Concrete slump loss determination method based on blockchain system

Abstract

The embodiments of the present application provide a method, device, equipment and computer-readable storage medium for determining the slump loss of concrete based on a blockchain system. The method includes obtaining a license plate image of a vehicle transporting concrete; determining an image feature vector of the license plate image to obtain a string recognition result of the license plate image; determining the concrete information transported by the transport vehicle based on the string recognition result; obtaining the concrete slump value at the construction site, and if the value is less than a threshold, obtaining the ambient temperature; based on the concrete information and the ambient temperature, calculating the slump of the concrete through a preset algorithm, determining the factors causing the slump loss of the concrete, and uploading them to the blockchain system. In this way, the factors affecting the loss of concrete can be accurately identified, providing inspiration for subsequent management personnel to manage construction site information and adjust concrete distribution processing methods.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and in particular to a method, device, equipment and computer-readable storage medium for determining concrete slump loss based on a blockchain system. Background Art

[0002] Concrete is a general term for engineering composite materials that are made of aggregates bonded together by cementitious materials. It is widely used in civil engineering. An important parameter for judging whether concrete meets the needs of the construction site is the slump of concrete. Slump mainly refers to the plasticization and pumpability of concrete. It is a quantitative indicator to measure its degree (plasticization and pumpability) and is used to judge whether the construction can proceed normally.

[0003] There are many factors that affect slump, such as traffic jams causing the initial setting time of concrete to be exceeded, incorrect mix ratio causing the initial setting time of concrete to be advanced, and insufficient mixing time resulting in poor workability of concrete.

[0004] During the mixing and transportation of concrete, its slump is constantly changing, and the slump of concrete directly affects the performance of concrete. In the traditional slump value test, the reasons for excessive slump loss of concrete, as well as the intrinsic relationship between the influence of ambient temperature and transportation time on slump are not taken into account. Therefore, it is not easy for managers to identify the reasons for excessive slump loss and there is a lack of targeted improvement methods. Summary of the invention

[0005] According to an embodiment of the present application, a concrete slump loss determination solution based on a blockchain system is provided.

[0006] In the first aspect of the present application, a method for determining concrete slump loss based on a blockchain system is provided. The method comprises:

[0007] Obtain the license plate image of the concrete transport vehicle;

[0008] Input the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; input the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image;

[0009] Based on the character string recognition result, determining the concrete information transported by the transport vehicle;

[0010] The concrete slump value at the construction site is obtained. If the value is less than a threshold, the ambient temperature is obtained. Based on the concrete information and the ambient temperature, the slump of the concrete is calculated through a preset algorithm, the factors causing the slump loss of the concrete are determined, and the results are uploaded to the blockchain system.

[0011] Further,

[0012] The image feature extraction model is a convolutional neural network, including a convolution layer, a pooling layer and a batch normalization layer.

[0013] Furthermore, the image feature extraction model and the character recognition model are trained through the following steps:

[0014] Generate a training sample set, wherein the training sample includes a license plate image and a corresponding license plate number;

[0015] The set of license plate images in the training sample set is taken as input, and the license plate number corresponding to the license plate image is taken as the expected output, so as to train the image feature extraction model and the character recognition model.

[0016] Furthermore, the concrete information includes cement type, water content, amount of admixture and / or slump information of concrete out of the tank.

[0017] Furthermore, the calculating the slump of the concrete based on the concrete information and the ambient temperature acquired in real time by a preset algorithm includes:

[0018] If the ambient temperature is in the first temperature range, the concrete slump is calculated by the following formula:

[0019] T g =T c X -2 (1+K)

[0020] Among them, the T c , is the slump of concrete when it leaves the tank;

[0021] The C is the time when the concrete is discharged from the tank;

[0022] X=C+1;

[0023] The K is the cement variety difference coefficient.

[0024] Furthermore, the calculating the slump of the concrete based on the concrete information and the ambient temperature acquired in real time by a preset algorithm also includes:

[0025] If the ambient temperature is in the second temperature range, the concrete slump is calculated by the following formula:

[0026]

[0027] Wherein, K is the coefficient of difference among cement varieties.

