Belt weigher metering joint control method, system and equipment and storage medium

Abstract

The invention discloses a belt weigher metering joint control method, system and device and a storage medium, and the method comprises the steps: receiving an operation task in real time, and starting an operation process matched with the operation task; determining an operation target quantity according to the received operation task; determining an operation target advance according to the received operation task and the operation process matched with the operation task; after the operation process is started, correspondingly controlling loading and unloading equipment at a starting end to execute loading and unloading operation according to the operation process, starting a belt and a bifurcated bucket, and metering by a belt weigher at the starting end and a belt weigher at a stopping end; monitoring whether the metering of the belt scale at the starting end reaches the operation target lead or not in real time, and if the metering reaches the operation target lead, generating a signal instruction for stopping the loading and unloading operation; or monitoring whether the metering of the stopping end belt weigher reaches the operation target lead or not in real time, and if the metering of the stopping end belt weigher reaches the operation target lead, generating a signal instruction for stopping feeding of the bifurcated hopper. According to the invention, efficient and accurate loading and unloading operation can be realized in process operation.

Description

Technical Field

[0001] The present application relates to the technical field of belt scale optimization control, and in particular to a belt scale metering joint control method, system, equipment and storage medium. Background Art

[0002] In bulk cargo handling operations, such as cargo handling at ports, highly process-oriented operations have become a key technical means to improve loading and unloading efficiency and reduce costs. By rationally arranging the operation process, the continuity and stability of loading and unloading operations have been improved. However, due to the relatively simple traditional operation process management method, it is difficult to accurately control material supply and operation processes. Therefore, in the process of automated loading and unloading of bulk cargo, how to improve the accuracy and stability of operations has always been a technical difficulty.

[0003] Common methods currently include manually adjusting the material supply and performing manual intervention according to actual conditions, as well as using fixed operating process parameters for automated control. Although the manual adjustment method can better adapt to changes in the work site, it requires manual operation, resulting in low operating efficiency and high labor costs. Automated control using fixed parameters reduces manual intervention to a certain extent, but due to the complexity and variability of the working environment, fixed parameters are difficult to adapt to the needs of different scenarios, so they are greatly limited in practical applications.

[0004] Although the above common methods can meet the basic needs of bulk cargo loading and unloading operations to a certain extent, there are problems such as multiple interruptions of material supply or multiple starts and stops of the operation process when achieving accurate control of the operation target, which makes it more difficult to implement the unmanned operation mode and also increases the energy consumption of the operation, thus affecting the overall operation efficiency. Therefore, how to achieve efficient and accurate material supply control in process-based operations is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to realize efficient and accurate loading and unloading operations in process operations, the present application provides a belt scale metering joint control method, system, equipment and storage medium.

[0006] In a first aspect, the present application provides a belt scale measurement joint control method, comprising: Receive work tasks in real time and start work processes that match the work tasks; different work tasks are pre-set with work processes that match them; Determine the target amount of work according to the received work tasks; Determining the advance amount of the operation target according to the received operation task and the operation process matching the operation task, including: using a deep learning algorithm to construct a first neural network, inputting the received operation task and the operation process matching the operation task into the first neural network, and obtaining the advance amount of the operation target; the first neural network is trained and generated by historical operation tasks, operation processes matching the historical operation tasks, and expert recommended advance amounts of the operation targets under historical corresponding conditions; After the operation process is started, the loading and unloading equipment at the departure end is controlled to perform loading and unloading operations, the belt and fork bucket are started, and the belt scale at the departure end and the belt scale at the stop end are measured according to the operation process; Real-time monitoring of the measurement of the belt scale at the departure end to see whether it has reached the operating target in advance. If so, a signal command to stop loading and unloading operations is generated to control the loading and unloading equipment at the departure end to stop loading and unloading operations; or real-time monitoring of the measurement of the belt scale at the stop end to see whether it has reached the operating target in advance. If so, a signal command to stop feeding the fork bucket is generated to control the fork bucket to stop feeding.

[0007] By adopting the above scheme, a deep learning algorithm is used to dynamically obtain the operation target advance amount that matches the operation task. When the measurement at the departure end reaches the operation target advance amount, the loading and unloading equipment at the departure end stops the operation in time, or when the measurement at the stop end reaches the operation target advance amount, the fork bucket stops the operation in time, reducing the situation of multiple start and stop operation processes, achieving precise control of the operation process, and ensuring a high match between the material weight and the target quantity.

