Quantity detection method, electronic equipment, mixer truck and computer readable storage medium

Abstract

The invention discloses a volume detection method, electronic equipment, a mixer truck and a computer readable storage medium, and the method comprises the steps: transmitting a data detection request to an Internet of Things platform, the data detection request comprising an identity label of the mixer truck; receiving detection data returned by the Internet of Things platform based on the data detection request, wherein the detection data comprises a volume detection model used for describing an association relationship between the torque of a mixing drum of the mixer truck and the volume of loaded concrete; and according to the volume detection model and the current torque of the mixing drum, obtaining and outputting the current predicted volume of the concrete in the mixing drum. Therefore, the volume of the concrete in the mixing drum of the mixer truck can be conveniently and intelligently detected in real time, the detection accuracy is high, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering machinery, and in particular to a volume detection method, electronic equipment, a mixer truck and a computer-readable storage medium. Background Art

[0002] With the continuous development of society, more and more infrastructure such as roads and bridges are being built. In the construction of infrastructure, mixer trucks are an indispensable engineering machinery and equipment. Among them, it is of great significance to detect the volume of concrete in the mixing drum of mixer trucks.

[0003] In the existing related technologies, the volume of concrete in the mixing drum is usually detected by means of sensors, etc., but the above method is easily affected by the errors of the sensors themselves, which may lead to low detection accuracy. Summary of the invention

[0004] The purpose of this application is to provide a volume detection method, an electronic device, a mixer truck and a computer-readable storage medium, which can conveniently and intelligently detect the volume of concrete in the mixing drum of the mixer truck in real time, with high detection accuracy and improved user experience.

[0005] To achieve the above objectives:

[0006] In a first aspect, an embodiment of the present application provides a volume detection method, which is applied to a mixer truck, comprising:

[0007] Sending a data detection request to the Internet of Things platform, wherein the data detection request includes the identity of the mixer truck;

[0008] Receiving detection data returned by the Internet of Things platform based on the data detection request, the detection data including a volume detection model for describing a correlation relationship between a torque of a mixing drum of the mixer truck and a volume of loaded concrete;

[0009] According to the volume detection model and the current torque of the mixing drum, the current predicted volume of concrete in the mixing drum is acquired and output.

[0010] Optionally, the detection data further includes the volume of concrete loaded by the mixing station on the mixer truck at different times; before acquiring and outputting the current predicted volume of concrete in the mixing drum according to the volume detection model and the current torque of the mixing drum, the method further includes:

[0011] During the process of the mixer truck being loaded with materials from the mixing station, obtaining the torques of the mixer drum corresponding to the different moments;

[0012] The volume detection model is corrected based on the torques corresponding to the mixing drum at the different moments and the volumes of concrete loaded by the mixing station on the mixer truck at different moments.

[0013] Optionally, the method further comprises:

[0014] The corrected volume detection model is uploaded to the Internet of Things platform.

[0015] Optionally, the method further comprises:

[0016] The current predicted volume and the location information of the mixer truck are sent to the Internet of Things platform.

[0017] In a second aspect, an embodiment of the present application provides a volume detection method, which is applied to an Internet of Things platform, including:

[0018] Acquire the working condition data uploaded by the mixing station, wherein the working condition data includes the identity of the mixer truck loading concrete from the mixing station and the corresponding concrete model;

[0019] Receiving a data detection request including an identity identifier of the mixer truck sent by the mixer truck;

[0020] Querying the working condition data according to the identity of the mixer truck to determine a volume detection model corresponding to the concrete model loaded by the mixer truck, wherein the volume detection model is used to describe the correlation between the torque of the mixer drum of the mixer truck and the volume of the loaded concrete;

[0021] Sending detection data to the mixer truck, wherein the detection data includes a volume detection model corresponding to the concrete model.

[0022] Optionally, the method further comprises:

[0023] The corrected volume detection model sent by the mixer truck is received, and the volume detection model corresponding to the concrete model is updated according to the corrected volume detection model.

[0024] Optionally, the method further comprises:

[0025] Receive the current predicted volume and the location information of the mixer truck sent by the mixer truck, and send the current predicted volume and the location information of the mixer truck to a preset terminal.

[0026] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing a computer program, and when the processor runs the computer program, the above-mentioned volume detection method is implemented.

[0027] In a fourth aspect, an embodiment of the present application provides a mixer truck, comprising the electronic device as described in the third aspect.

