CN113932875B - Method for determining the scale volume of a mixing station, processor and mixing station - Google Patents
Method for determining the scale volume of a mixing station, processor and mixing station Download PDFInfo
- Publication number
- CN113932875B CN113932875B CN202111081956.5A CN202111081956A CN113932875B CN 113932875 B CN113932875 B CN 113932875B CN 202111081956 A CN202111081956 A CN 202111081956A CN 113932875 B CN113932875 B CN 113932875B
- Authority
- CN
- China
- Prior art keywords
- value
- credible
- data
- mixing
- material data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F17/00—Methods or apparatus for determining the capacity of containers or cavities, or the volume of solid bodies
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B28—WORKING CEMENT, CLAY, OR STONE
- B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
- B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
- B28C7/02—Controlling the operation of the mixing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B28—WORKING CEMENT, CLAY, OR STONE
- B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
- B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
- B28C7/04—Supplying or proportioning the ingredients
- B28C7/0422—Weighing predetermined amounts of ingredients, e.g. for consecutive delivery
- B28C7/044—Weighing mechanisms specially adapted therefor; Weighing containers
Landscapes
- Chemical & Material Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physics & Mathematics (AREA)
- Fluid Mechanics (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present application relates to the field of construction machinery, and in particular, to a method, processor, mixing station, storage system, and storage medium for determining a scale volume of a mixing station. The method comprises the following steps: acquiring material data of each mixing station, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station; uploading the material data to a storage device to obtain material data sets of all mixing stations; classifying the material data set; analyzing and processing the classified material data set, and determining a first credible maximum value of the material set weighing value, a second credible average value of the material density value and a third credible maximum value of the metering precision value; and inputting the first credible maximum value, the second credible average value and the third credible maximum value into a scale volume design reference model to obtain a target volume value of the weighing scale volume. By the scheme, the matching degree of the scale volume and a design value can be improved, and the volume redundancy is reduced.
Description
Technical Field
The present application relates to the field of construction machinery, and in particular, to a method, processor, mixing station, storage system, and storage medium for determining a scale volume of a mixing station.
Background
In the prior art, when a new mixing plant is designed, a designer determines the volume of a metering scale of the mixing plant according to the requirement of a mixing plant user and manual experience, the maximum dosage principle is usually adopted for design when the volume of the metering scale is designed to avoid the situation that the metering volume is insufficient to cause secondary metering during disc production, and the redundancy of the volume of the scale is increased during design.
In the prior art, the scale volume is designed mainly by manpower according to empirical values, so that actual production data cannot be well matched. Therefore, the situation that the scale volume of the actually designed mixing plant is too large, redundant waste is caused, and the matching degree with the mixing plant is poor often occurs.
Disclosure of Invention
The application aims to provide a method, a processor, a mixing station, a storage system and a storage medium for determining the volume of a metering scale of the mixing station, which can avoid redundant waste of materials caused by overlarge design value of the volume of the scale.
To achieve the above objects, the present application provides a method for determining a mixing station weigh scale volume, comprising:
acquiring material data of each mixing station, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station;
uploading the material data to a storage device to obtain material data sets of all the mixing stations;
classifying the material data set;
analyzing and processing the classified material data set, and determining a first credible maximum value of the material set weighing value, a second credible average value of the material density value and a third credible maximum value of the metering precision value;
and inputting the first credible maximum value, the second credible average value and the third credible maximum value into a scale volume design reference model to obtain a target volume value of the weighing scale volume.
In an embodiment of the present application, classifying the material data set includes: acquiring the area position of each mixing station; determining the mixing stations with the same area position as the mixing stations in the same area; classifying data of the mixing stations belonging to the same region in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to the mixing stations in each region.
In an embodiment of the present application, the method further comprises: obtaining the model of each mixing station; determining the mixing stations with the same model as the mixing stations with the same model; classifying the data of the mixing stations belonging to the same model in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value of the mixing stations of each model.
In an embodiment of the present application, classifying the material dataset further includes: classifying data of mixing stations with the same type in the material data set, and determining the material type corresponding to the material contained in the weighing scale of each mixing station; and classifying data in the material data set according to the material types to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to each material type.
