Raw material blind sampling method, device, equipment and system

Abstract

The invention provides a raw material blind sampling method, device, equipment and system, and the method comprises the steps: recognizing the equipment type of raw material transportation equipment and the raw material type of raw materials loaded in the raw material transportation equipment after the raw material transportation equipment of a transaction party is detected; according to the equipment type and the raw material type, creating a blind sampling task about the raw material, and transmitting the blind sampling task to a preset sampling machine; obtaining a blind sampling detection result fed back by an online detection system, the blind sampling detection result being a result obtained by detecting the blind sampling result after the online detection system receives the blind sampling result obtained by blind sampling of the raw material by the sampler according to the blind sampling task; and displaying the blind sampling detection result. According to the method, the problems of low raw material sampling and detection efficiency, high cost and the like in the prior art can be effectively solved.

Description

Technical Field

[0001] The present application relates to the field of automation technology, and in particular to a method, device, equipment and system for blind sampling of raw materials. Background Art

[0002] In the modern production and processing industry, raw material procurement is a crucial link that directly affects production efficiency and product quality.

[0003] Taking a rice processing plant as an example, the raw grain purchasing process usually includes multiple steps: farmers arrive at the factory guard to register vehicle information, samplers take samples based on vehicle information, laboratory inspectors test samples, calculate the unit price of raw grain and feedback to farmers, farmers decide whether to sell raw grain, weighers weigh vehicles, and finally the financial department settles. The entire process involves multiple positions including guards, samplers, inspectors, weighers, and financial personnel, and personnel from each position are required to participate in the relevant process, involving a large number of manual operations, resulting in low efficiency and prone to human errors. For example, the guard may make mistakes when registering vehicle information, the sampler may not be accurate enough when taking samples, the inspector may make errors when testing samples, the weigher may deviate when weighing, and the financial staff may make calculation errors when settling accounts. These mistakes will lead to inaccurate price calculations and increase corporate costs.

[0004] Therefore, there is an urgent need to provide a system that can automate the raw material procurement process, reduce human intervention and errors, and improve efficiency and accuracy. This is a problem that needs to be solved urgently in the current production and processing industry. Summary of the invention

[0005] The present application provides a raw material blind sampling method, device, equipment and system to solve the problems of low efficiency and high cost of raw material sampling and detection in the prior art.

[0006] In a first aspect, the present application provides a blind sampling method for raw materials, which is applied to an MES system, and the method includes: after detecting the raw material transportation equipment of the trading party, identifying the equipment type of the raw material transportation equipment and the raw material type loaded in the raw material transportation equipment; creating a blind sampling task for the raw material according to the equipment type and the raw material type, and transmitting the blind sampling task to a preset sampling machine; obtaining a blind sampling detection result fed back by an online detection system, the blind sampling detection result is the result obtained by the online detection system after receiving the blind sampling result obtained by the sampling machine according to the blind sampling task for blind sampling of the raw material; and displaying the blind sampling detection result.

[0007] In one embodiment, the method also includes: acquiring image data about the raw material transportation equipment, and identifying the raw material transportation equipment and the raw materials loaded thereon in the image data through a target detection algorithm; the identifying the equipment type of the raw material transportation equipment and the type of raw materials transported in the equipment includes: comparing the raw material transportation equipment and the raw materials in the image data with a plurality of pre-stored raw material transportation equipment and a plurality of raw materials, respectively, to obtain a comparison result; and identifying the equipment type and the raw material type based on the comparison result.

[0008] In one embodiment, creating a blind sampling task regarding the raw material based on the equipment type and the raw material type includes: determining blind sampling parameters for blind sampling of the raw material based on the equipment type and the raw material type; and creating a blind sampling task regarding the raw material based on the blind sampling parameters.

[0009] In one embodiment, the blind sampling task includes at least one of the following blind sampling parameters: sampling depth, sampling point location, diameter and length of the sampling tube used for blind sampling; the blind sampling parameters for blind sampling of the raw material are determined according to the equipment type and the raw material type, including at least one of the following: the sampling depth and / or sampling point location of the blind sampling task is determined according to the equipment type and the height of the raw material loaded by the raw material transportation equipment; according to the raw material type, the particle size and fluidity information of the raw material is obtained, and the diameter of the sampling tube used for blind sampling is determined according to the particle size and fluidity information.

[0010] In one embodiment, the method further includes: generating a labeling instruction, wherein the labeling instruction is used to instruct the sampling machine to label the blind sampling result after blind sampling; creating a blind sampling task regarding the raw material based on the blind sampling parameters, including: creating a blind sampling task regarding the raw material based on the blind sampling parameters and the labeling instruction.

[0011] In one embodiment, the blind sampling test result includes the pricing result of the raw material; after displaying the blind sampling test result, it also includes: in response to the transaction party's consent trigger operation for the pricing result, obtaining the net weight of the raw material loaded on the raw material transportation equipment; determining the raw material settlement amount based on the net weight of the raw material and the pricing result.