[0028] Furthermore, the obtaining of the license plate image of the concrete transport vehicle includes:

[0029] Through the IOT device, the license plate image of the concrete transport vehicle is obtained.

[0030] In a second aspect of the present application, a concrete slump loss determination device based on a blockchain system is provided. The device comprises:

[0031] An acquisition module is used to acquire the license plate image of the concrete transport vehicle;

[0032] A recognition module, used to input the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; input the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image;

[0033] A determination module, used for determining the concrete information transported by the transport vehicle based on the character string recognition result;

[0034] The discrimination module is used to obtain the slump value of concrete at the construction site. If the value is less than a threshold, the ambient temperature is obtained; based on the concrete information and the ambient temperature, the slump of the concrete is calculated through a preset algorithm, the factors causing the slump loss of the concrete are determined, and the results are uploaded to the blockchain system.

[0035] In a third aspect of the present application, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0036] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present application is implemented.

[0037] The method for determining the slump loss of concrete based on a blockchain system provided in an embodiment of the present application comprises the following steps: obtaining a license plate image of a vehicle transporting concrete; inputting the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; inputting the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image; determining the concrete information transported by the transport vehicle based on the character string recognition result; obtaining the concrete slump value of the construction site, and obtaining the ambient temperature if the value is less than a threshold value; calculating the slump of the concrete based on the concrete information and the ambient temperature through a preset algorithm, determining the factors causing the slump loss of the concrete, and uploading the calculations to the blockchain system, thereby accurately determining the factors affecting the concrete loss, and providing inspiration for subsequent management personnel to manage construction site information and adjust concrete distribution processing methods.

[0038] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0040] Figure 1 A flowchart of a method for determining concrete slump loss based on a blockchain system according to an embodiment of the present application is shown;

[0041] Figure 2 A block diagram of a concrete slump loss determination device based on a blockchain system according to an embodiment of the present application is shown;

[0042] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0044] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0045] Figure 1 A flowchart of a method for determining concrete slump loss based on a blockchain system according to an embodiment of the present application is shown. The method comprises:

[0046] S110, acquiring a license plate image of a concrete transport vehicle.

[0047] In some embodiments, the license plate image of the concrete transporting vehicle is acquired through various sensing devices, such as IOT devices (Internet of Things) including various sensors.

[0048] In some embodiments, the license plate image includes 26 uppercase English letters and / or 10 natural integers from 0 to 9.

[0049] S120, inputting the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; inputting the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image.

[0050] Among them, the image feature extraction model is a convolutional neural network, including a convolution layer, a pooling layer and a batch normalization layer.

[0051] In some embodiments, the image feature extraction model and the character recognition model are trained by the following steps:

[0052] Generate a training sample set, wherein the training sample includes a license plate image and a corresponding license plate number;

[0053] The set of license plate images in the training sample set is taken as input, and the license plate number corresponding to the license plate image is taken as the expected output, so as to train the image feature extraction model and the character recognition model.

[0054] Furthermore, the training samples can be generated by the following steps:

[0055] a. Obtain several background images with different lighting conditions and background types, and crop the background images to obtain a license plate background of a preset width; for the accuracy of the training results, the number of background images is usually greater than 100; the background images can be downloaded from the Internet.

[0056] b. Randomly generate 5 virtual license plate characters in a blank background, and randomly twist the license plate characters at a preset angle; in order to improve the data processing speed, remove the first Chinese character and the first letter in the license plate number, that is, the Chinese character representing the location where the vehicle is photographed (such as Beijing), and the first letter (such as A, T, etc.), and only the last five digits and / or letters representing the vehicle information are recognized and trained.

[0057] The preset angle is set in advance according to the requirements, for example, 15°.

[0058] c. Add the twisted license plate characters to the license plate background to generate a first image (preliminary image). That is, add the twisted license plate characters in step b to the background cut out in step a;

[0059] d, randomly adding image noise to the first image to generate a second image;

[0060] e. randomly adding image blur to the second image to generate a third image;

[0061] f. Randomly crop the third image left and right and randomly crop it up and down to obtain the license plate number image.