In this embodiment of the present application, determining, according to analyzing and processing a classified material data set, a first reliable maximum value of a material set weighing value, a second reliable average value of a material density value, and a third reliable maximum value of a metering precision value includes: determining the material type corresponding to the material data; determining a standard difference value and an average value of material set weighing values corresponding to each material type, an average density value of material density values and a standard difference value and an average value of metering precision values corresponding to each material type aiming at each material type; processing the standard difference value and the average value of the material set weighing values corresponding to each material type by utilizing a normal distribution principle to obtain a first credible maximum value corresponding to each material type; determining a second credible average value according to the average density value of the material density values; and processing the standard difference value and the average value of the metering precision value corresponding to each material type by utilizing a normal distribution principle to obtain a third credible maximum value corresponding to each material type.
In the embodiment of the application, the scale volume design reference model determines the target volume value through the formula (1):
wherein V is a target volume value; m is a first credible maximum value;
is a second confidence average; k is the third trusted maximum.
A second aspect of the present application provides a processor configured to perform the method for determining a scale volume of a mixing station of any of the embodiments described above.
A third aspect of the application provides a mixing station, which includes a data acquisition device configured to acquire material data of each mixing station, where the material data includes a material set weighing value, a material density value, and a metering precision value of a metering scale of the mixing station;
the storage device is configured to receive the material data uploaded by the data transmission device;
the data transmission device is configured to upload the material data to the storage device so as to obtain material data sets of all the mixing stations;
and the processor described above.
The present application in a fourth aspect provides a production information data acquisition and storage system, comprising:
the above mixing stations, wherein there are a plurality of mixing stations;
the storage equipment is used for storing the material data of each mixing station; the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing plant;
the big data platform device is used for receiving the storage data in the storage device;
and the communication equipment is used for uploading the storage data in the storage equipment to the big data platform equipment.
A fifth aspect of the present application provides a machine-readable storage medium having instructions stored thereon for causing a machine to perform any one of the above-described methods for determining a scale volume of a mixing station.
According to the technical scheme, the material data of each mixing station are collected through the processor, wherein the material data comprise material set weighing value, material density value and metering precision value of the metering scale of the mixing station. The target volume value of the metering scale is obtained by classifying the collected material data, processing and analyzing the material data and inputting the processed and analyzed material data into the scale volume design reference model. In the technical scheme of this application, can acquire the stirring station through the collection of the material weighing value of setting for the material weighing value of the weigher of the stirring station and the actual material weighing value of weigher to combine the material density of every kind of material, predict the volumetric target volume value of balance through the design reference model of balance volume, give the reference value to improve the matching degree of balance volume and design value, reduce the redundancy and the waste of material.
Additional features and advantages of the present application will be described in detail in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application and not to limit the application. In the drawings:
FIG. 1 schematically illustrates a flow diagram of a method for determining a scale volume of a mixing station in accordance with an embodiment of the present application;
FIG. 2 schematically illustrates a schematic structural view of a mixing station according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating a production information data collection and storage system according to an embodiment of the present application;
fig. 4 schematically shows an internal structure diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are given by way of illustration and explanation only, not limitation.
It should be noted that if directional indications (such as up, down, left, right, front, and back … …) are referred to in the embodiments of the present application, the directional indications are only used to explain the relative positional relationship between the components, the movement situation, and the like in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are changed accordingly.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
Fig. 1 schematically shows a flow diagram of a method for determining a scale volume of a mixing station according to an embodiment of the present application. According to the technical scheme shown in fig. 1, material data of each mixing station are collected, the material data collected by the mixing stations are classified according to regions and types of the mixing stations, the classified material data are analyzed to obtain analyzed values, the analyzed material data values are input into a scale volume design reference model to obtain target volume values of the weighing scales, and therefore the target volume values of the weighing scales corresponding to each material in the mixing stations of the same type in the same region are determined, the target volume values of the weighing scales corresponding to the materials in the mixing stations of the same type in the region are standardized, and the problems that the volume redundancy of the weighing scales and the poor matching degree caused by the fact that the mixing stations are designed according to manual experience are solved.
In one embodiment of the present application, as shown in FIG. 1, there is provided a method for determining a scale volume of a mixing station, comprising the steps of:
step 101, material data of each mixing station are obtained, wherein the material data comprise a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station.