[0012] In one embodiment, the method further includes: obtaining transaction information of the transaction party; and remitting the raw material settlement amount to the transaction party terminal of the transaction party according to the transaction information.

[0013] According to a second aspect of the present application, a raw material blind sampling device is provided, which is applied to an MES system, and the device includes: a raw material identification module, which is configured to identify the equipment type of the raw material transportation equipment and the raw material type loaded in the raw material transportation equipment after detecting the raw material transportation equipment of the trading party; a task processing module, which is configured to create a blind sampling task about the raw material according to the equipment type and the raw material type, and transmit the blind sampling task to a preset sampling machine; a result acquisition module, which is configured to obtain the blind sampling detection result fed back by an online detection system, and the blind sampling detection result is the result obtained by the online detection system after receiving the blind sampling result obtained by the sampling machine according to the blind sampling task to blindly sample the raw material; and a result display module, which is configured to display the blind sampling detection result.

[0014] According to the third aspect of the present application, an electronic device is provided, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement a blind sampling method for raw materials as provided in any one of the first aspects above.

[0015] According to a fourth aspect of the present application, a raw material blind sampling system is provided, comprising an MES system, a sampling machine and an online detection system, wherein the sampling machine is electrically connected to the MES system and the online detection system, respectively, for receiving a blind sampling task issued by the MES system, and blindly sampling the raw material according to the blind sampling task to obtain a blind sampling result, and transmitting the blind sampling result to the online detection system; the online detection system is electrically connected to the sampling machine and the MES system, respectively, for inspecting the blind sampling result, obtaining a blind sampling detection result, and transmitting the blind sampling detection result to the MES system; the MES system is used to execute the raw material blind sampling method provided in any one of the first aspects above.

[0016] The blind sampling method, device, equipment and system of raw materials provided by the present application, after detecting the raw material transportation equipment of the trading party, identify the equipment type of the raw material transportation equipment and the raw material type of the raw material loaded in the raw material transportation equipment, create a blind sampling task about the raw material according to the equipment type and the raw material type, and transmit the blind sampling task to the preset sampling machine, obtain the blind sampling detection result fed back by the online detection system, the blind sampling detection result is the result obtained by the online detection system after receiving the blind sampling result obtained by the sampling machine for blind sampling of the raw material according to the blind sampling task, and the blind sampling result is inspected, and the blind sampling detection result is displayed. In this process, the automated blind sampling and inspection of raw materials are realized, which can significantly improve the efficiency and transparency of the raw grain acquisition process of production and processing factories such as rice processing factories, and can reduce the possibility of human errors and behind-the-scenes operations, reduce enterprise costs, and enhance market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] Figure 1 A possible application scenario diagram provided for an embodiment of the present application;

[0019] Figure 2 A schematic diagram of a process for blind sampling of raw materials provided in an embodiment of the present application;

[0020] Figure 3 for Figure 2 Schematic diagram of the process of step S202;

[0021] Figure 4 A schematic diagram of another blind sampling method for raw materials provided in an embodiment of the present application;

[0022] Figure 5 A system framework diagram provided for an exemplary embodiment of the present application;

[0023] Figure 6 A schematic structural diagram of a raw material blind sampling device provided in an embodiment of the present application;

[0024] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0025] Figure 8 A schematic diagram of the structure of a raw material blind sampling system provided in an embodiment of the present application.

[0026] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0027] 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.

[0028] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user data and other information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals. It should be noted that in the embodiments of this application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0029] At present, all rice production and processing factories are involved in the raw grain purchase process, which includes: farmers register their vehicle information at the factory guard, transmit the vehicle information to the sampler for sampling, and then pass the sample to the laboratory for testing, moisture, impurities, rice yield and other indicators, and calculate the unit price of raw grain based on the indicators and feed it back to the farmers. Farmers decide whether to sell according to their own situation. If they decide to sell, they will weigh the grain. After weighing, the raw grain summary of the vehicle will be calculated based on the weight, and the settlement will be completed in the financial department before leaving. In the above process, the price is manipulated by people and farmers behind the scenes, or human errors lead to inaccurate price calculations, which increases costs for the company. The positions involved include at least guards, samplers, inspectors, weighers, and finance.

[0030] In view of the above problems, the embodiment of the present application provides a blind sampling scheme for raw materials, through the identification of raw material transportation equipment (i.e., vehicle), automatic blind sampling, automatic detection of blind sampling data, as well as pricing, outdoor large-screen display, etc. Farmers can place vehicles on the license plate recognition system for automatic registration. After generating a blind sampling task, they can automatically blind sample by communicating with the robotic arm of the sampling machine. The samples are automatically packaged and affixed with a unique identification code (i.e., label), and then sent to the laboratory's online detection system by unmanned methods such as conveyor belts or drones, reducing contamination and errors during sample delivery. The test results are automatically displayed and can be uploaded to the cloud or central database. The unit price of raw grain is quickly calculated by the algorithm, reducing human calculation errors, and the unit price of raw grain of the vehicle can be displayed on the outdoor large screen. Specifically, after the farmer agrees to sell, after entering the unmanned weighing, the outdoor large screen automatically displays the settlement unit price, prints the settlement document, and pushes the financial settlement system. After the financial settlement system receives the settlement task, the financial department can confirm and pay the farmer, and the process ends. After the optimization, the positions involved only require inspectors and finance personnel, or even none at all, saving a lot of manpower and material resources.

[0031] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0032] Figure 1 FIG. 1 is an application scenario diagram of the method applicable to the embodiment of the present application. In this application scenario, raw grain detection is used as an example for illustration. Figure 1As shown, the application scenario includes: a manufacturing execution system (Manufacturing Execution System, MES) 110, a raw material transportation equipment 120, a camera 130, a sampling machine 140, and an online detection system (the scenario shows a management server 150 of the online detection system, and in actual applications it can also include multiple inspection lines. The management server 150 is used to manage the entire inspection process, such as interacting with the sampling machine 140, starting the inspection process, and collecting inspection data, etc.). Optionally, the raw grain transport equipment 120 can be a vehicle loaded with rice. The farmer drives the vehicle to the grain collection area of ​​the rice production and processing plant. A camera is arranged in the grain collection area. When the camera detects the vehicle in the grain collection area, it sends image data to the MES system 110. The MES system 110 receives the image data, identifies the vehicle type and the grain type loaded by the vehicle in the image data, and then creates a blind sampling task for the grain according to the vehicle type and the grain type, and sends the blind sampling task to the preset sampling machine 140. The sampling machine 140 may be a fixed sampling machine fixed in the grain collection area, or a movable sampling machine that can be placed in other areas. This embodiment does not specifically limit this. After receiving the blind sampling task, the sampling machine 140 uses a mechanical arm to perform blind sampling of the grain in the vehicle according to the blind sampling task, and transmits the blind sampling result to the online detection system for detection. The management server 150 generates a detection result for the blind sampling result, which is displayed by the MES system 110 or sent by the MES system 110 to the outdoor large screen 160 for display. Optionally, the management server 150 can also transmit the detection result to a cloud server, which stores the detection result.

[0033] It is understood that sampling is the process of extracting a small sample from a large batch of materials for the purpose of quality testing, component analysis or other types of evaluation. Sampling can be applied to various industrial and scientific fields, including but not limited to food processing, pharmaceuticals, chemicals, agriculture, mining, etc. Specifically, the main purpose of sampling is to ensure the consistency of the quality and characteristics of the entire batch of materials. By blind sampling, representative data about the entire batch of materials can be obtained for further analysis and decision-making. In modern production and processing industries, it is often necessary to sample and test raw materials, so efficient sampling methods are essential.

[0034] based on Figure 2 The application scenario shown in the figure, the embodiment of the present application provides a raw material blind sampling method, which can be applied Figure 2 MES system 110 in Figure 2 As shown, the method includes steps S201-S204:

[0035] Step S201: After detecting the raw material transportation equipment of the transaction party, identifying the equipment type of the raw material transportation equipment and the raw material type loaded in the raw material transportation equipment.

[0036] The raw material transportation equipment can be a vehicle, a conveyor, a crane and other equipment, and the raw material can be a granular raw material or a liquid raw material, etc. This embodiment does not specifically limit the specific form of the raw material transportation equipment and the raw material. This embodiment is described by taking the raw material transportation equipment as a vehicle and the raw material as rice as an example.

[0037] For example, a sensor (such as a camera, a laser radar, a wireless radio frequency identification RFID reader, etc.) can be used to detect raw material transportation equipment approaching or entering a detection area (the scope of the detection area can be determined by a person skilled in the art according to actual application). The sensor transmits the detected signal to the MES system, which responds to the detection signal and identifies the equipment type and raw grain type.

[0038] In some examples, considering that the factory has its own transportation equipment, in order to avoid misidentification of the transportation equipment within the factory, the MES system can record and store the transportation equipment in the factory in advance. When the detection signal is received, the transportation equipment in the image data can be compared with the stored transportation equipment in the factory. For example, the license plate number is detected by the target detection algorithm, and the license plate number of the current transportation equipment is compared with the license plate number of the vehicle in the factory to identify whether the detected transportation equipment is the raw material transportation equipment of the transaction party, so as to effectively avoid misidentification of the raw material transportation equipment of the transaction party.

[0039] In one embodiment, the raw material transport equipment and the loaded raw materials in the image data can be identified by a target detection algorithm, and the equipment type and the raw material type can be further identified to improve the recognition accuracy and efficiency. Specifically, the method can also include the following steps: acquiring image data about the raw material transport equipment, and identifying the raw material transport equipment and the loaded raw materials in the image data by a target detection algorithm.

[0040] Specifically, the image data of the raw material transportation equipment can be collected by the camera and transmitted to the MES system. The MES system uses the built-in target detection algorithm (such as YOLO) to identify the raw material transportation equipment and raw materials in the image data.