[0028] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned volume detection method is implemented.

[0029] The volume detection method, electronic device, mixer truck and computer-readable storage medium provided by the embodiment of the present application include: sending a data detection request to an Internet of Things platform, the data detection request includes the identity of the mixer truck; receiving detection data returned by the Internet of Things platform based on the data detection request, the detection data includes a volume detection model for describing the correlation between the torque of the mixer drum of the mixer truck and the volume of the loaded concrete; according to the volume detection model and the current torque of the mixer drum, obtaining and outputting the current predicted volume of the concrete in the mixer drum. In this way, the mixer truck obtains a volume detection model matching the loaded concrete from the Internet of Things platform, and predicts the volume of the concrete in the mixer drum based on the volume detection model and the torque of the mixer drum, so that the volume of the concrete in the mixer drum can be obtained in real time without adding sensors to the mixer truck, and the influence of the errors of different sensors on the volume detection results can be avoided, so that the volume of the concrete in the mixer drum of the mixer truck can be detected in real time conveniently and intelligently, with high detection accuracy, which improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the process of the volume detection method provided by the embodiment of the present invention Figure 1 ;

[0031] Figure 2 Schematic diagram of the process of the volume detection method provided by the embodiment of the present invention Figure 2 ;

[0032] Figure 3 A schematic diagram of data transmission in a volume detection system provided by an embodiment of the present invention;

[0033] Figure 4 Schematic diagram of the internal operation process of a mixer truck in an embodiment of the present invention;

[0034] Figure 5 A schematic diagram of the process of self-correction function assistance in an embodiment of the present invention;

[0035] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0038] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of..." or "when..." or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0039] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0040] It should be noted that, in this article, step codes such as S101, S102, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the scope of protection of this application.

[0041] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0043] See also Figure 1 , is a volume detection method provided in an embodiment of the present application. The volume detection method can be executed by a volume detection device provided in an embodiment of the present application. The volume detection device can be implemented in software and / or hardware, such as an electronic device such as a controller or a processor. In this embodiment, the execution subject of the volume detection method is a mixer truck as an example. The volume detection method provided in this embodiment includes:

[0044] Step S101: Send a data detection request to the Internet of Things platform, where the data detection request includes the identity of the mixer truck.

[0045] The data detection request is used to request the acquisition of detection data, which may include a volume detection model, etc. The user can register the mixer truck on the IoT platform, for example, register the mixer truck's license plate number, frame number, or assigned vehicle code as the mixer truck's identity on the IoT platform, so that the mixer truck can transmit data with the IoT platform. Optionally, the mixer truck can send a data detection request to the IoT platform after entering the mixing station and detecting that the torque of the mixing drum is gradually increasing.

[0046] Step S102: receiving detection data returned by the IoT platform based on a data detection request, wherein the detection data includes a volume detection model for describing a correlation between the torque of the mixing drum of the mixer truck and the volume of loaded concrete.

[0047] The volume detection model is matched with the concrete model loaded on the mixer truck. Different concrete models correspond to different volume detection models. At the same time, the input of the volume detection model is the torque of the mixer drum, and the output is the volume of concrete. It should be noted that the volume detection model can be established based on an existing curve model fitting plug-in.

[0048] Step S103: acquiring and outputting the current predicted volume of concrete in the mixing drum according to the volume detection model and the current torque of the mixing drum.

[0049] Specifically, after obtaining the current torque of the mixing drum, the mixer truck uses the current torque of the mixing drum as the input of the volume detection model, obtains the current predicted volume of concrete in the mixing drum output by the volume detection model, and outputs the current predicted volume of concrete in the mixing drum.

[0050] Among them, the current torque of the mixing drum refers to the torque used to maintain the rotation of the mixing drum of the mixer truck. In the case where the detection data includes the concrete mix ratio, the weight of the concrete in the mixing drum can be predicted based on the current predicted volume of the concrete and the concrete mix ratio. Optionally, the current predicted volume of the concrete in the mixing drum can be output by displaying the current predicted volume on a preset display device on the mixer truck, or by outputting the current predicted volume on a preset voice output device on the mixer truck, or by sending the current predicted volume to a preset recipient. It should be noted that in order to reduce the occupation of the memory resources of the mixer truck, the acquired detection data and the torque of the mixing drum can be deleted after the mixer truck completes unloading each time. In addition, in the case where the detection data also includes the concrete mix ratio, the weight of the concrete in the mixing drum can be calculated based on the concrete mix ratio and the current predicted volume, and the weight of the concrete in the mixing drum can be output.