The processor may collect material data for each mixing station via the data collection device. The material data collected specifically are the set weighing value, material density value and metering precision value of the material of the metering scale of each mixing station. The processor can firstly obtain the set weighing value of the material of the weighing scale of the mixing station and the actual weighing value of the weighing scale of the mixing station, and acquire the metering accuracy value of the mixing station through the collection of the set weighing value of the material of the weighing scale of the mixing station and the actual weighing value of the material of the weighing scale. The processor can calculate the difference between the actual material weighing of the weighing scale of the mixing plant and the material setting weighing value of the weighing scale of the mixing plant, and then determine the ratio of the difference to the material setting weighing value of the weighing scale of the mixing plant, wherein the ratio is the metering accuracy value of the weighing scale of the mixing plant.
And 102, uploading the material data to a storage device to obtain material data sets of all the mixing stations.
After the processor collects the material data of each mixing station through the data collection device, the collected material data are uploaded to the storage device through the data transmission device to obtain a material data set consisting of the material data of all the mixing stations.
And 103, classifying the material data set.
After the storage device obtains the material data uploaded by the processor through the data transmission device, the material data are summarized to obtain a corresponding material data set of the mixing plant. The processor may classify the material data set.
In one embodiment, classifying the material dataset includes: acquiring the area position of each mixing station; determining the mixing stations with the same area position as the mixing stations in the same area; classifying data of the mixing stations belonging to the regions in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to the mixing stations of each region.
Each set of material data in the material data set corresponds to a mixing station. The processor can obtain the area position of the mixing station corresponding to each group of material data in the material data set. And determining the mixing stations with the same area position as the mixing stations in the same area. For example, the material data set includes a plurality of sets of material data, and it is assumed that the mixing station corresponding to the material data is located in the south china area and the mixing station corresponding to the material data is located in the center china area. The processor can classify the material data corresponding to the mixing stations in the south China area from the material data set to obtain the material data set in the south China area. And classifying the material data corresponding to the mixing stations in the Huazhong area from the material data set to obtain the material data set of the Huazhong area.
The processor can classify the material data corresponding to the mixing station in the material data set according to the region position of the mixing station, so as to obtain the material data set which is classified according to the region. After the material data sets are classified according to regions, the processor can analyze and process the material setting weighing value, the material density value and the metering precision value of the metering scale of the mixing plant included in each group of material data in the material data sets to obtain a first credible maximum value corresponding to the material setting weighing value, a second credible average value corresponding to the material density value and a third credible maximum value corresponding to the metering precision value.
In one embodiment, the method further comprises: obtaining the model of each mixing station; determining the mixing stations with the same model as the mixing stations with the same model; classifying the data of the mixing stations belonging to the same model in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value of the mixing stations of each model.
Each set of material data in the material data set corresponds to a mixing station. The processor may obtain a model number of the mixing station corresponding to each set of material data in the material data set. And determining the mixing stations with the same model as the mixing stations with the same model. The processor classifies the material data in the material data set according to the model of the mixing station, classifies the material data corresponding to the mixing stations with the same model from the material data set, and finally obtains the material data set corresponding to each mixing station with the same model. After the material data sets are classified according to the types of the mixing stations, the processor can analyze and process the material set weighing value, the material density value and the metering precision value of the metering scale of the mixing station, which are included in each group of material data in the material data sets, to obtain a first credible maximum value corresponding to the material set weighing value, a second credible average value corresponding to the material density value and a third credible maximum value corresponding to the metering precision value.
In one embodiment, classifying the material dataset further comprises: classifying data of mixing stations with the same type in the material data set, and determining the material type corresponding to the material contained in the weighing scale of each mixing station; and classifying data in the material data set according to the material types to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to each material type.
After the processor classifies the material data set according to the model of the mixing plant, the material data in the material data set can be classified according to the category of the material. For example, the types of the materials may include stones, sand, coal ash, cement, and the like, and the materials are classified according to different types, for example, the material data corresponding to the material, sand, in the material data set is classified from the material data set. In this way, the processor may obtain material data sets corresponding to various different classes of materials. After the material data sets are classified according to the types of the materials, the processor can analyze and process the material set weighing value, the material density value and the metering precision value of the metering scale of the mixing plant included in each group of material data in the material data sets to obtain a first credible maximum value corresponding to the material set weighing value, a second credible average value corresponding to the material density value and a third credible maximum value corresponding to the metering precision value.
And 104, analyzing and processing the classified material data set, and determining a first credible maximum value of the material set weighing value, a second credible average value of the material density value and a third credible maximum value of the metering precision value.