[0041] The above-mentioned step S201 identifies the equipment type of the raw material transportation equipment and the type of raw materials transported in the equipment, specifically: the raw material transportation equipment and the raw materials in the image data are respectively compared with a plurality of pre-stored raw material transportation equipment and a plurality of raw materials to obtain a comparison result; based on the comparison result, the equipment type and the raw material type are identified.

[0042] Illustratively, the multiple raw material transportation equipment pre-stored in the MES system can be labeled with an equipment type label for each raw material transportation equipment to facilitate identification and distinction; accordingly, a raw material type label can also be affixed to each raw material. By comparing the identified equipment and raw materials with the pre-stored equipment and raw materials, and combining the equipment type label and the raw material type label, the equipment type and raw material type that currently require blind sampling can be quickly identified.

[0043] In some examples, if the equipment and raw materials in the image data cannot be identified based on the pre-stored equipment and raw materials, it may be a new equipment type and / or raw material. The MES system can transmit the image data to the user terminal of the manager, who will make further judgments and identify the corresponding equipment type and raw material type in response to the manager's judgment results.

[0044] Through the above technical solution, the recognition accuracy and efficiency of equipment type and raw material type can be effectively improved.

[0045] Step S202: Create a blind sampling task for the raw material according to the equipment type and the raw material type, and transmit the blind sampling task to a preset sampling machine.

[0046] After identifying the equipment type and raw material type, the MES system creates a blind sampling task for the raw material and transmits the blind sampling task to the sampling machine. The sampling machine automatically performs blind sampling, and the entire process does not require human involvement.

[0047] In one embodiment, a blind sampling task is created by determining blind sampling parameters, specifically, as follows: Figure 3 As shown, the above step S202 creates a blind sampling task for the raw material according to the equipment type and the raw material type, and may include the following steps S202a and S202b.

[0048] Step S202a: Determine blind sampling parameters for blind sampling of the raw material according to the equipment type and the raw material type.

[0049] Optionally, the blind sampling task may include at least one of the following blind sampling parameters: sampling depth, sampling point location, diameter and length of a sampling tube used for blind sampling.

[0050] Among them, in addition to the above parameters, the blind sampling parameters may also include other parameters, such as the motor speed of the sampling machine (the sampling amount can be effectively controlled by controlling the speed of the sampling machine), etc. In addition, in addition to the blind sampling parameters, the blind sampling task may also set task information such as the blind sampling task number, blind sampling time, and blind sampling times. Table 1 below shows a possible blind sampling parameter configuration, taking the database table as an example for introduction.

[0051] Table 1:

[0052]

[0053] In one example, the above step S202a determines the blind sampling parameters for blind sampling of the raw material according to the equipment type and the raw material type, which can be carried out in the following manner: according to the equipment type and the height of the raw material loaded by the raw material transportation equipment, the sampling depth and / or sampling point position of the blind sampling task is determined.

[0054] In this example, if the equipment type is a rice transport truck, since the truck compartment is relatively high and the rice is usually relatively uniform, the sampling depth can be determined according to the height of the equipment, and a representative sample can be obtained by random sampling. For example, the height of the raw material transport equipment loading the raw materials is 2.5 meters, the maximum sampling depth can be determined as 2.5 meters, and the sampling depth is designed to be 2 meters. In some examples, the sampling point position can also be designed, and sampling is performed at the sampling point position according to the sampling depth. Further, according to the rice loading height of 2.5 meters, the sampling depth can be set to different levels of the loading height, such as 0.5 meters, 1.5 meters and 2.5 meters, to ensure the representativeness of the sample. Accordingly, the sampling point position can also be sampled at different positions in the compartment, such as the front, middle, and rear, to cover the entire range of the rice pile, and the sampling depth of each position can be a different level set as above.

[0055] By considering the type of equipment (rice transport truck) and the height at which the raw material transport equipment is loaded with raw materials (2.5 meters), the sampling depth and sampling point locations for the blind sampling mission can be determined to obtain a representative rice sample.

[0056] In another example, the above step S202a determines the blind sampling parameters for blind sampling of the raw material according to the equipment type and the raw material type, which can be carried out in the following manner: according to the raw material type, the particle size and fluidity information of the raw material are obtained, and the tube diameter used for blind sampling is determined according to the particle size and fluidity information.

[0057] Taking rice as an example, the average length of rice grains is 7 mm, the width is 2 mm, and the rice grains have good fluidity, but may be blocked in the smaller diameter of the skewer. Since the average width of the rice grains is 2 mm, the inner diameter of the skewer needs to be greater than 2 mm to ensure that the rice grains can pass smoothly. In order to avoid blocking, a skewer with an inner diameter of 3-5 times the width of the grain is usually selected. Since rice has good fluidity, but may be blocked in the smaller diameter of the skewer, it is necessary to select an appropriate skewer diameter to ensure smooth sampling. For example, a skewer with an inner diameter of 3-5 times of 2 mm is selected, that is, the inner diameter is between 6 mm and 10 mm. In order to ensure the smooth progress of sampling, a skewer with an inner diameter of 8 mm can be selected. Through the above method, it can be determined that the diameter of the skewer used for blind sampling of rice is 8 mm, to ensure the smooth progress of the sampling process and the representativeness of the sample.

[0058] The diameter of the tube used for blind sampling can be determined by considering the raw material type (rice) and the obtained particle size (average width 2 mm) and flowability information. Based on the width and flowability of the rice particles, a tube with an inner diameter of 8 mm was selected to ensure a smooth sampling process and representative samples.

[0059] In some examples, the above two examples may be combined to jointly determine the blind sampling parameters, and the relevant description will not be repeated.

[0060] Step S202b: creating a blind sampling task for the raw material according to the blind sampling parameters.

[0061] In this embodiment, in addition to creating a blind sampling task based on blind sampling parameters, a blind sampling task of a raw material can also be created in combination with other data information, such as labeling instructions. Exemplarily, the method provided in this embodiment can also include the following steps: generating a labeling instruction, wherein the labeling instruction is used to instruct the sampling machine to label the blind sampling result after blind sampling.

[0062] Exemplarily, in the process of generating labeling instructions, the information that needs to be included on the label can be clearly defined. This information may include the sample number (unique identifier), sampling date and time, sampling location or batch number, raw material type, sampling test results, and other relevant information (such as storage conditions, transportation information, etc.). The label format can also be designed. The format of the label can include text, barcode, QR code, etc. to ensure that the label format meets the requirements of the reading device.

[0063] The above step S202b creates a blind sampling task for the raw material according to the blind sampling parameters, which can be done in the following manner: creating a blind sampling task for the raw material according to the blind sampling parameters and labeling instructions.

[0064] By carrying labeling instructions in the blind sampling task, the sampling machine can label the sampled raw materials (i.e., blind sampling results) according to the labeling instructions after completing the blind sampling, so as to distinguish multiple samples. After uploading to the online detection system, the detection system can detect the sampled raw materials corresponding to the corresponding label and record the detection results corresponding to the label to facilitate traceability of the detection results.

[0065] Continuing as shown in 2, step S203, obtaining the blind sampling detection result fed back by the online detection system, the blind sampling detection result is the result obtained by the online detection system after receiving the blind sampling result obtained by the sampling machine for blind sampling of the raw material according to the blind sampling task, and then inspecting the blind sampling result.

[0066] In this embodiment, after the blind sampling task is sent to the sampling machine, the sampling machine starts sampling according to the sent blind sampling task. After obtaining the sampled raw material (i.e., the sampling result), it can be labeled by the labeling machine through the conveying device, and then transmitted to the inspection production line of the online inspection system through the transmission device. The inspection production line can be inspected automatically, or the inspector can receive the inspection task of the MES system, scan the label (or QR code) on the sample to start the inspection, and after the inspection is completed, the inspection results such as inspection parameters (moisture, impurities, capacity, imperfect particles) are returned to the MES system.

[0067] Step S204: display the blind sampling detection result.

[0068] After obtaining the blind sampling test results fed back by the online detection system, the MES system displays the blind sampling test results. For example, the blind sampling test results can be displayed on an outdoor large screen so that the transaction parties can view the blind sampling test results in a timely manner. The blind sampling test results mainly include the inspection parameters of the above examples. In some examples, the blind sampling test results can also carry the system or user's pricing of the raw materials based on the inspection parameters. The specific process will be described in detail in the following embodiments, and will not be repeated here.

[0069] Through the above technical solution, automated blind sampling and inspection of raw materials are realized, which can significantly improve the efficiency and transparency of the raw grain procurement process in production and processing plants such as rice processing plants, and reduce the possibility of human errors and behind-the-scenes operations, reduce corporate costs, and enhance market competitiveness.

[0070] Figure 4This is a flow chart of another blind sampling method for raw materials provided in an embodiment of the present application. Based on the above embodiment, in addition to realizing automated blind sampling and detection, this embodiment can also realize automated pricing and settlement processes to further improve the efficiency of factory raw material acquisition. Specifically, the blind sampling detection results in this embodiment include the pricing results of the raw materials. In addition to the above steps S201-S204, this embodiment can also include the following steps S401 and S402 after displaying the blind sampling detection results in step S204.

[0071] Step S401: In response to a trigger operation of the transaction party agreeing to the pricing result, the net weight of the raw material loaded on the raw material transportation equipment is obtained.

[0072] Exemplarily, after the MES system displays the blind sampling test results (the blind sampling test results can be displayed on an outdoor large screen), the transaction party can agree to the pricing result (or reject it) on the display screen through touch screen or key operation and terminal interaction, that is, agree (or reject) to sell the raw grain. In response to the transaction party's consent trigger operation, the MES system can interact with the weighing equipment, use the weighing equipment to weigh the raw material transportation equipment loaded with raw materials, and use the weighing result to subtract the gross weight of the equipment to obtain the net weight of the raw materials. The weighing equipment transmits the net weight data to the MES system for the next step.

[0073] It can be understood that in response to the conditions or states on which the executed operations depend, when the dependent conditions or states are met, one or more operations executed can be in real time or have a set delay; unless otherwise specified, there is no restriction on the order of execution of the multiple operations executed.

[0074] Step S402: Determine the raw material settlement amount according to the net weight of the raw material and the pricing result.

[0075] Exemplarily, the pricing result includes a determined raw material unit price, and the raw material settlement amount can be calculated as follows: raw material settlement amount = raw material net weight × raw material unit price.

[0076] Through the above technical solution, the pricing and settlement process can be automated to further improve the factory's raw material procurement efficiency while optimizing the transaction experience.

[0077] Furthermore, after determining the raw material settlement amount, the MES system can also carry a payment function, thereby realizing the full-process transaction and improving efficiency. Specifically, the method provided in this embodiment can also include the following steps: obtaining the transaction information of the transaction party; and according to the transaction information, remitting the raw material settlement amount to the transaction party terminal of the transaction party.

[0078] Exemplarily, the transaction information of the transaction party can be obtained by the MES system by scanning the transaction party's identity information, bank card information, etc. during the equipment registration stage (that is, the stage when the raw material transportation equipment is detected), or by the MES system interacting with the transaction party's terminal. Optionally, after determining the raw material settlement amount, the MES system can interact with the settlement system to transmit the raw material settlement amount to the settlement system, which is then imported into the transaction party's terminal by the settlement system. This process effectively realizes the transaction link.

[0079] To facilitate further understanding of the embodiments of the present application, an exemplary embodiment is described below. This embodiment uses a computer and software system to provide a more convenient and intelligent blind sampling method for purchasing raw grains, which can integrate many parameters and customer requirements. Through the MES system and the camera, sampling machine, transmission device, inspection equipment, and weighing equipment, the configured parameters are issued to realize the automated and intelligent sampling, inspection, weighing, and settlement of raw material purchase processes. In this exemplary embodiment, the system framework is as follows Figure 5 As shown, next combine Figure 5 The example system framework diagram introduces the process as follows:

[0080] Vehicle registration (unmanned): The MES system communicates with the camera in real time to obtain the farmer’s vehicle information, identify the vehicle type, raw material type, driver, bank card and other information, and generate a blind sampling task for the vehicle.

[0081] Blind sampling (unmanned): In the blind sampling task, the sampling depth, sampling point location, sampling tube diameter and length, and motor speed of the vehicle model can be set according to the system. After the MES system sends the sampling parameters (socket protocol interaction), the sampling machine starts to press the send message to sample, and the labeling machine labels it through the transmission device, and then transmits it to the laboratory through the transmission device.

[0082] Inspection: The inspector can receive the inspection task from the MES system and scan the QR code on the sample to start the inspection (automatic inspection is also possible). After the inspection is completed, the MES system automatically collects the inspection parameters (moisture, impurities, capacity, imperfect particles) and uploads them to the system.

[0083] Outdoor large screen (unmanned): Real-time display of raw material inspection information and unit price for each truck.

[0084] Weighing (unmanned): After the farmer agrees to sell, he places the vehicle on the scale. After the MES reads the infrared switch vehicle through the modbus protocol and recognizes the vehicle in place, it will automatically save the gross weight information of the raw grain after 30 seconds, save the tare weight information after unloading, and automatically save the net weight information.

[0085] Settlement (unmanned): After weighing, farmers can scan the QR code at the gate, print out the pass, and obtain the raw grain settlement information (or directly sent by the MES system). The finance department receives the MES settlement task and pays the farmers for the raw grain.

[0086] The above improvement measures can significantly improve the efficiency and transparency of the raw grain procurement process of rice processing factories, reduce the possibility of human errors and behind-the-scenes operations, reduce corporate costs, and enhance market competitiveness. No samplers, weighers, security guards, etc. are required, which can greatly save labor costs.

[0087] Figure 6 is a schematic diagram of the structure of a raw material blind sampling device provided in an embodiment of the present application, which can be applied to an MES system, such as Figure 6 As shown, the device 600 includes a raw material identification module 601, a task processing module 602, a result acquisition module 603 and a result display module 604, wherein:

[0088] A raw material identification module 601, which is configured to identify the equipment type of the raw material transportation equipment and the raw material type loaded in the raw material transportation equipment after detecting the raw material transportation equipment of the transaction party;

[0089] A task processing module 602, which is configured to create a blind sampling task for the raw material according to the equipment type and the raw material type, and transmit the blind sampling task to a preset sampling machine;

[0090] A result acquisition module 603 is configured to acquire a blind sampling detection result fed back by an online detection system, wherein the blind sampling detection result is a result obtained by the online detection system after receiving the blind sampling result obtained by the sampling machine for blind sampling the raw material according to the blind sampling task and then inspecting the blind sampling result;

[0091] The result display module 604 is configured to display the blind sampling detection result.

[0092] In one embodiment, the device further comprises: an image acquisition module, which is configured to acquire image data about the raw material transportation equipment and identify the raw material transportation equipment and the raw materials loaded therein in the image data through a target detection algorithm;

[0093] The raw material identification module 601 includes:

[0094] a comparison unit, configured to compare the raw material transport device and the raw material in the image data with a plurality of pre-stored raw material transport devices and a plurality of raw materials, respectively, to obtain a comparison result;

[0095] An identification unit is configured to identify the equipment type and the raw material type according to the comparison result.

[0096] In one implementation, the task processing module 602 includes:

[0097] a parameter determination unit, configured to determine blind sampling parameters for blind sampling of the raw material according to the equipment type and the raw material type;

[0098] A creating unit is configured to create a blind sampling task for the raw material according to the blind sampling parameters.

[0099] In one embodiment, the blind sampling task includes at least one of the following blind sampling parameters: sampling depth, sampling point location, diameter and length of the sampling tube used for blind sampling;

[0100] The parameter determination unit is specifically configured to be at least one of the following: determining the sampling depth and / or sampling point position of the blind sampling task according to the equipment type and the height of the raw material loaded on the raw material transportation equipment; obtaining the particle size and fluidity information of the raw material according to the raw material type, and determining the sampling tube diameter used for blind sampling according to the particle size and fluidity information.

[0101] In one embodiment, the device further comprises:

[0102] A generating module, which is configured to generate a labeling instruction, wherein the labeling instruction is used to instruct the sampling machine to label the blind sampling result after blind sampling;

[0103] The task processing module 602 is specifically configured to create a blind sampling task for the raw material according to the blind sampling parameters and labeling instructions.

[0104] In one embodiment, the blind sampling test result includes the pricing result of the raw material; the device also includes:

[0105] A response module, configured to obtain the net weight of the raw material loaded on the raw material transportation equipment in response to a trigger operation of the transaction party agreeing with the pricing result;

[0106] A settlement determination module is configured to determine the raw material settlement amount based on the net weight of the raw material and the pricing result.

[0107] In one embodiment, the device further comprises:

[0108] An information acquisition module, configured to acquire transaction information of the transaction party;

[0109] The remittance module is configured to remit the raw material settlement amount to the transaction party terminal of the transaction party according to the transaction information.

[0110] The above-mentioned device provided in the embodiment of the present application can be used to implement the technical solution of the blind sampling method of raw materials in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0111] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the task processing module 602 can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above task processing module 602. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0112] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device may include: a transceiver 701 , a processor 702 , and a memory 703 .

[0113] The processor 702 executes the computer execution instructions stored in the memory, so that the processor 702 executes the raw material blind sampling method in the above embodiment. The processor 702 can be a general-purpose processor, including a central processing unit CPU, a network processor (NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0114] The memory 703 is connected to the processor 702 via a system bus and completes communication between them. The memory 703 is used to store computer program instructions.

[0115] The transceiver 701 may be used to exchange data with other devices or systems.

[0116] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The transceiver is used to realize the communication between the database access device and other computers (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory.

[0117] The electronic device provided in the embodiment of the present application may be the MES system of the above embodiment.

[0118] The above-mentioned electronic device provided in the embodiment of the present application can be used to implement the technical solution of the blind sampling method of raw materials in the above-mentioned embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0119] Figure 8 is a structural schematic diagram of a raw material blind sampling system provided in an embodiment of the present application, such as Figure 8 As shown, the system includes an MES system 801, a sampling machine 802, and an online detection system 803, wherein:

[0120] The sampling machine 802 is electrically connected to the MES system 801 and the online detection system 803, respectively, and is used to receive the blind sampling task issued by the MES system 801, and blindly sample the raw material according to the blind sampling task to obtain a blind sampling result, and transmit the blind sampling result to the online detection system 803;

[0121] The online detection system 803 is electrically connected to the sampling machine 802 and the MES system 801, respectively, and is used to check the blind sampling result, obtain the blind sampling detection result, and transmit the blind sampling detection result to the MES system 801;

[0122] The MES system is used to execute the blind sampling method of raw materials provided in any one of the first aspects above.

[0123] The above system provided in the embodiment of the present application includes the following features: automation and intelligence, 2. blind sample management, data recording and traceability, diversity and adaptability. Specifically, automation and intelligence: one of the key points of the above system is its level of automation and intelligence. The system can automatically identify the sampling area, automatically select the sampling point, and complete the sampling process by mechanical or electronic means, reducing manual intervention and improving the accuracy and efficiency of sampling; blind sample management: in the blind sampling system, blind sample management is a key link. The system should ensure that the samples taken remain anonymous during the subsequent testing process, that is, the testers do not know the source, batch and other specific information of the samples, so as to avoid the influence of human factors on the test results and ensure the fairness and accuracy of the test results; Data recording and traceability: The system should have complete data recording and traceability functions, which can record the time, location, quantity, operator and other information of each sampling, and can associate this information with the test results for traceability and review when necessary; Diversity and adaptability: The blind sampling system should be able to adapt to the sampling needs of materials of different types and properties, such as grain, minerals, chemical raw materials, etc., and can adjust the sampling strategies and parameters according to the characteristics of the materials.

[0124] It should be noted that the above-mentioned system provided in the embodiment of the present application can be used to implement the technical solution of the blind sampling method of raw materials in the above-mentioned embodiment. The unexplained parts can be understood by referring to the above-mentioned method embodiment and will not be repeated here.

[0125] The present application also provides a chip for executing instructions, which is used to execute the technical solution of the raw material blind sampling method in the above embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0126] The present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the technical solution of the raw material blind sampling method of the above embodiment. The implementation principle and technical effect are similar and will not be repeated here.

[0127] The present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, the technical solution of the raw material blind sampling method in the above embodiment can be implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0128] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0129] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0130] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above-mentioned module-composed unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0131] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0132] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0133] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0134] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0135] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0136] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic control unit or a main control device.

[0137] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for blind sampling of raw materials, characterized in that: Applied to generating an execution system MES, the method comprises: After detecting the raw material transportation equipment of the transaction party, identifying the equipment type of the raw material transportation equipment and the raw material type loaded in the raw material transportation equipment; Creating a blind sampling task for the raw material according to the equipment type and the raw material type, and transmitting the blind sampling task to a preset sampling machine; Obtaining a blind sampling test result fed back by an online testing system, wherein the blind sampling test result is a result obtained by the online testing system after receiving the blind sampling result obtained by the sampling machine for blind sampling the raw material according to the blind sampling task and then inspecting the blind sampling result; The blind sampling test results are displayed.

2. The method according to claim 1, characterized in that Also includes: Acquire image data about the raw material transportation equipment, and identify the raw material transportation equipment and the raw materials loaded therein in the image data through a target detection algorithm; The identifying the equipment type of the raw material transport equipment and the type of raw materials transported in the equipment includes: Comparing the raw material transport equipment and the raw material in the image data with a plurality of pre-stored raw material transport equipment and a plurality of raw materials, respectively, to obtain a comparison result; According to the comparison result, the equipment type and the raw material type are identified.

3. The method according to claim 1 or 2, characterized in that: The step of creating a blind sampling task for the raw material according to the equipment type and the raw material type comprises: Determining blind sampling parameters for blind sampling of the raw material according to the equipment type and the raw material type; A blind sampling task for the raw material is created according to the blind sampling parameters.

4. The method according to claim 3, characterized in that The blind sampling task includes at least one of the following blind sampling parameters: sampling depth, sampling point location, diameter and length of the sampling tube used for blind sampling; Determining blind sampling parameters for blind sampling of the raw material according to the equipment type and the raw material type includes at least one of the following: Determine the sampling depth and / or sampling point position of the blind sampling task according to the equipment type and the height of the raw material transport equipment loaded with raw materials; According to the type of the raw material, the particle size and fluidity information of the raw material are obtained, and the diameter of the sampling tube used for blind sampling is determined according to the particle size and fluidity information.

5. The method according to claim 3, characterized in that: Also includes: Generate a labeling instruction, wherein the labeling instruction is used to instruct the sampling machine to label the blind sampling result after blind sampling; The step of creating a blind sampling task for the raw material according to the blind sampling parameters comprises: A blind sampling task for the raw material is created according to the blind sampling parameters and labeling instructions.

6. The method according to claim 1 or 2, characterized in that: The blind sampling test result includes the pricing result of the raw material; after displaying the blind sampling test result, it also includes: In response to a triggering operation of the transaction party agreeing with the pricing result, obtaining the net weight of the raw material loaded on the raw material transportation equipment; The raw material settlement amount is determined based on the net weight of the raw materials and the pricing result.

7. The method according to claim 6, characterized in that Also includes: Obtaining transaction information of the transaction party; According to the transaction information, the raw material settlement amount is remitted to the transaction party terminal of the transaction party.

8. A raw material blind sampling device, characterized in that: Applying the MES system, the device includes: A raw material identification module, which is configured to identify the equipment type of the raw material transportation equipment and the raw material type loaded in the raw material transportation equipment after detecting the raw material transportation equipment of the transaction party; A task processing module, which is configured to create a blind sampling task for the raw material according to the equipment type and the raw material type, and transmit the blind sampling task to a preset sampling machine; A result acquisition module, which is configured to acquire a blind sampling test result fed back by an online detection system, wherein the blind sampling test result is a result obtained by the online detection system after receiving the blind sampling result obtained by the sampling machine for blind sampling the raw material according to the blind sampling task and then inspecting the blind sampling result; A result display module is configured to display the blind sampling detection result.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the blind sampling method for raw materials as described in any one of claims 1-7.

10. A raw material blind sampling system, characterized in that: Including MES system, sampling machine and online detection system, among which, The sampling machine is electrically connected to the MES system and the online detection system, respectively, and is used to receive the blind sampling task issued by the MES system, and blindly sample the raw material according to the blind sampling task to obtain a blind sampling result, and transmit the blind sampling result to the online detection system; The online detection system is electrically connected to the sampling machine and the MES system respectively, and is used to check the blind sampling result, obtain the blind sampling detection result, and transmit the blind sampling detection result to the MES system; The MES system is used to execute the blind sampling method of raw materials as described in any one of claims 1-7.

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