[0062] The above steps (af) are automatically repeated to obtain several randomly combined synthetic license plate number images, that is, to generate the training sample set.

[0063] In some embodiments, the license plate image is taken as input to the image feature extraction model, and the image feature vector corresponding to the target image is taken as the expected output to obtain the image feature vector.

[0064] In some embodiments, the character recognition model may be a two-layer bidirectional LSTM deep bidirectional long short-term memory network, which is used to identify the types of characters, such as uppercase and lowercase English letters, numbers, and blank characters.

[0065] In some embodiments, the image feature vector is used as input to the character recognition model, and the character corresponding to the image feature vector is used as the expected output to obtain the string recognition result of the license plate image, that is, to determine the combination of numbers and / or letters of the license plate number.

[0066] S130: Determine information of concrete transported by the transport vehicle based on the character string recognition result.

[0067] In some embodiments, the identified license plate information can be compared with the vehicle transportation information stored in the database through the Internet of Things (IOT) system to determine the concrete information transported by the transport vehicle;

[0068] Among them, the database stores the license plate number information of each transport vehicle and the corresponding transported concrete information; the concrete information includes cement type, water content, amount of admixture and / or slump information of concrete out of the tank, etc.; the slump of concrete out of the tank is that after the concrete is manufactured, a slump test is required to determine the relevant information of the concrete, and the data obtained through the experiment is the slump of concrete out of the tank.

[0069] S140, obtaining the concrete slump value at the construction site. If the value is less than a threshold, obtaining the ambient temperature; based on the concrete information and the ambient temperature, calculating the slump of the concrete through a preset algorithm, determining the factors causing the slump loss of the concrete, and uploading the calculated data to the blockchain system.

[0070] In some embodiments, when the transport vehicle arrives at the construction site, the slump of the concrete needs to be tested to determine whether the slump value of the concrete at the construction site meets the construction standards;

[0071] Furthermore, the slump value of the concrete at the construction site is compared with a preset threshold value. If it is less than the threshold value (does not meet the standard, the slump loss is large), it is necessary to determine the factors causing the slump loss of the concrete, so as to make subsequent improvements and reduce economic costs. The slump value measured on site, the concrete information and the factors causing the slump loss of the concrete are uploaded to the blockchain system together; the threshold value can be preset according to the application scenario, and can be a fixed value or a set of value intervals;

[0072] If it is greater than or equal to the threshold (meets the standard), the slump value and the concrete information measured on site will be uploaded to the blockchain system.

[0073] Specifically, there are many factors that cause the slump loss of concrete. In this disclosure, only the factors that may have a greater impact on the slump loss during transportation, such as transportation time and ambient temperature, are studied. It should be noted that the proportion of ingredients in each type of concrete is generally a fixed value, which is not discussed in this disclosure;

[0074] In practical applications, through a large number of experiments, it is known that the ambient temperature has a great influence on the slump of concrete. When the ambient temperature is higher, the concrete slump loss rate is faster, and when the atmospheric temperature is lower, the concrete slump loss rate is slower. The slump loss rate of commercial concrete will also change with the extension of time. After the mixing is completed, the slump loss rate is faster in the early stage and slower in the later stage; the loss rate is faster in summer and slower in winter. Moreover, in the same period of time, the greater the slump of the concrete out of the tank, the greater the slump loss value will be, and the smaller the slump of the concrete out of the tank, the smaller the slump loss value will be.

[0075] Based on the above characteristics, in the present disclosure, the relationship between the slump of concrete and the temperature is determined in the following manner. For the accuracy of the experiment, only the temperature is used as a variable for explanation, and the other variables remain the same.