The processor obtains material data from the mixing station to obtain a material data set, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station. After the processor classifies the material data in the material data set, the processor may analyze and process the material setting weighing value, the material density value, and the metering accuracy value included in each group of material data to determine a first reliable maximum value of the material setting weighing value, a second reliable average value of the material density value, and a third reliable maximum value of the metering accuracy value.
In one embodiment, determining the first trusted maximum of the material set weighing values, the second trusted average of the material density values, and the third trusted maximum of the metrology accuracy values based on an analysis of the sorted material data sets comprises: determining the material type corresponding to the material data; determining a standard difference value and an average value of material set weighing values corresponding to each material type, an average density value of material density values and a standard difference value and an average value of metering precision values corresponding to each material type aiming at each material type; processing the standard difference value and the average value of the material set weighing values corresponding to each material type by utilizing a normal distribution principle to obtain a first credible maximum value corresponding to each material type; determining the second credible average value according to the average density value of the material density values; and processing the standard difference value and the average value of the metering precision value corresponding to each material type by utilizing a normal distribution principle to obtain a third credible maximum value corresponding to each material type.
After the collected material data sets are classified, the processor may determine the material type corresponding to the material data in the material data sets. And determining material data corresponding to each material according to the category of each material, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of the metering scale. The processor may determine a standard value for the material set point for each materialAnd the average value, the average density value of the material density values of each material and the standard deviation value and the average value of the metering precision values of each material. The processor may be represented by a formula
To calculate the standard deviation of the data, x when calculating the standard value of the material set weighing value i A weighing value is set for each material,
and setting an average value of the weighing values for the material, and obtaining a standard value sigma of the weighing values for the material according to a formula. In calculating the standard value of the measurement accuracy value, x i For each of the values of the measurement accuracy,
and obtaining a standard value sigma of the metering precision value according to a formula as an average value of the metering precision values.
After the processor obtains the standard deviation and the average value of the material set weighing values corresponding to each material type, the processor can process the standard deviation and the average value of the material set weighing values by using a normal distribution principle to obtain a first credible maximum value corresponding to each material type. The processor can select the average value plus 3 times of standard deviation to determine the credible maximum value of the material set weighing value according to the 3 sigma principle of normal distribution. For example, after the processor determines the standard deviation of the material set weighing value, the standard deviation is used to determine the probability interval, wherein the values are distributed in
The probability of the interval is 0.9973, the values of the group of data are almost all concentrated in the interval, and the probability of exceeding the interval is only less than 0.3%, so that the error exceeding the interval is not random error but coarse error, the reliability of the data containing the error is not high, and the data should be rejected. The upper limit of this interval is the confidence maximum. Whereby the processor can determine the analyzed material settingsThe constant weighing value is the first maximum value.
Similarly, the processor may be represented by a formula
The standard deviation of the metrology accuracy value is calculated. And the normal distribution principle can be utilized to select the average value and the standard deviation of 3 times to process the standard deviation and the average value of the metering precision value according to the 3 sigma principle of the normal distribution so as to obtain a third credible maximum value corresponding to each material category. And determining the average density value of the material density values as a second credible average value.
And 105, inputting the first credible maximum value, the second credible average value and the third credible maximum value into a scale volume design reference model to obtain a target volume value of the weighing scale volume.
After the processor obtains the first credible maximum value, the second credible average value and the third credible maximum value of the material data through analysis and processing, the data can be input into the scale volume design reference model, and the target volume value of the metering scale volume is obtained through processing of the scale volume design reference model on the data.
In one embodiment, the scale volume design reference model determines the target volume value by equation (1):
wherein V is a target volume value; m is a first credible maximum value;
is a second confidence average; k is the third confidence maximum.
The processor analyzes and processes the material data to obtain a first credible maximum value, a second credible average value and a third credible maximum value, and then inputs the obtained data into the scale volume design reference model
And outputting a target volume value for measuring the scale volume by using the scale volume design reference model. The processor may classify the collected material data set to obtain a classified material data set, and process and analyze the classified material data set. And inputting the processed material data into a scale volume design reference model, so that target volume values of the scales corresponding to each material in different regions and different models of mixing stations can be obtained.
And the processor inputs the processed and analyzed material data into the scale volume design reference model so as to output a target volume value of the material for measuring the scale volume. The model predicts the volume of the weighing scale and provides a reference value, so that the matching degree of the volume of the weighing scale and a design value is improved, and the redundancy and waste of materials are reduced.