[0008] Preferably, the operation flow is divided into a first classification operation flow in which only a main flow participates in the operation, and a second classification operation flow in which a sub-flow participates in the operation; When the matching operation process is the first classification operation process, directly compare whether the measurement of the conveyor belt scale at the departure end reaches the operation target advance amount. If the operation target advance amount is reached, a signal instruction to stop the loading and unloading operation is generated to control the loading and unloading equipment at the departure end to stop the loading and unloading operation; When the matched operation process is the second classification operation process, determine whether the measurement of the belt scale at the stop end corresponding to each sub-process of the second classification operation process has reached the target advance amount corresponding to each sub-process. When the measurement of the belt scale at the stop end corresponding to each sub-process has reached the operation target advance amount, a signal instruction is generated for the fork bucket to stop feeding to the sub-process to control the fork bucket to stop feeding to the corresponding material transmission path of the sub-process.

[0009] By adopting the above scheme, the first category operation process and the second category operation process are intelligently distinguished according to different operation tasks. For the first category operation process, a direct comparison is made as to whether the operation target advance amount is achieved to decide whether to stop the loading and unloading equipment at the starting end, thereby ensuring precise control of the target quantity to be completed in one go. For the second category operation process, the measurement of the belt scale at the stopping end corresponding to each sub-process is monitored to see whether it reaches the target advance amount corresponding to each sub-process, thereby effectively realizing synchronous and precise operation control of multiple processes.

[0010] Preferably, it also includes: Receive the working environment in real time; the working environment includes temperature, humidity and vibration conditions; The method of determining the advance amount of the operation target based on the received operation task and the operation process matching the operation task also includes: using a deep learning algorithm to construct a second neural network, inputting the received operation task and the operation process matching the operation task into the second neural network instead of inputting the first neural network to obtain the advance amount of the operation target; the second neural network is trained and generated through historical operation tasks, operation processes matching historical operation tasks, historical operation environments and expert-recommended operation target advance amounts under historical corresponding conditions.

[0011] By adopting the above solution, the impact of different working environments on material transmission is taken into consideration, and the working environment, working tasks and the working processes corresponding to the working tasks are comprehensively considered to dynamically adjust the working target advance amount, thereby improving the accuracy and adaptability of the working process and ensuring precise control in various working environments.

[0012] Preferably, it also includes: Real-time monitoring of the belt scale measurement at the shutdown end. If it is detected that the measurement value of the belt scale at the shutdown end is lower than the minimum measurement value within the first preset time period or the change amount is continuously lower than the preset change amount within the second preset time period, the process is deemed to be empty and a process stop instruction is generated.

[0013] By adopting the above solution, the possibility of process emptying is taken into consideration, and the process emptying situation is judged in time according to the measurement results and the operation is interrupted in time to avoid invalid operation and reduce energy consumption.

[0014] Preferably, it also includes: Real-time monitoring of the operating data of loading and unloading equipment, belts, and fork buckets at the departure end; A third neural network is constructed by using a deep learning algorithm, and the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end are input into the third neural network in real time, and a prediction result of whether the belt scale measurement is abnormal or not is output; the third neural network is trained and generated by annotating the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end with the history of abnormal belt scale measurement; When the prediction result of whether the belt scale measurement is abnormal or not is that there is an abnormal risk, a process stop instruction is generated, and a belt scale measurement abnormality prompt information is generated at the same time.

[0015] By adopting the above scheme, comprehensive consideration is given to the operating conditions of the operating equipment and the measurement values ​​of the belt scale to predict possible abnormal measurement of the belt scale and stop related operations in advance, reducing operational losses caused by abnormal measurement and improving overall operating efficiency.

[0016] Preferably, it also includes: After determining that the measurement of the conveyor belt scale at the departure end reaches the operation target advance amount through real-time monitoring, before generating a signal instruction to stop the loading and unloading operation, continue to determine whether the difference between the material weight between the conveyor belt loading end and the conveyor belt scale at the departure end and the difference between the operation target amount and the operation advance amount is not greater than a preset difference. If it is not greater than the preset difference, generate a signal instruction to stop the loading and unloading operation; wherein, the step of obtaining the material weight between the conveyor belt loading end and the conveyor belt scale at the departure end includes: The weight of the first material is obtained by using a sensor installed between the starting loading end and the starting end belt scale; an image between the starting loading end and the starting end belt scale is collected by using a camera device, the material type and material size are identified by using image recognition technology, and the weight of the second material is calculated according to the material type and material size; Compare whether the difference between the weight of the first material and the weight of the second material is greater than the second preset difference. If so, re-adopt the spare sensor installed between the belt scale from the departure loading end to the departure end to obtain the weight of the first material or re-use the image recognition technology to calculate and obtain the weight of the second material. Re-compare whether the difference between the weight of the first material and the weight of the second material is greater than the second preset difference. Repeat the steps of re-obtaining and comparing the weight of the first material and the weight of the second material until the difference between the weight of the first material and the weight of the second material is not greater than the second preset difference. Perform weighted calculation based on the weight of the first material and the weight of the second material to obtain the material weight between the belt scale from the departure loading end to the departure end of the belt.