[0076] In some embodiments, if the ambient temperature is in the first temperature interval, the slump of concrete is calculated by the following formula:

[0077] T g =T c X -2 (1+K)

[0078] If the ambient temperature is in the second temperature range, the concrete slump is calculated by the following formula:

[0079]

[0080] Among them, the T c , is the slump of concrete when it leaves the tank;

[0081] C is the time it takes for concrete to be discharged from the tank, in hours;

[0082] X=C+1

[0083] The K is the cement variety difference coefficient, and its specific value is shown in Table 1:

[0084] Serial number Cement Type K 1 Ordinary cement 0 2 Ordinary cement mixed with fly ash 0.2 3 Slag cement 0.2 4 Slag cement mixed with fly ash 0.4

[0085] Table 1

[0086] The cement types and corresponding K values ​​in Table 1 are for illustration only and can be supplemented based on experimental data or manual experience.

[0087] The first temperature range is usually a temperature range higher than normal temperature, such as 26-40°C;

[0088] The second temperature range is usually a temperature range lower than or equal to room temperature, such as 1 to 25°C;

[0089] It should be noted that, based on considerations of most application scenarios, the normal temperature is set to 25° C., and the first temperature range and the second temperature range are the ambient temperatures of most application scenarios.

[0090] Furthermore, the calculated concrete slump is compared with the concrete slump value measured at the construction site to determine the relationship between the ambient temperature and transportation time and the concrete slump loss. If the calculated concrete slump is close to the concrete slump value measured at the construction site, such as the calculated slump is 12 and the slump measured at the construction site is 13, it can be determined that the factors causing the concrete loss are the ambient temperature and transportation time. At this time, the concrete slump value measured at the construction site, the corresponding transport vehicle information (license plate and / or vehicle photo, etc.), the factors causing the slump loss (temperature, etc.) and / or the corresponding concrete information are uploaded to the blockchain through the IOT device;

[0091] Furthermore, if the calculated slump of concrete is significantly different from the slump value of concrete measured at the construction site, such as the calculated slump is 5 and the slump measured at the construction site is 13, it can be determined that the factors causing the loss of the concrete also include other factors, such as raw material factors, etc. At this time, the slump value of concrete measured at the construction site, the corresponding transport vehicle information (license plate and / or vehicle photo, etc.), the factors causing slump loss (raw materials, etc.) and / or the corresponding concrete information, etc. are uploaded to the blockchain through the IOT device.

[0092] In some embodiments, the reasonable transportation time of the concrete can also be calculated based on the obtained construction site temperature and the slump of the concrete when it leaves the tank;

[0093] Specifically, the reasonable transportation time of concrete is calculated by the following formula:

[0094] or

[0095]

[0096] The reasonable transportation time is then compared with the actual transportation time (calculated by license plate information) calculated by the Internet of Things system to determine the relationship between the slump loss caused by transportation and the transportation time, and upload them to the blockchain system together; for example, the slump of qualified concrete at the construction site is 10, and the reasonable transportation time calculated by the construction site temperature and the slump of concrete when it is out of the tank is 2 hours. The slump actually measured at the construction site is 5, and the actual transportation time is 3 hours. It can be known that the relationship between the slump loss of the concrete and the transportation time, that is, if the transportation time exceeds 1 hour, the slump loss of the concrete is 5, and the data is uploaded to the blockchain system so that the information can be sent to relevant personnel and corresponding managers who transport the same type of concrete.

[0097] According to the embodiments of the present application, the following technical effects are achieved:

[0098] Through linkage with the Internet of Things system, the information of transport vehicles can be automatically identified, and the factors causing slump loss can be automatically identified through preset algorithms and uploaded to the blockchain system. This can accurately determine the factors affecting concrete loss, and provide inspiration for subsequent managers to manage construction site information and adjust concrete distribution methods. No human participation is required throughout the process, which reduces labor costs and improves construction efficiency.

[0099] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0100] The above is an introduction to the method embodiment. The following is a further explanation of the scheme described in this application through an apparatus embodiment.