In one embodiment, a processor configured to perform the method for determining a volume of a weigh scale of a mixing station of any of the above embodiments is provided.
The processor may acquire material data for each mixing station listed as requiring acquisition via the data acquisition device. The material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing plant. After the processor obtains the material data corresponding to each mixing station, the collected material data can be uploaded to the storage device through the data transmission device, so that a material data set corresponding to the mixing station can be obtained. After the storage device stores the received material data to obtain a material data set, the processor can classify the material data set.
When the processor classifies the material data set, the processor may first obtain each mixing station corresponding to each group of material data in the material data set, and then determine the area position of each mixing station. The processor may classify the determined mixing stations according to zone locations, and determine mixing stations with the same zone location as mixing stations of the same zone. Each mixing station corresponds to a group of material data, and the processor can classify the material data corresponding to the mixing stations in the same area according to the area positions of the mixing stations. At this time, the material data sets are classified by the processor according to the region positions of the corresponding mixing stations, and are divided into corresponding material data sets of which the mixing stations are in the same region. After the processor obtains the material data classified according to the same region position of the mixing plant. The processor may further categorize the material data set for each of the mixing stations at the same regional location.
The processor can acquire the model of each mixing station of the mixing stations with the same area position, and the processor can acquire the model of the mixing station corresponding to each group of material data in the material data sets corresponding to the mixing stations with the same area position. And determining the mixing stations with the same model as the mixing stations with the same model. The processor classifies the material data in the material data set according to the model of the mixing station, classifies the material data corresponding to the mixing stations of the same model from the material data set, and finally obtains the material data set corresponding to each mixing station of the same model.
After classifying the material data sets corresponding to the mixing stations in the same region according to the models of the mixing stations, the processor can determine the material types contained in the material data sets in the classified material data sets and determine the material data of each group corresponding to the material types. The processor can classify the material data set again according to the material category, and classify the material data of the same material category from the material data set to obtain the material data set of each material. At this time, the processor may obtain a material data set corresponding to the same material contained in the weighers in the mixing stations of the same type in the same area.
For example, the material data set includes a plurality of sets of material data, and it is assumed that the mixing station corresponding to the material data is located in the south china area and the mixing station corresponding to the material data is located in the center china area. The processor can then classify the material data corresponding to the mixing stations in the south china area from the material data set to obtain the material data set in the south china area. And classifying the material data corresponding to the mixing station in the Huazhong area from the material data set to obtain the material data set of the Huazhong area. Suppose that the processor continues to classify the material data set of the Huazhong region after obtaining the material data set corresponding to the stirrer of the Huazhong region. The processor may determine the model of the mixing station corresponding to each set of material data in the material data set corresponding to the Huazhong region. Assuming that the types of the mixing stations corresponding to the material data in the material data set in the huazhong area are 120 types, 180 types, and the like (the types of the mixing stations 180 are relatively representative), the processor may classify the material data corresponding to the mixing stations of the same type in the data set, for example, classify the material data corresponding to the mixing stations of the 120 types in the material data set from the material data set, so as to obtain the material data set corresponding to the mixing stations of the 120 types in the huazhong area. And classifying the material data corresponding to the stirring stations of 180 models from the material data set to obtain the material data set corresponding to the stirring stations of 180 models in the Huazhong region. The processor may continue to classify the classified material data set according to the class of the material. Assume that the processor obtains a material data set corresponding to a central region 180 model mixing station and the processor needs to continue to sort the data set. The processor may obtain a material category corresponding to each set of material data in the material data set. The material categories may include: stones, sand, coal ash, cement, and the like. The processor can classify the material data in the material data set according to the corresponding material categories, for example, the material data corresponding to the stone can be classified, and the material data corresponding to the sand can be classified. After classification is completed, the processor can obtain material data of stones in the weighing scale corresponding to the stirring stations of type 180 in the Huazhong area, material data of sands in the weighing scale corresponding to the stirring stations of type 180 in the Huazhong area, and the like.
