method of claim 1, wherein detecting a construction quality parameter of the constructed fluid material comprises:
detecting construction quality parameters of the constructed fluid material through an operation system on a construction site;
wherein the operating system comprises at least one of: the system comprises a construction robot, a material robot, a detection robot and a cloud service system which is in communication connection with the construction robot, the material robot and the detection robot.
6. An apparatus for optimizing parameters of a fluid material, comprising:
the detection module is used for detecting construction quality parameters of the constructed fluid material;
the first optimization module is used for feeding back and optimizing the material parameters of the fluid material according to the construction quality parameters and a first incidence relation between the material parameters of the fluid material and the construction quality parameters, wherein the first incidence relation is determined by training a first neural network through historical material parameters and historical construction quality parameters, and the historical material parameters are the material parameters of the fluid material prepared by the fluid material preparation device; the historical construction quality parameters are construction quality parameters of the construction robot after the construction of the fluid material is completed;
after optimizing the material parameters of the fluidized material, the method further comprises the following steps:
feeding back and optimizing the device parameters of the preparation device of the fluid material through the optimized material parameters and a second incidence relation between the device parameters of the preparation device and the material parameters, wherein the second incidence relation is determined by training a second neural network through historical device parameters and historical material parameters, and the historical device parameters are historical device parameters of the preparation device of the fluid material.
7. The apparatus of claim 6, further comprising:
and a second optimization module, configured to feed back and optimize the device parameter of the preparation device of the fluid material according to the optimized material parameter and a second association relationship between the device parameter of the preparation device and the material parameter, where the second association relationship is determined by training a second neural network according to a historical device parameter and a historical material parameter, and the historical device parameter is a historical device parameter of the preparation device of the fluid material.
8. A storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method for optimizing parameters of a fluid material according to any one of claims 1 to 6.
9. An electronic device comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to call program instructions in the memory to perform a method of parameter optimization of a fluid material according to any one of claims 1 to 6.
10. A system for optimizing parameters of a fluid material, comprising: a fluid material site operation device, the parameter optimization device of fluid material of any one of claims 6 to 7;
the fluid material on-site construction device is used for constructing the fluid material, collecting construction quality parameters of the fluid material on-site construction device, and optimizing and adjusting the material parameters of the fluid material input into the fluid material on-site construction device according to a first incidence relation determined by the fluid material parameter optimization device so as to enable the material parameters of the fluid material to meet the requirements of the construction quality parameters;
after optimizing the material parameters of the fluidized material, the method further comprises the following steps:
feeding back and optimizing the device parameters of the preparation device of the fluid material through the optimized material parameters and a second incidence relation between the device parameters of the preparation device and the material parameters, wherein the second incidence relation is determined by training a second neural network through historical device parameters and historical material parameters, and the historical device parameters are historical device parameters of the preparation device of the fluid material.
11. The system of claim 10, further comprising: a fluid material preparation device;
the fluid material preparation device is used for preparing the fluid material, acquiring device parameters of the fluid material preparation device, and optimizing and adjusting the operating parameters of the fluid material preparation device according to the second incidence relation determined by the fluid material parameter optimization device, so that the material parameters of the fluid material prepared and output by the fluid material preparation device meet the requirements.
12. The system of claim 10, wherein the fluidized material field operation device comprises: a construction robot, a material robot, a construction site detection robot deployed on a construction site; and the building site construction monitoring cloud processing system is in communication connection with the building construction robot, the material robot and the construction site detection robot.
13. The system of claim 11, wherein the fluidized material preparation device comprises: a container, a motor, an actuating tip, and a detection mechanism;
the container is used for containing raw materials for preparing the fluid material; the motor provides power for the execution tail end, and the execution tail end stirs the raw materials in the container;
the detection mechanism comprises a fluid body characteristic sensor and is used for collecting material parameters of the stirred fluid body material.
14. The system of claim 13, wherein the container is further provided with a temperature sensor, a humidity sensor, a pressure sensor and a weight sensor;
the humidity sensor and the temperature sensor are arranged on the rotating shaft at the execution tail end and are used for collecting the temperature and the humidity of the raw materials which are being stirred;
the pressure sensors are arranged on the inner wall of the container and used for collecting the pressure of the raw materials on the inner wall of the container;
the weight sensor is arranged at the bottom of the container and is used for collecting the weight of the raw material to the container.
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