[0101] Figure 2 A block diagram of a concrete slump loss determination device 200 based on a blockchain system according to an embodiment of the present application is shown. Figure 2 As shown, the device 200 includes:

[0102] An acquisition module 210 is used to acquire a license plate image of a concrete transport vehicle;

[0103] The recognition module 220 is used to input the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; input the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image;

[0104] A determination module 230, configured to determine information of concrete transported by the transport vehicle based on the character string recognition result;

[0105] The discrimination module 240 is used to obtain the slump value of concrete at the construction site. If the value is less than a threshold, the ambient temperature is obtained; based on the concrete information and the ambient temperature, the slump of the concrete is calculated through a preset algorithm, the factors causing the slump loss of the concrete are determined, and the results are uploaded to the blockchain system.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0107] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. As shown, the device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 302 or loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0108] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processing unit 301 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the CPU 301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the CPU 301 may be configured to perform the method 100 in any other suitable manner (e.g., by means of firmware).

[0110] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0111] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0112] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0114] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for determining concrete slump loss based on a blockchain system, characterized in that: include: Obtain the license plate image of the concrete transport vehicle; Input the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; input the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image; Based on the character string recognition result, determining the concrete information transported by the transport vehicle; Obtain the concrete slump value of the mixing station. If the value is less than a threshold, obtain the ambient temperature. Based on the concrete information and the ambient temperature, calculate the slump of the concrete through a preset algorithm, determine the factors causing the slump loss of the concrete, and upload them to the blockchain system. The concrete information includes cement type, water content, amount of admixture and / or slump information of concrete out of the tank; The calculating the slump of the concrete based on the concrete information and the ambient temperature acquired in real time by a preset algorithm includes: If the ambient temperature is in the first temperature range, the slump of concrete is calculated by the following formula: T g =T c X -2 If the ambient temperature is in the second temperature range, the slump of concrete is calculated by the following formula: T g =T c X -3 / 2 Among them, the T c , is the slump of concrete when it leaves the tank; C is the slump of concrete out of the tank; Said X=C+1.

2. The method according to claim 1, characterized in that The image feature extraction model is a convolutional neural network, including a convolution layer, a pooling layer and a batch normalization layer.

3. The method according to claim 2, characterized in that The image feature extraction model and the character recognition model are trained by the following steps: Generate a training sample set, wherein the training sample includes a license plate image and a corresponding license plate number; The set of license plate images in the training sample set is taken as input, and the license plate number corresponding to the license plate image is taken as the expected output, so as to train the image feature extraction model and the character recognition model.

4. The method according to claim 1, characterized in that: The calculating the slump of the concrete based on the concrete information and the ambient temperature acquired in real time by a preset algorithm also includes: If the concrete information includes the slump of concrete out of the tank, and the ambient temperature is in the first temperature range, the slump of concrete is calculated by the following formula: T g =T c X -2 (1+K) If the concrete information includes the slump of concrete out of the tank, and the ambient temperature is in the second temperature range, the slump of concrete is calculated by the following formula: Wherein, K is the coefficient of difference among cement varieties.

5. The method according to claim 4, characterized in that The method of obtaining the license plate image of the concrete transport vehicle comprises: Through the IOT device, the license plate image of the concrete transport vehicle is obtained.

6. A concrete slump loss determination device based on a blockchain system, characterized in that: include: An acquisition module is used to acquire the license plate image of the concrete transport vehicle; A recognition module, used to input the license plate image into a pre-trained image feature extraction model to obtain an image feature vector of the license plate image; input the image feature vector into a pre-trained character recognition model to obtain a character string recognition result of the license plate image; A determination module, used for determining the concrete information transported by the transport vehicle based on the character string recognition result; A discrimination module is used to obtain the concrete slump value of the mixing station. If the value is less than a threshold, the ambient temperature is obtained; based on the concrete information and the ambient temperature, the slump of the concrete is calculated through a preset algorithm, the factors causing the slump loss of the concrete are determined, and the information is uploaded to the blockchain system; the concrete information includes cement type, water content, amount of admixture, and / or slump information of concrete out of the tank; The discrimination module is further configured to: If the ambient temperature is in the first temperature range, the slump of concrete is calculated by the following formula: T g =T c X -2 If the ambient temperature is in the second temperature range, the slump of concrete is calculated by the following formula: T g =T c X -3 / 2 Among them, the T c , is the slump of concrete when it leaves the tank; C is the slump of concrete out of the tank; Said X=C+1.

7. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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