After the processor finishes classifying the material data, the processor may determine a material data set corresponding to each material category for each material category, and perform analysis processing on the material data set. The processor may determine a standard deviation and a mean of the material set weighing values corresponding to each material category, an average density value of the material density values, and a standard deviation and a mean of the metrology precision values corresponding to each material category. The processor may be represented by a formula
To calculate the standard deviation of the data. For example, the processor analyzes and processes a material data set corresponding to the material, sand in the scale, corresponding to the stirring station of model 180 in the huazhong area. The material data set comprises a material set weighing value, a material density value and a metering precision value. The processor can obtain the standard deviation and the average value of the material set weighing value of the sand, and process the standard deviation and the average value of the material set weighing value by utilizing a normal distribution principle so as to obtain a first credible maximum value corresponding to the material, namely the sand in the weighing scale corresponding to the stirring station with the model of 180 in the Huazhong area. And determining a corresponding second credible average value by obtaining the average density value of the material density values, and determining a corresponding third credible maximum value by obtaining the standard deviation and the average value of the metering precision values and utilizing a normal distribution principle.
After determining a first credible maximum value, a second credible average value and a third credible maximum value corresponding to the same material of the metering scales of the mixing stations with the same type in the same region, the processor inputs the values into a scale volume design reference model
Wherein V is a target volume value; m is a first credible maximum value;
is a second confidence average; k is a third credible maximum value, and the target volume value of the weighing scale volume is output through the scale volume design reference model. For example, a corresponding first credible maximum value, a second credible average value and a third credible maximum value can be obtained by analyzing and processing a material data set corresponding to the material, namely the sand in the weighing scale in the stirring station of model 180 in huazhong area, and the target volume value of the sand in the weighing scale in the stirring station of model 180 in huazhong area is obtained by inputting a scale volume design reference model.
In one embodiment, as shown in fig. 2, a mixing station 200 is provided, the mixing station 200 comprising: the data acquisition device 201 is configured to acquire material data of the mixing station 200, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station 200; the data transmission device 202 is configured to upload the material data to the storage device 203 to obtain a material data set consisting of the material data of all the mixing stations 200 acquired by the data acquisition device 201; a storage device 203 configured to receive the material data uploaded by the data transmission device 202; and the processor 204 described above.
In one embodiment, as shown in FIG. 3, there is provided a production information data collection and storage system 300 comprising: the above-mentioned mixing stations 200, wherein there are a plurality of mixing stations 200; a storage device 301 for storing material data for each mixing station 200; the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing plant 200; the communication device 302 is used for uploading the storage data in the storage device 301 to the big data platform device; and the big data platform device 303 is used for receiving the storage data in the storage device 301.
In one embodiment, a machine-readable storage medium having instructions stored thereon for causing a machine to perform any one of the above-described methods for determining a scale volume of a mixing station is provided.
In one embodiment, there is provided an apparatus for determining a scale volume of a mixing station comprising a processor as described above.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The inner core can be provided with one or more than one, and the determination of the volume of the weighing scale of the mixing plant is realized by adjusting the parameters of the inner core.
The present embodiments provide a processor for executing a program, wherein the program when executed performs the above-described method for determining a scale volume of a mixing station.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor a01, a network interface a02, a memory (not shown), and a database (not shown) connected by a system bus. Wherein processor a01 of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises internal memory a03 and non-volatile storage medium a 04. The non-volatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a 04. The database of the computer equipment is used for storing the collected related material data of the engineering machinery. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02 is executed by the processor a01 to implement a method for determining a scale volume of a mixing station.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the application provides equipment, the equipment comprises a processor, a memory and a program which is stored on the memory and can run on the processor, and the following steps are realized when the processor executes the program: acquiring material data of each mixing station, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station; uploading the material data to a storage device to obtain material data sets of all mixing stations; classifying the material data set; analyzing and processing the classified material data set, and determining a first credible maximum value of the material set weighing value, a second credible average value of the material density value and a third credible maximum value of the metering precision value; and inputting the first credible maximum value, the second credible average value and the third credible maximum value into a scale volume design reference model to obtain a target volume value of the weighing scale volume.
In one embodiment, classifying the material dataset includes: acquiring the area position of each mixing station; determining the mixing stations with the same area position as the mixing stations in the same area; classifying data of the mixing stations belonging to the same region in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to the mixing stations in each region.
In one embodiment, the method further comprises: obtaining the model of each mixing station; determining the mixing stations with the same model as the mixing stations with the same model; and classifying the data of the mixing stations belonging to the same model in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value of the mixing stations of each model.
In one embodiment, classifying the material dataset further comprises: classifying data of mixing stations with the same type in the material data set, and determining the material type corresponding to the material contained in the weighing scale of each mixing station; and classifying the data in the material data set according to the material classes to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to each material class.