[0017] By adopting the above solution, the accuracy and reliability of material weight measurement are ensured, the inaccurate measurement caused by single sensor failure or measurement error is avoided, and the accuracy and reliability of the overall operation process are improved.

[0018] Preferably, it also includes: Design a visual interactive interface setting to display the measurement values ​​of the belt scale at the starting end and the belt scale at the stopping end during the operation process in real time.

[0019] By adopting the above solution, operators can monitor the progress of the operation process in real time, ensure timely understanding of the measurement status of the belt scale, and thus improve operation efficiency and accuracy.

[0020] In a second aspect, the present application provides a belt scale metering joint control system, comprising: The operation process acquisition module is used to receive the operation tasks in real time and start the operation process matching the operation tasks; wherein different operation tasks are pre-set with the matching operation processes; The operation target quantity acquisition module is used to determine the operation target quantity according to the received operation task; The operation target lead time acquisition module is used to determine the operation target lead time according to the received operation task and the operation process matching the operation task, including: using a deep learning algorithm to construct a first neural network, inputting the received operation task and the operation process matching the operation task into the first neural network, and obtaining the operation target lead time; the first neural network is trained and generated by historical operation tasks, operation processes matching the historical operation tasks, and expert recommended operation target lead times under historical corresponding conditions; The operation operation module is used to control the loading and unloading equipment at the departure end to perform loading and unloading operations, start the belt and fork bucket, and measure the belt scale at the departure end and the belt scale at the stop end according to the operation process after the operation process is started; The operation monitoring module is used to monitor in real time whether the measurement of the belt scale at the departure end reaches the operation target ahead of time. If the operation target is reached ahead of time, a signal instruction to stop the loading and unloading operation is generated to control the loading and unloading equipment at the departure end to stop the loading and unloading operation; or to monitor in real time whether the measurement of the belt scale at the stop end reaches the operation target ahead of time. If the operation target is reached ahead of time, a signal instruction to stop feeding the fork bucket is generated to control the fork bucket to stop feeding.

[0021] By adopting the above solution, efficient and accurate loading and unloading operations can be achieved in process operations.

[0022] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method as described above.

[0023] In a fourth aspect, the present application provides a computer device, comprising a memory, a processor, and a program stored and executable on the memory, wherein the program implements the steps of the above method when executed by the processor.

[0024] In summary, this application has the following beneficial effects: 1. By receiving the work tasks in real time and starting the matching work processes, the deep learning algorithm is used to more accurately determine the work target lead time, so that the loading and unloading equipment at the departure end automatically stops loading materials when the target lead time is reached, ensuring one-time accurate loading and unloading; 2. Use deep learning algorithms to build neural networks, automatically adjust the operation target lead time according to different operation environments, operation tasks and operation processes, and significantly improve the accuracy and stability of the operation; 3. After the operation process is started, the measurement value of the belt scale is monitored in real time and the loading and unloading equipment at the departure end is automatically controlled to stop operation according to the preset conditions, which significantly improves the operation efficiency and reduces the operation energy consumption and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a belt scale measurement joint control method described in a specific embodiment; Figure 2 It is a flow chart of different specific operation tasks in a belt scale measurement joint control method described in a specific embodiment; Figure 3 It is a structural schematic diagram of a belt scale metering and joint control system described in a specific embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] like Figure 1 As shown, the embodiment of the present application discloses a belt scale measurement joint control method, and the specific steps are as follows: S1. Receive job tasks in real time and start the job process that matches the job tasks.

[0028] Specifically, taking the cargo loading and unloading operation at a port as an example, different operation tasks are set according to the loading and unloading requirements, and matching operation processes are also pre-set for different operation tasks and stored in the database accordingly.

[0029] Among them, the operation tasks include: operation objects, that is, the specific type of loading and unloading goods, operation duration, operation target quantity and other operation requirements. The operation process can be drawn and matched manually according to the operation tasks, and can also be divided according to whether the operation tasks need to be diverted and processed. The main divisions include: the first category of operation processes in which only the main process participates in the operation, and the second category of operation processes in which sub-processes participate in the operation. For example, if the ratio of the operation target quantity to the operation duration is greater than the preset ratio value, then multiple processes need to be carried out simultaneously for loading and unloading operations. Otherwise, the operation can be completed directly with one main process.

[0030] Receive the job tasks uploaded by users in real time, match the job tasks with the job processes that match them, and automatically start the corresponding job processes.

[0031] S2. Determine the operation target quantity according to the received operation task.

[0032] Based on the received job task query, the job target quantity in the job task is obtained. The specific query method can use the unique identifier pre-set for the job target quantity to traverse the query in the job task.

[0033] S3. Determine the advance amount of the operation target according to the received operation task and the operation process matching the operation task.

[0034] Specifically, considering that when the loading and unloading equipment or the fork bucket at the departure end stops, the goods or materials already remaining on the belt have not yet been measured by the belt scale at the end, in order to more accurately achieve precise loading and unloading, a target advance amount will be set. When this target advance amount is reached, the operation of the loading and unloading equipment or the fork bucket at the departure end will be stopped in time, and the operation target advance amount is determined in advance.

[0035] Taking into account that the target lead time can be calculated by users or experts based on experience or by analyzing belt speed, belt width, cargo or material density, and time, in order to obtain the corresponding target lead time according to different operations, a first neural network is constructed using a deep learning algorithm. The input of the first neural network model is the operation task and the operation process matching the operation task, and the output is the operation target lead time; specifically, it is generated through training of historical operation tasks, operation processes matching historical operation tasks, and expert-recommended operation target lead times under historical corresponding conditions; the received operation tasks and the operation processes matching the operation tasks are input into the first neural network to obtain the operation target lead time under the corresponding conditions.

[0036] Among them, since the work process matched by the historical work task also has a second classification work process, there is an advance amount of work target corresponding to each sub-process set by experts under the historical corresponding conditions for this classification work process. Therefore, the input is the work task, and the one matched with the work task is the second classification work process, and the output is the advance amount of work target corresponding to each sub-process.

[0037] S4. After the operation process is started, the operation is performed according to the operation process.

[0038] Specifically, the loading and unloading equipment at the departure end is controlled to perform loading and unloading operations according to the operating process, that is, the loading and unloading equipment at the departure end starts to load the process, and the loading and unloading operations officially begin; while performing the loading and unloading operations, the start belt and the fork bucket are controlled to start according to the operating process, that is, the belt is started, and if there is a sub-process, the fork bucket is started accordingly; while starting the belt according to the operating process, the belt scale at the departure end and the belt scale at the stop end are started synchronously to measure the weight of the goods or materials.

[0039] S5. When performing operations according to the operation process, the measurement values ​​of the belt scale at the starting end or the belt scale at the stopping end are monitored in real time, and the operation or stop of the operation is controlled based on the detection results.

[0040] During the operation according to the operation process, real-time monitoring is performed on the belt scale at the departure end to see whether it has reached the operation target in advance. If so, a signal command to stop loading and unloading operations is generated to control the loading and unloading equipment at the departure end to stop loading and unloading operations; or real-time monitoring is performed on the belt scale at the stop end to see whether it has reached the operation target in advance. If so, a signal command to stop feeding the fork bucket is generated to control the fork bucket to stop feeding.

[0041] Specifically, Figure 2 As shown, when the matching operation process is the first category operation process, due to the simplicity of the process, it is possible to directly compare whether the measurement of the belt scale at the departure end has reached the operation target in advance. If the operation target is reached in advance, a signal instruction to stop the loading and unloading operation is generated to control the loading and unloading equipment at the departure end to stop the loading and unloading operation, thereby ensuring that the loading and unloading is completed in one go.

[0042] like Figure 2 As shown, when the matched operation process is the second classification operation process, due to the complexity of the process, measurement is only performed from the start of the belt transportation. After the diversion, there may be loss of goods due to the diversion. It is more accurate to confirm whether the advance amount of the operation target is achieved by the belt scale measurement at the stop end corresponding to each sub-process of the final sub-process. Therefore, it is judged whether the measurement of the belt scale at the stop end corresponding to each sub-process of the second classification operation process has achieved the target advance amount corresponding to each sub-process. When the measurement of the belt scale at the stop end corresponding to each sub-process has achieved the operation target advance amount, a signal instruction is generated for the fork bucket to stop feeding to the sub-process to control the fork bucket to stop feeding to the corresponding material transmission path of the sub-process.

[0043] In a specific embodiment, considering that different working environments may affect the execution of working tasks, such as: different temperatures and humidity may affect the measurement of goods or materials on the belt, thereby causing the actual weight to be different from the required weight, or the belt may have problems with the age of the belt, and different belt transmission vibrations or other environmental vibrations may affect the measurement of goods or materials on the belt by the belt scale. Therefore, in order to further achieve accurate loading and unloading, the method further includes: The working environment is received in real time; the working environment includes temperature, humidity, vibration, belt service life, etc. Specifically, a vibration sensor installed on the belt can be used to collect working environment data of the current operation.

[0044] The step of determining the operation target advance amount according to the received operation task and the operation process matching the operation task also includes: A second neural network is constructed using a deep learning algorithm. The input of the second neural network is the work environment, work tasks, and work processes matching the work tasks. The output is the work target lead time. The second neural network is trained and generated through historical work tasks, work processes matching historical work tasks, historical work environments, and expert-recommended work target lead times under historical corresponding conditions.

[0045] The received operation task and the operation process matching the operation task are input into the second neural network to replace the first neural network, so as to obtain the advance amount of the operation target.

[0046] In addition, in order to ensure accuracy and reduce the amount of calculation, it can be set that when there is a sudden change in environmental data between the working environment received in real time and the working environment received at the previous moment, that is, there is a difference between at least one environmental data that is greater than the preset difference of the corresponding environmental data or the similarity of at least one environmental data is less than the preset similarity of the corresponding environmental data, such as: the temperature difference is greater than the preset temperature difference, and the vibration signal similarity is less than the preset vibration signal similarity; then the received work task and the work process matching the work task are input into the second neural network to replace the first neural network, otherwise, the first neural network continues to be used to obtain the advance amount of the work target.

[0047] In a specific embodiment, considering that there may be a situation where the process is empty due to reasons such as untimely transfer of goods or materials or belt failure, in order to avoid invalid operations, the current operation is interrupted in time to improve the overall efficiency; the method also includes: Real-time monitoring of the measurement of the belt scale at the shutdown end; if it is detected that the measurement value of the belt scale at the shutdown end is lower than the minimum measurement value within the first preset time period, it means that no goods or materials are currently detected, or the change amount is continuously lower than the preset change amount within the second preset time period, indicating that the weight of any goods or materials currently detected has not changed, then it is determined that the process is empty, and a process stop instruction is generated, and the operation ends.

[0048] Specifically, Figure 2 As shown, when the matched operation process is the first classification operation process, when the belt scale at the shutdown end detects that the process is empty, a process stop instruction is automatically generated, the main process automatically stops running, and the operation ends.

[0049] When the matched operation process is the second classification operation process, when the belt scale at the stop end corresponding to the sub-process detects that the process is empty, a process stop instruction for the corresponding sub-process is automatically generated, the sub-process automatically stops running, and the operation of the operation line ends.

[0050] In a specific embodiment, considering that an operating failure of an operating device or a failure of a measuring device will cause abnormal measurement of a belt scale, in order to achieve accurate loading and unloading operations, predict abnormal conditions and stop operations in time to avoid invalid operations, the method includes: Real-time monitoring of the operating data of loading and unloading equipment, belts, and fork buckets at the departure end; In order to comprehensively predict abnormal situations, a third neural network is constructed using a deep learning algorithm. The input of the third neural network is the operation task, operation process, operation data of the loading and unloading equipment, belt, and fork bucket at the departure end, the measurement value of the belt scale at the departure end, and the measurement value of the belt scale at the stop end. The output is whether there is an abnormality in the belt scale measurement. Specifically, the third neural network is generated by training the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end that have marked the history of abnormal belt scale measurement; The operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the start-up end, and measurement values ​​of the belt scale at the stop-operation end are input into the third neural network in real time, and the prediction result of whether the belt scale measurement is abnormal or not is output; When the prediction result of whether the belt scale measurement is abnormal or not is that there is an abnormal risk, a process stop instruction is generated, and a belt scale measurement abnormality prompt information is generated at the same time.

[0051] In a specific embodiment, in order to further achieve efficient and accurate loading and unloading operations, it is determined that the weight of the material between the starting loading end of the belt and the starting end belt scale can compensate for the difference between the target operation volume and the target advance volume before stopping the loading and unloading; the method also includes: After determining that the real-time monitoring shows that the measurement of the belt scale at the departure end has reached the operation target ahead of time, before generating a signal instruction to stop the loading and unloading operation, continue to determine whether the difference between the material weight between the departure loading end of the belt and the belt scale at the departure end and the difference between the operation target quantity and the operation lead time is not greater than the preset difference. If it is not greater than the preset difference, generate a signal instruction to stop the loading and unloading operation; the preset difference can be set manually.

[0052] The step of obtaining the weight of the material between the starting loading end of the belt and the starting end belt scale includes: The weight of the first material is obtained by using a sensor installed between the starting loading end and the starting end belt scale; an image between the starting loading end and the starting end belt scale is collected by using a camera device, the material type and material size are identified by using image recognition technology, the material density is obtained according to the material type, and the weight of the second material is calculated according to the material density and the material size; Compare whether the difference between the weight of the first material and the weight of the second material is greater than the second preset difference. If so, re-adopt the spare sensor installed between the conveyor belt scale from the departure loading end to the departure end to obtain the weight of the first material or re-use the image recognition technology (after incremental training optimization) to calculate and obtain the weight of the second material, and then re-compare whether the difference between the weight of the first material and the weight of the second material is greater than the second preset difference. Repeat the steps of re-obtaining and comparing the weight of the first material and the weight of the second material until the difference between the weight of the first material and the weight of the second material is not greater than the second preset difference. The weight of the material between the starting loading end of the belt and the starting end belt scale is obtained by performing a weighted calculation based on the weight of the first material and the weight of the second material; wherein the specific weight can be dynamically updated according to the operating environment. When the operating environment data is within the preset environmental data range, the corresponding weights of different proportions are matched, and each preset environmental data range corresponds to a matching preset proportion of weights; for example: the first preset environmental data range, including: a first temperature range, a first humidity range and a first vibration signal amplitude range, corresponds to a matching first proportion weight, i.e. 1:1.

[0053] In a specific embodiment, in order to facilitate users to view measurement values ​​and prompt information in real time, and to facilitate analysis and adjustment, the method includes: Design a visual interactive interface setting to display the measurement values ​​of the belt scale at the starting end and the belt scale at the stopping end during the operation process in real time.

[0054] In addition, the method also includes: receiving a target advance setting value uploaded by a user on a visual interactive interface, comparing the target advance setting value submitted by the current user with the acquired target advance, and if the difference between the two is less than a third preset difference, generating confirmation information to determine the target advance setting value, and modifying the target advance to the target advance setting value when receiving the confirmation information.

[0055] like Figure 3 As shown, the embodiment of the present application discloses a belt scale metering joint control system, including: The operation process acquisition module 101 is used to receive the operation task in real time and start the operation process matching the operation task; wherein different operation tasks are pre-set with the operation process matching them; The operation target quantity acquisition module 102 is used to determine the operation target quantity according to the received operation task; The operation target lead time acquisition module 103 is used to determine the operation target lead time according to the received operation task and the operation process matching the operation task, including: using a deep learning algorithm to construct a first neural network, inputting the received operation task and the operation process matching the operation task into the first neural network, and obtaining the operation target lead time; the first neural network is trained and generated by historical operation tasks, the operation process matching the historical operation tasks, and the expert recommended operation target lead time under historical corresponding conditions; The operation operation module 104 is used to control the loading and unloading equipment at the departure end to perform loading and unloading operations, start the belt and fork bucket, and measure the belt scale at the departure end and the belt scale at the stop end according to the operation process after the operation process is started; The operation monitoring module 105 is used to monitor in real time whether the measurement of the belt scale at the departure end reaches the operation target ahead of time. If the operation target is reached ahead of time, a signal instruction to stop the loading and unloading operation is generated to control the loading and unloading equipment at the departure end to stop the loading and unloading operation; or to monitor in real time whether the measurement of the belt scale at the stop end reaches the operation target ahead of time. If the operation target is reached ahead of time, a signal instruction to stop feeding the fork bucket is generated to control the fork bucket to stop feeding.

[0056] In a specific embodiment, the system further includes: The operation display module 106 is used to design a visual interactive interface setting to display the measurement values ​​of the belt scale at the start end and the belt scale at the stop end during the operation process in real time; The operating environment acquisition module 107 is used to receive the operating environment in real time; the operating environment includes temperature, humidity and vibration conditions; The operation target advance amount acquisition module 103 is also used to construct a second neural network using a deep learning algorithm, and input the received operation task and the operation process matching the operation task into the second neural network instead of inputting the first neural network to obtain the operation target advance amount; the second neural network is generated by training the historical operation tasks, the operation process matching the historical operation tasks, the historical operation environment and the expert recommended operation target advance amount under the historical corresponding conditions.

[0057] In a specific embodiment, the operation monitoring module 105 is also used to monitor the measurement of the belt scale at the shutdown end in real time. If it is detected that the measurement value of the belt scale at the shutdown end is lower than the minimum measurement value within the first preset time period or the change amount is continuously lower than the preset change amount within the second preset time period, the process is deemed to be empty and a process stop instruction is generated.

[0058] In a specific embodiment, the operation monitoring module 105 is also used to monitor the operation data of the loading and unloading equipment, belts, and fork buckets at the departure end in real time; a third neural network is constructed using a deep learning algorithm, and the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end are input into the third neural network in real time, and a prediction result of whether the belt scale measurement is abnormal or not is output; the third neural network is trained and generated by annotating the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end in which the belt scale measurement has a history of abnormalities; when the prediction result of whether the belt scale measurement is abnormal or not is output as an abnormal risk, a process stop instruction is generated, and a belt scale measurement abnormality prompt information is generated at the same time. In a specific embodiment, the operation monitoring module 105 is also used to continue to determine whether the difference between the material weight and the operation target quantity and the operation advance quantity between the starting loading end of the belt and the starting end belt scale is not greater than a preset difference after determining that the measurement of the starting end belt scale reaches the operation target advance quantity through real-time monitoring, and before generating a signal instruction to stop the loading and unloading operation. If the difference is not greater than the preset difference, the signal instruction to stop the loading and unloading operation is generated.

[0059] The embodiment of the present application also discloses a computer-readable storage medium.

[0060] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned belt scale metering joint control method. The computer-readable storage medium includes, for example: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0061] The embodiment of the present application also discloses a computer device.

[0062] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned belt scale measurement joint control method.

[0063] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A belt scale metering joint control method, characterized in that: include: Receive work tasks in real time and start work processes that match the work tasks; different work tasks are pre-set with work processes that match them; Determine the target amount of work according to the received work tasks; Determining the advance amount of the operation target according to the received operation task and the operation process matching the operation task, including: using a deep learning algorithm to construct a first neural network, inputting the received operation task and the operation process matching the operation task into the first neural network, and obtaining the advance amount of the operation target; the first neural network is trained and generated by historical operation tasks, operation processes matching the historical operation tasks, and expert recommended advance amounts of the operation targets under historical corresponding conditions; After the operation process is started, the loading and unloading equipment at the departure end is controlled to perform loading and unloading operations, the belt and fork bucket are started, and the belt scale at the departure end and the belt scale at the stop end are measured according to the operation process; Real-time monitoring of the measurement of the belt scale at the departure end to see whether it has reached the operating target in advance. If so, a signal command to stop loading and unloading operations is generated to control the loading and unloading equipment at the departure end to stop loading and unloading operations; or real-time monitoring of the measurement of the belt scale at the stop end to see whether it has reached the operating target in advance. If so, a signal command to stop feeding the fork bucket is generated to control the fork bucket to stop feeding.

2. The belt scale metering and joint control method according to claim 1, characterized in that: The operation process is divided into a first-class operation process in which only the main process participates in the operation, and a second-class operation process in which the sub-process participates in the operation; When the matching operation process is the first classification operation process, directly compare whether the measurement of the conveyor belt scale at the departure end reaches the operation target advance amount. If the operation target advance amount is reached, a signal instruction to stop the loading and unloading operation is generated to control the loading and unloading equipment at the departure end to stop the loading and unloading operation; When the matched operation process is the second classification operation process, determine whether the measurement of the belt scale at the stop end corresponding to each sub-process of the second classification operation process has reached the target advance amount corresponding to each sub-process. When the measurement of the belt scale at the stop end corresponding to each sub-process has reached the operation target advance amount, a signal instruction is generated for the fork bucket to stop feeding to the sub-process to control the fork bucket to stop feeding to the corresponding material transmission path of the sub-process.

3. The belt scale metering and joint control method according to claim 1, characterized in that: Also includes: Receive the working environment in real time; the working environment includes temperature, humidity and vibration conditions; The method of determining the advance amount of the operation target based on the received operation task and the operation process matching the operation task also includes: using a deep learning algorithm to construct a second neural network, inputting the received operation task and the operation process matching the operation task into the second neural network instead of inputting the first neural network to obtain the advance amount of the operation target; the second neural network is trained and generated through historical operation tasks, operation processes matching historical operation tasks, historical operation environments and expert-recommended operation target advance amounts under historical corresponding conditions.