[0051] In one embodiment, the detection data also includes the volume of concrete loaded by the mixing station on the mixer truck at different times; before obtaining and outputting the current predicted volume of concrete in the mixing drum according to the volume detection model and the current torque of the mixing drum, it also includes:

[0052] When the mixer truck is loading materials from the mixing station, the torque of the mixer drum at different times is obtained;

[0053] Based on the torques of the mixing drum at different times and the volume of concrete loaded on the mixer truck by the mixing station at different times, the volume detection model is corrected.

[0054] In the process of loading the mixer truck from the mixing station, the torque data of the mixing drum will be obtained and recorded in real time. After determining the volume of concrete loaded by the mixing station to the mixer truck at different times based on the detection data, the torque data of the mixing drum recorded at different times can be extracted to obtain the torque corresponding to the mixing drum at different times. For example, when the different times include time A and time B, the torque of the mixing drum at time A and time B is extracted from the recorded torque data of the mixing drum. It should be noted that the volume of concrete loaded by the mixing station to the mixer truck at different times can also be sent directly to the mixer truck by the mixing station.

[0055] It can be understood that since the volume detection model obtained from the Internet of Things platform is determined based on the historical torque data of the mixing drum and the volume of concrete loaded on the mixer truck in history, in order to further improve the prediction accuracy of the volume detection model, the volume detection model can be corrected based on the torque corresponding to the mixing drum at different times and the volume of concrete loaded on the mixer truck by the mixing station at different times, so as to use the corrected volume detection model to predict the current volume of concrete in the mixing drum. Among them, based on the torque corresponding to the mixing drum at different times, the volume detection model can be used to predict the volume of concrete in the mixing drum, and then combined with the volume of concrete loaded on the mixer truck by the mixing station at different times, the prediction deviation of the volume detection model can be analyzed, so as to use the prediction deviation to correct the volume detection model. In this way, by correcting the volume detection model, the accuracy of volume prediction can be further improved.

[0056] In one embodiment, the method further includes: uploading the corrected volume detection model to an Internet of Things platform. In order to facilitate the subsequent direct retrieval of the required volume detection model from the Internet of Things platform for volume detection, the mixer truck can upload the corrected volume detection model to the Internet of Things platform for storage to improve ease of use.

[0057] In one embodiment, the method further comprises:

[0058] Send the current predicted volume and location information of the mixer truck to the IoT platform.

[0059] Among them, in order to enable users to timely know the volume of concrete in the mixing drum of the mixer truck at different positions, after obtaining the current predicted volume and the location information of the mixer truck, the mixer truck can send the current predicted volume and the location information of the mixer truck to the Internet of Things platform, so that the Internet of Things platform can inform the required users of the current predicted volume and the location information of the mixer truck.

[0060] In summary, in the volume detection method provided in the above embodiment, the mixer truck obtains a volume detection model that matches the loaded concrete from the Internet of Things platform, and predicts the volume of the concrete in the mixing drum based on the volume detection model and the torque of the mixing drum. This can achieve real-time acquisition of the volume of the concrete in the mixing drum without adding sensors to the mixer truck, and at the same time avoid the influence of different sensors' own errors on the volume detection results, thereby enabling convenient and intelligent real-time detection of the volume of concrete in the mixing drum of the mixer truck, with high detection accuracy, thereby improving user experience.

[0061] See also Figure 2 , is a volume detection method provided in an embodiment of the present application. The volume detection method can be executed by a volume detection device provided in an embodiment of the present application. The volume detection device can be implemented in software and / or hardware, such as a server such as an Internet of Things platform. In this embodiment, the execution subject of the volume detection method is an Internet of Things platform as an example. The volume detection method provided in this embodiment includes:

[0062] Step S201: Acquire the working condition data uploaded by the mixing station, where the working condition data includes the identity of the mixer truck loading concrete from the mixing station and the corresponding concrete model.

[0063] Among them, when the mixer truck is loading concrete from the mixing station, the mixing station can package the mixer truck's identity and the corresponding concrete model and other working condition data and upload them to the Internet of Things platform for storage by the Internet of Things platform. It should be noted that the Internet of Things platform can store the working condition data corresponding to different mixer trucks uploaded by the mixing station separately. In addition, the working condition data may also include data such as the volume of concrete loaded by the mixing station on the mixer truck at different times and the concrete ratio, for example, the volume of concrete loaded on the mixer truck at time A is 2 cubic meters, and the volume of concrete loaded on the mixer truck at time B is 3 cubic meters, etc.

[0064] Step S202: receiving a data detection request including an identity identifier of the mixer truck sent by the mixer truck.

[0065] The data detection request is used to request to obtain detection data including information such as volume detection model, etc. The identity of the mixer truck can be a license plate number, frame number or assigned vehicle code of the mixer truck.

[0066] Step S203: querying the working condition data according to the identity of the mixer truck, determining the volume detection model corresponding to the concrete model loaded by the mixer truck, the volume detection model is used to describe the correlation between the torque of the mixer drum of the mixer truck and the volume of loaded concrete.

[0067] Specifically, the working condition data is queried according to the identity identification of the mixer truck to determine the concrete model loaded on the mixer truck, and then the volume detection model corresponding to the concrete model loaded on the mixer truck is determined according to the correspondence between different concrete models and different volume detection models.

[0068] The input of the volume detection model is the torque of the mixing drum, and the output is the volume of concrete. The relationship between the torque of the mixing drum and the volume of concrete can be obtained by collecting data through experimental methods. It should be noted that the volume detection model can be established based on an existing curve model fitting plug-in.

[0069] Step S204: sending detection data to the mixer truck, where the detection data includes a volume detection model corresponding to the concrete model.

[0070] Specifically, the Internet of Things platform sends detection data including a volume detection model corresponding to the concrete model to the mixer truck, so that the mixer truck performs volume detection based on the volume detection model.

[0071] The detection data may also include data such as the volume of concrete and / or concrete mix ratio loaded by the mixing station on the mixer truck at different times.

[0072] In one embodiment, the method further comprises:

[0073] Receive the corrected volume detection model sent by the mixer truck, and update the volume detection model corresponding to the concrete model according to the corrected volume detection model.

[0074] Specifically, after receiving the corrected volume detection model sent by the mixer truck, the IoT platform uses the corrected volume detection model as the updated volume detection model corresponding to the concrete model loaded by the mixer truck, and stores the corrected volume detection model.

[0075] In this way, by updating the square meter detection model, the accuracy of subsequent square meter prediction using the square meter detection model can be improved.

[0076] In one embodiment, the method further comprises:

[0077] Receive the current predicted volume and location information of the mixer truck sent by the mixer truck, and send the current predicted volume and location information of the mixer truck to a preset terminal.

[0078] Specifically, after receiving the current predicted volume and location information of the mixer truck predicted by the volume detection model, the IoT platform sends the current predicted volume and location information of the mixer truck to the preset terminal to display the current predicted volume and location information of the mixer truck on the preset terminal.

[0079] Among them, in order to enable users to know the concrete volume in the mixing drum of the mixer truck at different positions in time, after obtaining the current predicted volume and the location information of the mixer truck, the IoT platform can send the current predicted volume and the location information of the mixer truck to a preset terminal such as a smart phone or a display, so as to output the current predicted volume and the location information of the mixer truck on the preset terminal. In this way, by sending the current predicted volume and the location information of the mixer truck to the preset terminal in time, it is convenient for users to know the relevant information in time, thereby improving the user experience.

[0080] In summary, in the volume detection method provided in the above embodiment, the Internet of Things platform sends the volume detection model corresponding to the concrete model of the concrete loaded in the mixer truck to the mixer truck based on the working condition data uploaded by the mixing station, so that the mixer truck can predict the volume of the concrete in the mixing drum based on the volume detection model and the torque of the mixing drum. It can be achieved without adding sensors to the mixer truck. The volume of the concrete in the mixing drum can be obtained in real time, and the influence of different sensors on the volume detection results due to their own errors can be avoided. Therefore, the volume of the concrete in the mixing drum of the mixer truck can be detected in real time conveniently and intelligently, with high detection accuracy, thereby improving user experience.

[0081] Based on the same inventive concept as the above-mentioned embodiment, the above-mentioned embodiment is described in detail below through a specific example. In this embodiment, the data detection request is a data retrieval request and the detection data is the retrieval data.

[0082] In this embodiment, a dynamic real-time volume detection system for a mixer truck is provided, which integrates multi-terminal interconnection of data transmission, internal intelligent computing, and self-learning correction function, so as to detect the volume of the mixer truck. The main functions or operations of the volume detection system provided in this embodiment are described below:

[0083] 1. Data transmission

[0084] Data transmission and multi-terminal interconnection start from the moment the concrete mixer truck enters the station to load materials and ends when the concrete mixer truck leaves the station. Figure 3 , which is a schematic diagram of the data transmission flow. Among them, the mixing station uploads the working condition data including the identity of the mixer truck, concrete model, concrete ratio and the volume of loaded concrete to the IoT platform, and the IoT platform stores the working condition data in the mixer truck IoT database. After receiving the data retrieval request sent by the mixer truck, the IoT platform extracts the corresponding retrieval data from the mixer truck IoT database, and sends the retrieval data to the mixer truck for analysis, modeling and archiving by the mixer truck. At the same time, the IoT platform can also reconstruct the model regularly. It should be noted that when the mixer truck performs volume detection for the first time, it is necessary to first establish a torque-volume model based on the working condition data and torque data, and in subsequent use, the torque-volume model can be directly called from the IoT platform.

[0085] 2. Internal intelligent computing

[0086] Internal calculation is to sort the relevant working condition data sent by the IoT platform, incorporate the corresponding data into the data information matrix, and fit the real-time torque-volume model under a special working condition of the current transport material, so as to realize the real-time display of the concrete volume in the mixing drum under different torques. The following takes the model establishment of a mixer truck as an example to explain the internal calculation process of the mixer truck. Figure 4 As shown, the following steps are included:

[0087] Step S301: The mixer truck determines whether the preset information meets the detection requirements.

[0088] Among them, the preset information that meets the detection requirements includes that the mixer truck is in the loading or transporting state, the mixer truck is located at the mixing station or on the road, and the torque of the mixer drum is in a gradually increasing state.

[0089] Step S302: The mixer truck sends a data retrieval request to the Internet of Things platform.

[0090] Step S303: The mixer truck parses and sorts the data packets sent by the Internet of Things platform and incorporates them into the data information matrix.

[0091] The data packet may include data such as the volume of concrete loaded on the mixer truck by the mixing station at different times and the concrete mix ratio.

[0092] Step S304: The mixer truck reads the data information matrix through the curve fitting plug-in to establish a model.

[0093] Step S305: The mixer truck predicts the volume of concrete in the mixing drum according to the torque of the mixing drum through the model, and displays the volume in real time on the on-board display screen.

[0094] Step S306: The mixer truck transmits the model back to the Internet of Things platform so that the Internet of Things platform can store and accumulate it according to the concrete model.

[0095] 3. Self-correction function assistance

[0096] The process of the self-correction function assistance is as follows Figure 5 As shown, after the mixer truck successfully obtains the license plate number and related data from the IoT platform, the model transmitted after the IoT platform retrieves the data model library based on the license plate number is corrected, and after completing the model correction, the model is transmitted back to the IoT platform so that the IoT platform can complete the IoT model update.

[0097] Here, through the continuous use of mixer trucks, the amount of data continues to accumulate, and the model prediction error is continuously reduced through big data methods, thereby achieving correction of the model.

[0098] 4. Customer terminal display

[0099] The final calculated data is not only displayed on the on-board display screen of the mixer truck, but also transmitted to the customer terminal through the Internet of Things platform to display the transportation status of the corresponding mixer truck (including GPS positioning and other information) and the corresponding material transportation data displayed by time or document number on the customer terminal.

[0100] In summary, the volume detection system provided by this embodiment has the following technical effects: 1) It is more intelligent. By making full use of the mediating role of the Internet of Things platform, the information barriers between different devices are broken. It is no longer a single device for production and transportation. Multiple devices are integrated into a whole to provide an overall solution. 2) It is more advanced. The mixing station actively uploads the required time stack, the mixer truck self-checks and identifies the status, actively applies to retrieve the data of the corresponding time stack uploaded in the Internet of Things and sends it down for analysis, and obtains the data model for this time, thereby realizing real-time material volume detection. This method avoids the method of relying on other hardware equipment for detection, overcomes the influence of the errors of different sensors themselves, and makes full use of digital and intelligent technologies. 3) It is more developmental. Through the self-correction method, data models are continuously accumulated for auxiliary analysis and verification, which can make the calculation results more and more accurate, and complete the automatic iteration of system upgrades. 4) It is more humanized. The Internet of Things platform synchronizes data to the client App, helps customers manage mixer trucks online in real time, and can remind customers in time to a certain extent for various problems and risks that arise during transportation, thereby increasing customer adhesion. 5) By adopting the data collection and result calculation mode of vehicle-network collaboration, not only can the accumulated transportation data be continuously recorded, but also the transportation data can be fed back in real time, so as to apply intelligence and digitization in practice. 6) By adopting real-time online detection, analysis and calculation, the volume value of each transportation is reflected on the client's display screen, making the data more three-dimensional and more accurate, thus achieving a more humane and precise network application. 7) By adopting the self-correction auxiliary correction function, a data model library is formed according to the mathematical model and previous data, and the calculated data is corrected and then uploaded and archived, which will make the obtained results closer to the actual value, thereby achieving a more accurate effect.

[0101] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides an electronic device, such as Figure 6 As shown, the electronic device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 6 The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Figure 6The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the above-mentioned volume detection method is implemented.

[0102] The electronic device may also include: at least one network interface 312. The various components in the electronic device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 313 is not used in the following examples. Figure 6 Various buses are labeled as bus system 313.

[0103] The memory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0104] The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the electronic device. Examples of these data include: any computer program used to operate on the electronic device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0105] Based on the same inventive concept as the aforementioned embodiment, this embodiment further provides a mixer truck, comprising the electronic device as described above.

[0106] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer storage medium, in which a computer program is stored. The computer storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM) or the like; it may also be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer storage medium is executed by the processor, the above-mentioned volume detection method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 or Figure 2 The description of the illustrated embodiment will not be repeated here.

[0107] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0109] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A volume detection method, applied to a mixer truck, characterized in that: The method comprises: Sending a data detection request to the Internet of Things platform, wherein the data detection request includes the identity of the mixer truck; Receiving detection data returned by the Internet of Things platform based on the data detection request, the detection data including a volume detection model for describing a correlation relationship between a torque of a mixing drum of the mixer truck and a volume of loaded concrete; According to the volume detection model and the current torque of the mixing drum, the current predicted volume of concrete in the mixing drum is acquired and output.

2. The method according to claim 1, characterized in that: The detection data also includes the volume of concrete loaded by the mixing station on the mixer truck at different times; before obtaining and outputting the current predicted volume of concrete in the mixing drum according to the volume detection model and the current torque of the mixing drum, it also includes: During the process of the mixer truck being loaded with materials from the mixing station, obtaining the torques of the mixer drum corresponding to the different moments; The volume detection model is corrected based on the torques corresponding to the mixing drum at the different moments and the volumes of concrete loaded by the mixing station on the mixer truck at different moments.

3. The method according to claim 2, characterized in that The method further comprises: The corrected volume detection model is uploaded to the Internet of Things platform.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The current predicted volume and the location information of the mixer truck are sent to the Internet of Things platform.

5. A volume detection method, applied to the Internet of Things platform, characterized in that: The method comprises: Acquire the working condition data uploaded by the mixing station, wherein the working condition data includes the identity of the mixer truck loading concrete from the mixing station and the corresponding concrete model; Receiving a data detection request including an identity identifier of the mixer truck sent by the mixer truck; Querying the working condition data according to the identity of the mixer truck to determine a volume detection model corresponding to the concrete model loaded by the mixer truck, wherein the volume detection model is used to describe the correlation between the torque of the mixer drum of the mixer truck and the volume of the loaded concrete; Sending detection data to the mixer truck, wherein the detection data includes a volume detection model corresponding to the concrete model.

6. The method according to claim 5, characterized in that The method further comprises: The corrected volume detection model sent by the mixer truck is received, and the volume detection model corresponding to the concrete model is updated according to the corrected volume detection model.

7. The method according to claim 5, characterized in that The method further comprises: Receive the current predicted volume and the location information of the mixer truck sent by the mixer truck, and send the current predicted volume and the location information of the mixer truck to a preset terminal.

8. An electronic device, characterized in that: include: A processor and a memory storing a computer program, wherein when the processor runs the computer program, the volume detection method according to any one of claims 1 to 4 is implemented.

9. A mixer truck, characterized in that: Comprising an electronic device according to claim 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the volume detection method described in any one of claims 1 to 4 or claims 5 to 7 is implemented.

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