In one embodiment, determining the first trusted maximum of the material set weighing values, the second trusted average of the material density values, and the third trusted maximum of the metrology accuracy values based on an analysis of the sorted material data sets comprises: determining material types corresponding to the material data; aiming at each material type, determining a standard difference value and an average value of material set weighing values corresponding to each material type, an average density value of material density values and a standard difference value and an average value of metering precision values corresponding to each material type; processing the standard difference value and the average value of the material set weighing values corresponding to each material type by utilizing a normal distribution principle to obtain a first credible maximum value corresponding to each material type; determining a second credible average value according to the average density value of the material density values; and processing the standard difference value and the average value of the metering precision value corresponding to each material type by utilizing a normal distribution principle to obtain a third credible maximum value corresponding to each material type.
In one embodiment, the scale volume design reference model determines the target volume value by equation (1):
wherein V is a target volume value; m is a first credible maximum value;
is a second confidence average; k is the third confidence maximum.
The preferred embodiments of the present application have been described in detail with reference to the accompanying drawings, however, the present application is not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the present application within the technical idea of the present application, and these simple modifications are all within the protection scope of the present application.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described in the present application.
In addition, any combination of the various embodiments of the present application is also possible, and the same should be considered as disclosed in the present application as long as it does not depart from the idea of the present application.
Claims (9)
1. A method for determining a scale volume of a mixing station, comprising:
acquiring material data of each mixing station, wherein the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station;
uploading the material data to a storage device to obtain material data sets of all the mixing stations;
classifying the material data set;
according to the analysis processing of the classified material data set, determining a first credible maximum value of the material set weighing value, a second credible average value of the material density value and a third credible maximum value of the metering precision value;
inputting the first credible maximum value, the second credible average value and the third credible maximum value into a scale volume design reference model to obtain a target volume value of the weighing scale volume;
wherein classifying the material data set comprises:
acquiring the area position of each mixing station;
determining the mixing stations with the same area position as the mixing stations in the same area;
classifying the data of the mixing stations belonging to the same region in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to the mixing stations in each region.
2. The method of claim 1, further comprising:
obtaining the model of each mixing station;
determining the mixing stations with the same model as the mixing stations with the same model;
and classifying the data of the mixing stations belonging to the same model in the material data set to determine a first credible maximum value, a second credible average value and a third credible maximum value of the mixing stations of each model.
3. The method of claim 2, wherein the classifying the material dataset further comprises:
classifying the data of the mixing stations with the same type in the material data set, and determining the material category corresponding to the material contained in the weighing scale of each mixing station;
and classifying the data in the material data set according to the material classes to determine a first credible maximum value, a second credible average value and a third credible maximum value corresponding to each material class.
4. The method of claim 1, wherein determining the first trusted maximum value of the material set weighing value, the second trusted average value of the material density value, and the third trusted maximum value of the metrology precision value based on an analysis of the sorted material data set comprises:
determining a material type corresponding to the material data;
determining a standard difference value and an average value of material set weighing values corresponding to each material type, an average density value of material density values and a standard difference value and an average value of metering precision values corresponding to each material type aiming at each material type;
processing the standard difference value and the average value of the material set weighing values corresponding to each material type by utilizing a normal distribution principle to obtain a first credible maximum value corresponding to each material type;
determining the second trusted average value according to the average density value of the material density values;
and processing the standard difference value and the average value of the metering precision value corresponding to each material type by utilizing a normal distribution principle to obtain a third credible maximum value corresponding to each material type.
5. The method of claim 1, wherein the scale volume design reference model determines the target volume value by equation (1):
wherein V is the target volume value; m is the first confidence maximum;
is the second confidence average; k is said thirdA trusted maximum.
6. A processor characterized by being configured to perform the method for determining a scale volume of a mixing station according to any of claims 1 to 5.
7. A mixing station, characterized in that it comprises:
the system comprises a data acquisition device, a data processing device and a data processing device, wherein the data acquisition device is configured to acquire material data of a mixing station, and the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing station;
the storage device is configured to receive the material data uploaded by the data transmission device;
the data transmission device is configured to upload the material data to the storage device so as to obtain material data sets of all the mixing stations;
and
the processor of claim 6.