4. The belt scale metering and joint control method according to claim 1, characterized in that: Also includes: Real-time monitoring of the belt scale measurement at the shutdown end. If it is detected that the measurement value of the belt scale at the shutdown end is lower than the minimum measurement value within the first preset time period or the change amount is continuously lower than the preset change amount within the second preset time period, the process is deemed to be empty and a process stop instruction is generated.

5. The belt scale metering and joint control method according to claim 1, characterized in that: Also includes: Real-time monitoring of the operating data of loading and unloading equipment, belts, and fork buckets at the departure end; A third neural network is constructed by using a deep learning algorithm, and the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end are input into the third neural network in real time, and a prediction result of whether the belt scale measurement is abnormal or not is output; the third neural network is trained and generated by annotating the operation tasks, operation processes, operation data, measurement values ​​of the belt scale at the departure end, and measurement values ​​of the belt scale at the stop end with the history of abnormal belt scale measurement; When the prediction result of whether the belt scale measurement is abnormal or not is that there is an abnormal risk, a process stop instruction is generated, and a belt scale measurement abnormality prompt message is generated at the same time.

6. The belt scale metering and joint control method according to claim 1, characterized in that: Also includes: After determining that the measurement of the conveyor belt scale at the departure end reaches the operation target advance amount through real-time monitoring, before generating a signal instruction to stop the loading and unloading operation, continue to determine whether the difference between the material weight between the conveyor belt loading end and the conveyor belt scale at the departure end and the difference between the operation target amount and the operation advance amount is not greater than a preset difference. If it is not greater than the preset difference, generate a signal instruction to stop the loading and unloading operation; wherein, the step of obtaining the material weight between the conveyor belt loading end and the conveyor belt scale at the departure end includes: The weight of the first material is obtained by using a sensor installed between the starting loading end and the starting end belt scale; an image between the starting loading end and the starting end belt scale is collected by using a camera device, the material type and material size are identified by using image recognition technology, and the weight of the second material is calculated according to the material type and material size; Compare whether the difference between the weight of the first material and the weight of the second material is greater than the second preset difference. If so, re-adopt the spare sensor installed between the belt scale from the departure loading end to the departure end to obtain the weight of the first material or re-use the image recognition technology to calculate and obtain the weight of the second material. Re-compare whether the difference between the weight of the first material and the weight of the second material is greater than the second preset difference. Repeat the steps of re-obtaining and comparing the weight of the first material and the weight of the second material until the difference between the weight of the first material and the weight of the second material is not greater than the second preset difference. Perform weighted calculation based on the weight of the first material and the weight of the second material to obtain the material weight between the belt scale from the departure loading end to the departure end of the belt.

7. The belt scale metering and joint control method according to claim 1, characterized in that: Also includes: Design a visual interactive interface setting to display the measurement values ​​of the belt scale at the starting end and the belt scale at the stopping end during the operation process in real time.

8. A belt scale metering joint control system, characterized in that: include: The operation process acquisition module is used to receive the operation tasks in real time and start the operation process matching the operation tasks; wherein different operation tasks are pre-set with the matching operation processes; The operation target quantity acquisition module is used to determine the operation target quantity according to the received operation task; The operation target lead time acquisition module is used to determine the operation target lead time according to the received operation task and the operation process matching the operation task, including: using a deep learning algorithm to construct a first neural network, inputting the received operation task and the operation process matching the operation task into the first neural network, and obtaining the operation target lead time; the first neural network is trained and generated by historical operation tasks, operation processes matching the historical operation tasks, and expert recommended operation target lead times under historical corresponding conditions; The operation operation module is used to control the loading and unloading equipment at the departure end to perform loading and unloading operations, start the belt and fork bucket, and measure the belt scale at the departure end and the belt scale at the stop end according to the operation process after the operation process is started; The operation monitoring module is used to monitor in real time whether the measurement of the belt scale at the departure end reaches the operation target ahead of time. If the operation target is reached ahead of time, a signal instruction to stop the loading and unloading operation is generated to control the loading and unloading equipment at the departure end to stop the loading and unloading operation; or to monitor in real time whether the measurement of the belt scale at the stop end reaches the operation target ahead of time. If the operation target is reached ahead of time, a signal instruction to stop feeding the fork bucket is generated to control the fork bucket to stop feeding.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device comprises a memory, a processor and a program stored and executable on the memory, and the program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

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