8. A production information data acquisition and storage system is characterized by comprising:
the mixing station of claim 7, wherein the number of mixing stations is plural;
the storage equipment is used for storing the material data of each mixing station; the material data comprises a material set weighing value, a material density value and a metering precision value of a metering scale of the mixing plant;
the big data platform equipment is used for receiving the storage data in the storage equipment;
and the communication equipment is used for uploading the storage data in the storage equipment to the big data platform equipment.
9. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the method for determining scale volume of a mixing station of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111081956.5A CN113932875B (en) | 2021-09-15 | 2021-09-15 | Method for determining the scale volume of a mixing station, processor and mixing station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111081956.5A CN113932875B (en) | 2021-09-15 | 2021-09-15 | Method for determining the scale volume of a mixing station, processor and mixing station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113932875A CN113932875A (en) | 2022-01-14 |
| CN113932875B true CN113932875B (en) | 2022-09-23 |
Family
ID=79275917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111081956.5A Active CN113932875B (en) | 2021-09-15 | 2021-09-15 | Method for determining the scale volume of a mixing station, processor and mixing station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113932875B (en) |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2753588C (en) * | 2011-09-27 | 2016-01-26 | Westport Power Inc. | Apparatus and method for volume and mass estimation of a multiphase fluid stored at cryogenic temperatures |
| CN206671117U (en) * | 2017-04-12 | 2017-11-24 | 山东钢铁股份有限公司 | Material kind device for identifying |
| CN109959436B (en) * | 2017-12-14 | 2021-12-24 | 湖南中联重科混凝土机械站类设备有限公司 | Material weighing control method and device and material weighing system |
| JP2019120637A (en) * | 2018-01-10 | 2019-07-22 | 光洋機械産業株式会社 | Ready-mixed concrete amount acquisition device and ready-mixed concrete manufacturing equipment with the same |
| CN110608698A (en) * | 2018-06-15 | 2019-12-24 | 西部超导材料科技股份有限公司 | Method for measuring length of superconducting braided flat wire by accumulated turns |
| CN110899089A (en) * | 2019-11-02 | 2020-03-24 | 江苏德丰新建材科技有限公司 | Novel jade metering device of polymer |
-
2021
- 2021-09-15 CN CN202111081956.5A patent/CN113932875B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113932875A (en) | 2022-01-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2006263327B2 (en) | Apparatus and method for evaluating data points against cadastral regulations | |
| Timm et al. | Axle load spectra characterization by mixed distribution modeling | |
| CN111080477A (en) | Household power load prediction method and system | |
| CN104952247A (en) | Congestion level analysis platform based on double communication data | |
| CN115238568A (en) | Digital twin model construction method and device and terminal equipment | |
| CN113932875B (en) | Method for determining the scale volume of a mixing station, processor and mixing station | |
| CN113850244B (en) | Coal conveying quantity monitoring method, device and equipment based on image recognition and storage medium | |
| CN110557829A (en) | Positioning method and positioning device for fusing fingerprint database | |
| CN116303480B (en) | Electric energy meter error checking method based on cloud computing | |
| CN105844169A (en) | Method and device for information safety metrics | |
| CN116719714A (en) | Training method and corresponding device for screening model of test case | |
| CN113239815B (en) | Remote sensing image classification method, device and equipment based on real semantic full-network learning | |
| CN112182759A (en) | Method for testing wave numerical simulation result based on satellite altimeter data | |
| EP3518153A1 (en) | Information processing method and information processing system | |
| CN116176579B (en) | Automatic driving following distance measuring and calculating device and method | |
| CN114485889A (en) | Method and device for determining calibration scheme of weighing equipment | |
| CN114792232B (en) | Engineering quantity processing method, system, equipment and readable storage medium | |
| CN116108788B (en) | Method and device for automatically customizing eFPGA (electronic component design and packaging architecture) device | |
| CN117454122B (en) | Mountain torrent disaster rainfall early warning analysis method and device based on fixed-point fixed-surface relation | |
| CN116072282B (en) | Remote intelligent detection and analysis method and system for CT equipment | |
| CN109284320A (en) | Automatic returning diagnostic method in big data platform | |
| CN117436795B (en) | Warehouse material monitoring method and system for hierarchical management | |
| CN113742849B (en) | Variable sensitivity analysis method and device for solid-liquid-like aircraft overall design | |
| CN115860846B (en) | Construction cost calculation method, system, equipment and readable storage medium | |
| CN117710833B (en) | Mapping geographic information data acquisition method and related device based on cloud computing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |