Material movement balancing methods, apparatus, electronic equipment and storage media

Abstract

This application provides a material movement balancing method, apparatus, electronic device, and storage medium. By acquiring node model information of a node model, which consists of at least two processing nodes corresponding to physical nodes in the material production and supply process, the node model information characterizes the predicted material change at each physical node during the material flow between the processing nodes. Based on the node model information and a preset time period, node constraint information is obtained, characterizing the target material loss at each node model during the material flow between the processing nodes. Based on the node constraint information, a target balance arbitration value is obtained, which is used to determine whether the material loss between the physical nodes is balanced during the material flow between the processing nodes, thus solving the problem of low accuracy in material balance results.

Description

Technical Field

[0001] This application relates to the field of factory material management technology, and in particular to a material movement balancing method, apparatus, electronic device and storage medium. Background Technology

[0002] Material balancing is a crucial aspect of the daily operations management of refining and chemical enterprises. It helps companies maximize the utilization of raw material and energy resources. By conducting material balancing, companies can identify imbalances in production and supply, minimize waste such as storage and transportation losses, leaks, and metering losses, and ultimately improve production efficiency and energy utilization.

[0003] In actual business operations, the material balance problem in refining and chemical enterprises is solved through the experience of staff and trial-and-error methods.

[0004] However, material balance results determined through human experience and trial-and-error methods suffer from low accuracy. Summary of the Invention

[0005] This application provides a material movement balancing processing method, apparatus, electronic device, and storage medium to solve the problem of low accuracy of material balance results.

[0006] In a first aspect, this application provides a material movement balancing processing method, including: obtaining node model information of a node model, wherein the node model consists of at least two processing nodes, the processing nodes correspond to physical nodes in the material production and supply process, and the node model information characterizes the predicted material change amount corresponding to the physical nodes during the process of material flow between the physical nodes corresponding to the processing nodes.

[0007] Based on the node model information and the preset time period, the node constraint information is obtained. The node constraint information represents the target loss of the material corresponding to the node model during the process of material flowing between the physical nodes corresponding to the processing node.

[0008] Based on the node constraint information, the target balance arbitration value is obtained. The target balance arbitration value is used to determine whether the loss of material between physical nodes is in a balanced state during the process of material flow between the physical nodes corresponding to the processing node.

[0009] In one possible implementation, node constraint information is obtained based on node model information and a preset time period, including: obtaining a node constraint model, which is used to constrain the flow characteristics of materials during the flow of materials between physical nodes corresponding to the processing node; and obtaining node constraint information based on node model information, a preset time period, and the node constraint model.

[0010] In one possible implementation, the node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model. Obtaining the node constraint model includes: obtaining the first constraint model based on node balance quantity variables and node true value variables, where the node balance quantity variable is at least one flow quantity of material when flowing through the physical nodes corresponding to the processing node, and the node true value variable is the total flow quantity of material when flowing through the physical nodes corresponding to the processing node; obtaining the second constraint model based on node true value variables, node measured values, and node loss quantity variables, where the node loss quantity variable is the loss quantity of material when flowing through the physical nodes corresponding to the processing node; obtaining the third constraint model based on node model priority and target loss quantity, where the node model priority represents the allocation order when allocating target loss quantities to the physical nodes corresponding to the processing node; obtaining the fourth constraint model based on node measured values ​​and node model accuracy, where the node model accuracy is used to quantify the accuracy of the node measured values ​​corresponding to the processing node, and the fourth constraint model is used to determine the target loss proportion of the node loss quantity variable in the target loss quantity; and obtaining the fifth constraint model based on node absolute loss quantity variables and the target loss quantity, where the node absolute loss quantity variable is the absolute value of the node loss quantity variable.

[0011] In one possible implementation, the first constraint model includes an input constraint model, an output constraint model, and an input-output constraint model. The input constraint model corresponds to a first processing node with only an input stream, the output constraint model corresponds to a second processing node with only an output stream, and the input-output constraint model corresponds to a third processing node with both an input stream and an output stream.

[0012] In one possible implementation, the node model information includes node model accuracy and node model priority. The node model accuracy is used to quantify the accuracy of the node measurement values ​​of the material corresponding to the processing node, and the node model priority represents the allocation order when allocating material losses to the physical nodes corresponding to the processing node. The node model priority is determined based on the node model accuracy.

[0013] Secondly, this application provides a material movement balancing system, comprising:

[0014] The node definition unit is used to define the physical nodes in the material production and supply process to obtain the corresponding processing nodes;

[0015] The model definition unit is used to establish a node model corresponding to the material production and supply process based on the processing nodes.

[0016] The data acquisition unit is used to obtain node model information based on the node model. The node model information represents the predicted material change at the physical nodes corresponding to the processing nodes during the material flow between the physical nodes corresponding to the processing nodes.

[0017] The material movement balancing processing unit is used to obtain node constraint information based on node model information and a preset time period. The node constraint information represents the target loss of the material corresponding to the node model during the flow of material between the physical nodes corresponding to the processing node. Based on the node constraint information, the target balance arbitration value is obtained. The target balance arbitration value is used to determine whether the loss of material between physical nodes is in a balanced state during the flow of material between the physical nodes corresponding to the processing node.

[0018] Thirdly, this application provides a material movement balancing processing apparatus, comprising:

[0019] The acquisition module is used to acquire node model information of the node model. The node model consists of at least two processing nodes, which correspond to physical nodes in the material production and supply process. The node model information represents the predicted material change of the physical node during the process of material flow between the physical nodes corresponding to the processing nodes.

[0020] The processing module is used to obtain node constraint information based on node model information and preset time period. The node constraint information represents the target loss of the material corresponding to the node model during the flow of material between the physical nodes corresponding to the processing node.

[0021] The determination module is used to obtain the target balance arbitration value based on the node constraint information. The target balance arbitration value is used to determine whether the loss of material between physical nodes is in a balanced state during the process of material flow between the physical nodes corresponding to the processing node.

[0022] In one possible implementation, when the processing module obtains node constraint information based on node model information and a preset time period, it specifically performs the following steps: acquiring a node constraint model, which is used to constrain the flow characteristics of materials during the flow of materials between physical nodes corresponding to the processing node; and obtaining node constraint information based on the node model information, the preset time period, and the node constraint model.

[0023] In one possible implementation, the node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model. When acquiring the node constraint model, the processing module specifically performs the following steps: Based on the node balance quantity variable and the node true value variable, it obtains the first constraint model, where the node balance quantity variable is at least one flow amount of material at the physical node corresponding to the processing node, and the node true value variable is the total flow amount of material at the physical node corresponding to the processing node; based on the node true value variable, the node measured value, and the node loss variable, it obtains the second constraint model, where the node loss variable is the total flow amount of material at the physical node corresponding to the processing node. The loss amount during the flow of the physical node corresponding to the point; based on the node model priority and the target loss amount, the third constraint model is obtained, where the node model priority represents the allocation order when allocating the target loss amount to the physical node corresponding to the processing node; based on the node measurement value and the node model accuracy, the fourth constraint model is obtained, where the node model accuracy is used to quantify the accuracy of the node measurement value corresponding to the processing node, and the fourth constraint model is used to determine the target loss proportion of the node loss amount variable corresponding to the processing node in the target loss amount; based on the node absolute loss amount variable and the target loss amount, the fifth constraint model is obtained, where the node absolute loss amount variable is the absolute value of the node loss amount variable.

[0024] In one possible implementation, the first constraint model includes an input constraint model, an output constraint model, and an input-output constraint model. The input constraint model corresponds to a first processing node with only an input stream, the output constraint model corresponds to a second processing node with only an output stream, and the input-output constraint model corresponds to a third processing node with both an input stream and an output stream.

[0025] In one possible implementation, the node model information includes node model accuracy and node model priority. The node model accuracy is used to quantify the accuracy of the node measurement values ​​of the material corresponding to the processing node, and the node model priority represents the allocation order when allocating material losses to the physical nodes corresponding to the processing node. The acquisition module determines the node model priority based on the node model accuracy.

[0026] Fourthly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0027] The memory stores instructions that the computer executes;

[0028] The processor executes computer execution instructions stored in the memory to implement the material movement balancing processing method as described in any of the first aspects of the embodiments of this application.

[0029] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the material movement balancing processing method as described in any of the first aspects of the embodiments of this application.

[0030] According to a sixth aspect of the embodiments of this application, this application provides a computer program product, including a computer program that, when executed by a processor, implements the material movement balancing processing method as described in any of the first aspects above.

[0031] The material movement balancing method, apparatus, electronic device, and storage medium provided in this application acquire node model information of a node model, which consists of at least two processing nodes. Each processing node corresponds to a physical node in the material production and supply process. The node model information characterizes the predicted material change at each physical node during the material flow between the processing nodes. Based on the node model information and a preset time period, node constraint information is obtained. This node constraint information characterizes the target material loss corresponding to the node model during the material flow between the processing nodes. Based on the node constraint information, a target balance arbitration value is obtained. This target balance arbitration value is used to determine whether the material loss between the physical nodes is in a balanced state during the material flow between the processing nodes. By analyzing the material flow process between physical nodes in the material production and supply process and the node characteristics of each physical node, the predicted material change in a node model consisting of processing nodes corresponding to at least two physical nodes is determined. Then, within a preset time period, based on the predicted material change, the material measurement values ​​at the physical nodes corresponding to the node model, the material inflow, and the material outflow, the target material loss is determined. Furthermore, based on the node characteristics of each physical node, the target material loss is allocated to the corresponding physical node, obtaining the target balance arbitration value for that physical node, which is also the target balance arbitration value for the processing node. This allows for the determination of whether the material loss between physical nodes is in a balanced state during the material flow process between the processing nodes, thus obtaining the material balance result and solving the problem of low accuracy in material balance results. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0033] Figure 1 This is an application scenario diagram of the material movement balancing method provided in the embodiments of this application;

[0034] Figure 2A flowchart illustrating a material movement balancing method provided in one embodiment of this application;

[0035] Figure 3 A schematic diagram illustrating the flow of materials between physical nodes, provided in this application;

[0036] Figure 4 A schematic diagram illustrating the material quantity change corresponding to the flow of materials between physical nodes, provided in this application;

[0037] Figure 5 A flowchart of a material movement balancing method provided in another embodiment of this application;

[0038] Figure 6 This is a schematic diagram of a material movement balancing system provided in one embodiment of this application;

[0039] Figure 7 This is a schematic diagram of the structure of a material movement balancing processing device provided in one embodiment of this application;

[0040] Figure 8 A schematic diagram of an electronic device provided according to one embodiment of this application;

[0041] Figure 9 This is a block diagram illustrating a terminal device in an exemplary embodiment of this application.

[0042] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] The technical solution of this application involves the collection, storage, use, processing, transmission, provision and disclosure of user personal information and data, which comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0045] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0046] First, let me explain the terms used in this application:

[0047] Single receiving tank: If only one material output node inputs material into the material tank, then the material tank is defined as a single receiving tank.

[0048] Single-load tank: If only one material receiving node receives the material output from the material tank, then the material tank is defined as a single-load tank.

[0049] Receiving tank: If two or more material output nodes input material into a material tank, then the material tank is defined as a receiving tank.

[0050] Simultaneous receiving and discharging tank: refers to a material tank that can receive and discharge materials at the same time, enabling simultaneous receiving and discharging operations during material storage and transportation.

[0051] Material balance: refers to the comprehensive and systematic statistical control of material flow in the production process of an enterprise to achieve a balance between input and output materials. Based on the principles of continuity of material flow and conservation of mass, the total amount of materials in the entire production system should remain constant.

[0052] Balanced arbitration value: refers to the value at which the quantity of some materials is appropriately adjusted based on actual production conditions and statistical accounting rules, while ensuring the overall material balance.

[0053] The application scenarios of the embodiments of this application are explained below:

[0054] Figure 1 This diagram illustrates an application scenario of the material movement balancing method provided in this application. The material movement balancing method can be applied to scenarios where material losses are determined during the material production and supply process. For example,... Figure 1As shown, the execution subject of the method provided in this application embodiment can be a cloud server, an electronic device, or a terminal device. Taking an electronic device as the execution subject of the method in this embodiment, the physical nodes of the material in the material production and supply process include the inbound node, the outbound node, the material tank node, the sideline node, and the material handling device corresponding to the sideline node. The electronic device device_0 receives the node feature information and the material flow data corresponding to the material sent by the inbound node, the outbound node, the material tank node, and the sideline node according to the material flow direction in the material production and supply process. Then, the electronic device device_0 determines the loss of the material in the production and supply process according to the material flow data corresponding to each physical node. For example, it determines the loss amount loss_0 of the physical node according to the initial storage amount, material input amount, and material output amount of the material corresponding to the physical node. Then, the user determines the material balance result according to the loss amount loss_0 calculated by the electronic device device_0.

[0055] Because the determination of material balance results is based on the user's work experience and trial-and-error methods, for example, if the user determines based on work experience and trial-and-error methods that the loss amount loss_0 is greater than the loss amount corresponding to the material balance state, then the physical node is further determined to be in a material imbalance state; if the loss amount loss_0 is less than or equal to the loss amount corresponding to the material balance state, then the physical node is further determined to be in a material balance state. This leads to the problem of low accuracy of material balance results.

[0056] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0057] Figure 2 A flowchart of a material movement balancing method provided in one embodiment of this application is shown below. Figure 2 As shown, the execution subject of the material movement balancing processing method provided in this embodiment can be a cloud server, an electronic device, or a terminal device. For example, this embodiment uses an electronic device as the execution subject of the method. The material movement balancing processing method provided in this embodiment includes the following steps:

[0058] Step S101: Obtain the node model information of the node model. The node model consists of at least two processing nodes. The processing nodes correspond to physical nodes in the material production and supply process. The node model information represents the predicted material change of the physical nodes during the flow of materials between the physical nodes corresponding to the processing nodes.

[0059] For example, in the material production and supply process, the material is processed by at least two physical nodes. That is, the node model corresponding to the material consists of at least two processing nodes. The processing nodes correspond to the physical nodes in the material production and supply process. Then, based on the node characteristics corresponding to the physical nodes, the predicted material change of the physical nodes during the flow of the material between the physical nodes corresponding to the processing nodes can be determined, thus obtaining the node model information.

[0060] In one possible implementation, the physical nodes of materials in the material production and supply process include inbound nodes, outbound nodes, material tank nodes, sideline nodes, and mutual supply nodes. Inbound nodes indicate nodes with independent inbound loading / unloading and / or metering functions; outbound nodes indicate nodes with independent outbound loading / unloading and / or metering functions; material tank nodes indicate containers for storing gaseous or liquid materials, including vertical tanks, spherical tanks, and horizontal tanks, and also virtual pipelines, coking tanks, and long-distance pipelines; sideline nodes indicate material input pipelines and / or material output pipelines on material handling devices that process materials; mutual supply nodes indicate mutual supply operations corresponding to the materials, and these mutual supply nodes do not belong to a single material production and supply process but are shared by two upstream and downstream material production and supply processes. Further, the electronic equipment determines the corresponding physical nodes based on the material production and supply process in which the material is located, and then determines the processing nodes corresponding to the physical nodes. Further, the electronic equipment determines the corresponding node model and the node model information corresponding to the node model based on the determined processing nodes, thus obtaining the predicted material change when the material flows between physical nodes in that node model. For example, Figure 3 This application provides a schematic diagram of the flow of materials between physical nodes, such as... Figure 3 As shown, materials enter the material supply process from the inbound node in_1, are transported to the material tank node tank_1 via material transfer pipe_1 for storage, and then via material transfer pipe_2 and side line node line_1 from the material tank node tank_1 to the material handling device deal_1 for processing. After processing, the material is then via side line node line_2 and material transfer pipe_3 from the material handling device deal_1 to the material tank node tank_2 for storage. Next, the material is via material transfer pipe_4 from the material tank node tank_2 to the outbound node out_1. Finally, after processing, the material is via side line node line_3 and material transfer pipe_5 from the material handling device deal_1 to the material tank node tank_3 for storage. Electronic equipment is based on... Figure 3The material flow process between physical nodes is shown. A node model is established, consisting of the processing nodes corresponding to the in-plant node in_1, the material tank node tank_1, the side line node line_1, the side line node line_2, the material tank node tank_2, the side line node line_3, the material tank node tank_3, and the out-plant node out_1. Then, based on the node characteristics corresponding to the physical nodes, the predicted material change of the physical nodes is determined during the material flow between the physical nodes corresponding to the processing nodes, thus obtaining the node model information.

[0061] Furthermore, the node model information includes node model accuracy and node model priority. Node model accuracy is used to quantify the accuracy of the node measurement values ​​of the material corresponding to the processing node, and node model priority represents the allocation order when allocating material losses to the physical nodes corresponding to the processing node. The node model priority is determined based on the node model accuracy.

[0062] For example, node model accuracy is used to quantify the accuracy of node measurement values ​​of materials corresponding to nodes, and the range of node model accuracy is defined as 0-100, where 0 represents the lowest node model accuracy and 100 represents the highest node model accuracy.

[0063] For the inbound and outbound nodes, in refining and chemical enterprises, the metering data (node ​​measurement values) of the inbound and outbound nodes are generated through metering slip data. Since the metering slip is a legal document and cannot be adjusted, the node measurement values ​​are accurate values. Therefore, the accuracy of the node model corresponding to the inbound and outbound nodes is determined to be 100, that is, the accuracy of the node model of the inbound and outbound nodes is the highest accuracy. Furthermore, in the material balance process, the inbound and outbound nodes do not participate in the allocation of material loss (material imbalance). Therefore, the priority of the node model corresponding to the inbound and outbound nodes is determined to be the highest balance priority, level A.

[0064] For mutual supply nodes, the metering data (node ​​measurement values) of mutual supply nodes are calculated by arbitration using the metering arbitration formula, and no loss allocation is performed in actual business. Therefore, the node measurement values ​​are accurate values, and the accuracy of the node model corresponding to the mutual supply node is determined to be 100, that is, the accuracy of the node model of the mutual supply node is the highest accuracy. Furthermore, in the material balance process, mutual supply nodes do not participate in the allocation of material loss (material imbalance). Therefore, the priority of the node model corresponding to the mutual supply node is determined to be the highest balance priority, level A.

[0065] For the material tank node, the metering data (node ​​measurement value) of the material tank node is retrieved by volume through the tank capacity table, and the measurement data involved in the calculation (including liquid level, temperature, and density) can be measured. Therefore, the node measurement value is an accurate value, and the accuracy of the node model corresponding to the material tank node is determined to be 100, that is, the accuracy of the node model of the material tank node is the highest accuracy. Therefore, in the material balance process, when material movement occurs with physical nodes with low accuracy, it does not participate in the allocation of material loss (material imbalance). When material movement occurs with physical nodes with high accuracy, it participates in the allocation of material loss (material imbalance). Therefore, the priority of the node model corresponding to the material tank node is determined to be level C.

[0066] Material tanks include single receiving tanks, single dispatch tanks, receiving tanks, and receiving-while-dispatching tanks. Material tank nodes include single receiving tank nodes corresponding to single receiving tanks, single dispatch tank nodes corresponding to single dispatch tanks, receiving tank nodes corresponding to receiving tanks, and receiving-while-dispatching tank nodes corresponding to receiving-while-dispatching tanks. For single receiving tank nodes, during material balancing, when material movement occurs between a single receiving tank node and other material tank nodes, the node model priority corresponding to the single receiving tank node is determined to be level C1. For single dispatch tank nodes, during material balancing, when material movement occurs between a single dispatch tank node and other material tank nodes, the node model priority corresponding to the single dispatch tank node is determined to be level C2. For receiving tank nodes, during material balancing, when a material tank node that has material movement with a receiving tank node is neither a single receiving tank node nor a receiving tank node, the node model priority is determined to be level C2. When it is a single receiving tank node, it does not participate in the allocation of material loss (material imbalance), and the node model priority corresponding to the receiving tank node is determined to be level C3. For receiving and receiving tank nodes, during the material balancing process, when material movement occurs between the receiving and receiving tank node and other material tank nodes, the node model priority corresponding to the receiving and receiving tank node is determined to be level C4. Furthermore, the priority order is C1, C2, C3, C4, that is, when material tank nodes move with other material tank nodes, the receiving and receiving tank node corresponding to priority C4 participates in the allocation of material loss (material imbalance) first, followed by the receiving tank node corresponding to priority C3, then the single receiving tank node corresponding to priority C2, and finally the single receiving tank node corresponding to priority C1.

[0067] For sideline nodes, there are many types of sideline metering instruments, with varying accuracy and types, such as high-precision instruments, orifice flow meters, and volumetric flow meters. Therefore, the accuracy of the data from sideline nodes is uncertain, and the accuracy of the node model corresponding to a sideline node can be adjusted between 0 and 100. If the node measurement value of a sideline node is accurate, the accuracy of the node model corresponding to that sideline node is set to 100. When material movement occurs between a sideline node and other sideline nodes, it does not participate in the allocation of material loss (material imbalance). Therefore, the priority of the node model corresponding to the sideline node is set to level B. When material movement occurs between a sideline node and a material tank node, it does not participate in the allocation of material loss (material imbalance). Therefore, the priority of the node model corresponding to the sideline node is determined as level B; if the accuracy of the node model corresponding to the sideline node is any value between 0 and 100 (inclusive), when the sideline node moves materials with the incoming node, or moves materials with the outgoing node, or moves materials with the mutual supply node, the sideline node must participate in the allocation of material loss (material imbalance), and the priority of the node model corresponding to the sideline node is determined as level D; if the accuracy of the node model corresponding to the sideline node is any value other than 100, when the sideline node moves materials with other sideline nodes, the sideline node must participate in the allocation of material loss (material imbalance), and the priority of the node model corresponding to the sideline node is determined as level D.

[0068] Furthermore, based on the determined node model priority, the priority order can be obtained as A, B, C, D. That is, the sideline node corresponding to priority D participates in the allocation of material loss (material imbalance) first, followed by the material tank node corresponding to priority C, then the sideline node corresponding to priority B, and finally the inbound node, outbound node, and mutual supply node corresponding to priority A.

[0069] Step S102: Based on the node model information and the preset time period, obtain the node constraint information. The node constraint information represents the target loss of the material corresponding to the node model during the process of material flowing between the physical nodes corresponding to the processing node.

[0070] For example, during the flow of materials between physical nodes corresponding to processing nodes, material loss occurs. Therefore, within a preset time period, based on the predicted material change, node measurement values, material inflow, and material outflow corresponding to the node model information, the target material loss amount corresponding to the node model can be determined. The preset time period includes: production shift time period, daily time period, weekly time period, monthly time period, quarterly time period, and yearly time period. In one possible implementation... Figure 4This application provides a schematic diagram illustrating the changes in material quantity corresponding to the flow of materials between physical nodes, such as... Figure 4 As shown, the change in material quantity corresponds to the material flow process at physical nodes. Within a preset time period, the node measurement value of the material at the inlet node in_1 is num_1. The material is transported to the material tank node tank_1 for storage via the material transfer pipe_1. The node measurement value of the material at the material tank node tank_1 is num_2_1. The node measurement value of the material tank node tank_1 before receiving the material is num_2_0. Further, the material is input from the material tank node tank_1 to the material handling device deal_1 for processing via the material transfer pipe_2 and the side line node line_1. The predicted material change amount change_1 is at the side line node line_1. Further, the processed material passes through the side line node l Material is input from the material handling device deal_1 to the material tank node tank_2 for storage via ine_2 and pipe_3. The predicted material change amount change_2 is at the side line node line_2, and the node measurement value of the material at the material tank node tank_2 is num_3_1. The node measurement value of the material tank node tank_2 before receiving the material is num_3_0. Further, the material is input from the material tank node tank_2 to the outgoing node out_1 via the material transfer pipe_4. The node measurement value of the material at the outgoing node out_1 is num_3. Based on the above data of the material during its flow through each physical node, the electronic equipment can generate the target loss amount goal_num_0, that is, obtain the node constraint information.

[0071] Step S103: Based on the node constraint information, obtain the target balance arbitration value. The target balance arbitration value is used to determine whether the material loss between physical nodes is in a balanced state during the material flow between the physical nodes corresponding to the processing node.

[0072] For example, the electronic device processes the target loss amount of the material corresponding to the node constraint information and the node characteristics of the physical nodes in the material production and supply process corresponding to each processing node of the node model to obtain the target balance arbitration value. Then, based on the target balance arbitration value, it can be determined whether the loss amount of the material between the physical nodes is in a balanced state during the process of the material flowing between the physical nodes corresponding to the processing node.

[0073] In one possible implementation, such as Figure 3The node model shown consists of processing nodes corresponding to the inbound node in_1, the tank node tank_1, the sideline node line_1, the sideline node line_2, the tank node tank_2, the sideline node line_3, the tank node tank_3, and the outbound node out_1. Based on the node characteristics of the physical nodes, it is determined that the inbound node in_1, the tank node tank_1, the tank node tank_2, the tank node tank_3, and the outbound node out_1 do not participate in the allocation of material loss (material imbalance) during the material production and supply flow process, that is, they do not participate in the allocation of the target loss. Therefore, the target loss is allocated by the sideline nodes line_1 and line_2. Furthermore, based on the proportion of the node loss of the physical node in the target loss, the target balance arbitration value of the corresponding physical node is determined. More specifically, for example, the proportion of the node loss in the target loss is determined based on the predicted material change and the accuracy of the node model, thereby obtaining the target balance arbitration value.

[0074] Exemplary, such as Figure 3 The node model shown has a predicted material change amount change_1 at the sideline node line_1 with a corresponding node model accuracy of AR_1, a predicted material change amount change_2 at the sideline node line_2 with a corresponding node model accuracy of AR_2, and a predicted material change amount change_2 at the sideline node line_3 with a corresponding node model accuracy of AR_3. The proportions of sideline node line_1 (Ratio_1), sideline node line_2 (Ratio_2), and sideline node line_3 (Ratio_3) are shown in Equation (1). The higher the node model accuracy, the lower the proportion of node loss in the target loss.

[0075]

[0076] Furthermore, based on equation (1), the target balance arbitration value goal_L_1 corresponding to sideline node line_1, the target balance arbitration value goal_L_2 corresponding to sideline node line_2, and the target balance arbitration value goal_L_3 corresponding to sideline node line_3 are obtained as shown in equation (2), where goal_num_0 is the target loss amount; furthermore, based on the target balance arbitration value, it is determined whether the loss amount of the material between the physical nodes is in a balanced state during the flow of the material between the physical nodes corresponding to the processing node, that is, the material balance result is obtained. That is, when the actual loss amount of the material at the physical node is greater than the target balance arbitration value, the material balance result is that the loss amount of the material at the physical node is in an unbalanced state, and when the actual loss amount of the material at the physical node is less than or equal to the target balance arbitration value, the material balance result is that the loss amount of the material at the physical node is in a balanced state.

[0077]

[0078] In this embodiment, node model information is obtained by acquiring the node model information. The node model consists of at least two processing nodes, which correspond to physical nodes in the material production and supply process. The node model information represents the predicted material change of the physical nodes during the flow of material between the processing nodes. Based on the node model information and a preset time period, node constraint information is obtained. The node constraint information represents the target loss of the material corresponding to the node model during the flow of material between the processing nodes. Based on the node constraint information, a target balance arbitration value is obtained. The target balance arbitration value is used to determine whether the loss of material between physical nodes is in a balanced state during the flow of material between the processing nodes. By analyzing the material flow process between physical nodes in the material production and supply process and the node characteristics of each physical node, the predicted material change in a node model consisting of processing nodes corresponding to at least two physical nodes is determined. Then, within a preset time period, based on the predicted material change, the material measurement values ​​at the physical nodes corresponding to the node model, the material inflow, and the material outflow, the target material loss is determined. Furthermore, based on the node characteristics, the target material loss is allocated to the corresponding physical nodes, obtaining the target balance arbitration value for each physical node, which is also the target balance arbitration value for the processing node. Finally, based on the target balance arbitration value, it is determined whether the material loss between the physical nodes is in a balanced state during the material flow process, thus obtaining the material balance result. This solves the problem of low accuracy in material balance results.

[0079] Figure 5 A flowchart of a material movement balancing method provided in another embodiment of this application is shown below. Figure 5As shown, the material movement balancing method provided in this embodiment is... Figure 2 Based on the material movement balancing method provided in the illustrated embodiment, step S102 is further refined, and the material movement balancing method provided in this embodiment includes the following steps:

[0080] Step S201: Obtain the node model information of the node model. The node model consists of at least two processing nodes. The processing nodes correspond to physical nodes in the material production and supply process. The node model information represents the predicted material change of the physical nodes during the flow of materials between the physical nodes corresponding to the processing nodes.

[0081] Step S202: Obtain the node constraint model. The node constraint model is used to constrain the flow characteristics of materials during the process of material flow between physical nodes corresponding to the processing nodes.

[0082] For example, when materials flow between physical nodes corresponding to processing nodes, corresponding flow characteristics are obtained, and then corresponding node constraint models are obtained to constrain the flow characteristics of materials so that the loss of materials is controlled within a preset range.

[0083] In one possible implementation, the node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model;

[0084] Based on the node balance quantity variable and the node true value variable, a first constraint model is obtained. The node balance quantity variable is at least one flow quantity of material when it flows through the physical node corresponding to the processing node, and the node true value variable is the total flow quantity of material when it flows through the physical node corresponding to the processing node. More specifically, the first constraint model includes an input constraint model, an output constraint model, and an input-output constraint model. The input constraint model corresponds to a first processing node with only input flow. The first processing node includes an inbound node, a mutual supply node that only provides material receiving function, and a sideline node that only provides material receiving function. The output constraint model corresponds to a second processing node with only output flow. The second processing node includes an outbound node, a mutual supply node that only provides material output function, and a sideline node that only provides material output function. The input-output constraint model corresponds to a third processing node with both input flow and output flow. The third processing node includes a material tank node.

[0085] As an example, the input constraint model is shown in equation (3).

[0086]

[0087] Among them, R I I_F is the node real value variable for the first processing node that exists only in the input stream. iThe node balance variable is the input stream of the first processing node, where m is the number of input streams of the first processing node;

[0088] As an example, the output constraint model is shown in equation (4).

[0089]

[0090] Among them, R O O_F is the node real value variable for the second processing node that only has an output stream. j The node balance variable is the output stream of the second processing node, where n is the number of output streams of the second processing node;

[0091] As an example, the input-output constraint model is shown in equation (5).

[0092]

[0093] Among them, R B I_F is the node real value variable for a third processing node that simultaneously has input and output streams. a The node balance variable is used to input the input streams of the third processing node, where A is the number of input streams to the third processing node, and O_F is the node balance variable. c C is the node balance variable for the output stream of the third processing node, and C is the number of output streams of the third processing node.

[0094] Based on the node true value variable, node measured value, and node loss variable, a second constraint model is obtained, where the node loss variable is the amount of material loss during the flow of material through the physical nodes corresponding to the processing nodes; exemplarily, the second constraint model is shown in equation (6).

[0095] R = V + L (6)

[0096] Where R is the actual value variable of the physical node corresponding to the processing node, V is the node measurement value of the physical node corresponding to the processing node, and L is the node loss variable of the node model.

[0097] Based on the node model priority and the target loss amount, a third constraint model is obtained. The node model priority represents the allocation order when allocating the target loss amount to the physical nodes corresponding to the processing node. For example, based on the node model priorities corresponding to the inbound node, outbound node, mutual supply node, sideline node and material tank node determined in step S101, a third constraint model with priority order A, B, C and D can be obtained. That is, the sideline node corresponding to priority D participates in the allocation of the target loss amount (material imbalance amount) of the material first, followed by the material tank node corresponding to priority C, then the sideline node corresponding to priority B, and finally the inbound node, outbound node and mutual supply node corresponding to priority A.

[0098] Based on the node measurements and the node model accuracy, a fourth constraint model is obtained. The node model accuracy is used to quantify the accuracy of the node measurements corresponding to the processed node, and the fourth constraint model is used to determine the proportion of the node loss variable corresponding to the processed node in the target loss. The fourth constraint model is shown in equation (7).

[0099]

[0100] Where Ratio represents the proportion of the node loss of the physical node corresponding to the processing node to the total material loss during material movement, V x ARx is the node measurement value of the physical node corresponding to the processing node where material loss occurs, ARx is the node model accuracy of the physical node corresponding to the processing node, and X is the number of physical nodes corresponding to the processing node where material loss occurs during material movement.

[0101] Based on the absolute node loss variable and the target loss, a fifth constraint model is obtained. The fifth constraint model is used to minimize the node loss caused by material movement in the material production and supply process to obtain the target loss. The absolute node loss variable is the absolute value of the node loss variable. For example, the fifth constraint model is shown in equation (8).

[0102]

[0103] Among them, ABS_L x Here, represents the absolute loss of the node, and minimize_Obj represents the target loss of the material.

[0104] Step S203: Based on the node model information, the preset time period, and the node constraint model, obtain the node constraint information. The node constraint information represents the target loss of the material corresponding to the node model during the flow of material between the physical nodes corresponding to the processing node.

[0105] For example, by substituting the predicted material change, node measurement, material inflow, material outflow, node model accuracy, and node model priority corresponding to the node model information into the node constraint models (first constraint model, second constraint model, third constraint model, fourth constraint model, and fifth constraint model), the node constraint information can be obtained, which is to obtain the target loss of the material corresponding to the node model. Among them, the predicted material change corresponds to the node loss variable, the material inflow and material outflow correspond to the node true value variable, and the node measurement corresponds to the node balance variable.

[0106] Step S204: Based on the node constraint information, obtain the target balance arbitration value. The target balance arbitration value is used to determine whether the material loss between physical nodes is in a balanced state during the material flow between the physical nodes corresponding to the processing node.

[0107] In this embodiment, the implementation methods of steps S201 and S204 are the same as those in this application. Figure 2 The implementation methods of steps S101 and S103 in the illustrated embodiment are the same, and will not be described in detail here.

[0108] Furthermore, in another possible implementation, step S204 further includes: based on equations (7) and (8), determining the proportion of node loss in the target loss according to the node measurement value of the physical node and the accuracy of the node model, and then obtaining the target balance arbitration value goal_L, as shown in equation (9).

[0109] goal_L=minimize_Obj*Ratio (9)

[0110] Where Ratio is the proportion of the node loss of the physical node corresponding to the processing node to the target loss (total loss) of the material during the material movement process, and minimize_Obj is the target loss (total loss) of the material.

[0111] That is, the electronic device processes the node model information corresponding to the material obtained in the preset time period based on the node constraint model, and then performs calculations in combination with the preset solution model to obtain the target equilibrium arbitration value. The solution model includes a linear solver and / or a nonlinear solver.

[0112] In this embodiment, when material movement occurs in the material production and supply process, the node loss caused by the material movement is minimized by solving the model to obtain the target loss amount (total loss amount). The target loss amount (total loss amount) is the result of minimizing the node loss. Then, by combining the node loss amount of the physical node corresponding to the processing node with the proportion of the target loss amount (total loss amount) of the material during the material movement, the target balance arbitration value can be accurately determined, which solves the problem of low accuracy of the material balance result determined by manual experience and trial and error methods.

[0113] Figure 6 This is a schematic diagram of a material movement balancing system provided in one embodiment of this application, as shown below. Figure 6 As shown, the material movement balancing system 3 provided in this embodiment includes:

[0114] The node definition unit 31 is used to define the physical nodes in the material production and supply process to obtain the corresponding processing nodes;

[0115] For example, the node definition unit 31 determines the processing node corresponding to the physical node based on the processing characteristics of the physical node in the material production and supply process and / or the data characteristics after processing the material. For example, the processing characteristics of the physical node single tank are that only one material receiving node receives the material output from the material tank, and thus the processing node corresponding to the physical node single tank is obtained, namely the single tank node.

[0116] Model definition unit 32 is used to establish a node model corresponding to the material production and supply process based on the processing nodes;

[0117] For example, the model definition unit 32 establishes a corresponding node model based on the flow process of the processing node and the material flow between physical nodes determined by the node definition unit 31.

[0118] The data acquisition unit 33 is used to obtain node model information based on the node model. The node model information represents the predicted material change amount corresponding to the physical node during the process of material flow between the physical nodes corresponding to the processing node.

[0119] The material movement balancing processing unit 34 is used to obtain node constraint information based on node model information and a preset time period. The node constraint information represents the target loss of the material corresponding to the node model during the process of material flow between the physical nodes corresponding to the processing node. Based on the node constraint information, the target balance arbitration value is obtained. The target balance arbitration value is used to determine whether the loss of the material between the physical nodes is in a balanced state during the process of material flow between the physical nodes corresponding to the processing node.

[0120] Furthermore, the material movement balancing system 3 also includes a processing node maintenance unit. In response to the node characteristics of the physical nodes, the processing node maintenance unit determines the accuracy and priority of the node model of the processing node corresponding to the physical node, so that the data acquisition unit 33 can obtain the node model information based on the node model.

[0121] The material movement and balancing system 3 provided in this embodiment can perform the following... Figures 2-5 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0122] Figure 7 This is a schematic diagram of the structure of a material movement balancing processing device provided in one embodiment of this application, as shown below. Figure 7 As shown, the material movement balancing processing device 4 provided in this embodiment includes:

[0123] The acquisition module 41 is used to acquire the node model information of the node model. The node model consists of at least two processing nodes. The processing nodes correspond to physical nodes in the material production and supply process. The node model information represents the predicted material change of the physical nodes during the process of material flow between the physical nodes corresponding to the processing nodes.

[0124] Processing module 42 is used to obtain node constraint information based on node model information and preset time period. The node constraint information represents the target loss of the material corresponding to the node model during the flow of material between the physical nodes corresponding to the processing node.

[0125] The determination module 43 is used to obtain the target balance arbitration value based on the node constraint information. The target balance arbitration value is used to determine whether the loss of material between physical nodes is in a balanced state during the process of material flow between the physical nodes corresponding to the processing node.

[0126] In one possible implementation, when the processing module 42 obtains the node constraint information based on the node model information and the preset time period, it is specifically used to: obtain the node constraint model, which is used to constrain the flow characteristics of materials during the flow of materials between the physical nodes corresponding to the processing node; and obtain the node constraint information based on the node model information, the preset time period, and the node constraint model.

[0127] In one possible implementation, the node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model. When acquiring the node constraint model, the processing module 42 specifically performs the following steps: Based on the node balance quantity variable and the node true value variable, it obtains the first constraint model, where the node balance quantity variable is at least one flow quantity of material at the physical node corresponding to the processing node, and the node true value variable is the total flow quantity of material at the physical node corresponding to the processing node; based on the node true value variable, the node measured value, and the node loss variable, it obtains the second constraint model, where the node loss variable is the total flow quantity of material at the physical node corresponding to the processing node. The loss amount during the flow of the physical node corresponding to the node; based on the node model priority and the target loss amount, the third constraint model is obtained, where the node model priority represents the allocation order when allocating the target loss amount to the physical node corresponding to the processing node; based on the node measurement value and the node model accuracy, the fourth constraint model is obtained, where the node model accuracy is used to quantify the accuracy of the node measurement value corresponding to the processing node, and the fourth constraint model is used to determine the target loss proportion of the node loss amount variable corresponding to the processing node in the target loss amount; based on the node absolute loss amount variable and the target loss amount, the fifth constraint model is obtained, where the node absolute loss amount variable is the absolute value of the node loss amount variable.

[0128] In one possible implementation, the first constraint model includes an input constraint model, an output constraint model, and an input-output constraint model. The input constraint model corresponds to a first processing node with only an input stream, the output constraint model corresponds to a second processing node with only an output stream, and the input-output constraint model corresponds to a third processing node with both an input stream and an output stream.

[0129] In one possible implementation, the node model information includes node model accuracy and node model priority. The node model accuracy is used to quantify the accuracy of the node measurement values ​​of the material corresponding to the processing node, and the node model priority represents the allocation order when allocating material losses to the physical node corresponding to the processing node. The acquisition module 41 determines the node model priority based on the node model accuracy.

[0130] The acquisition module 41, processing module 42, and determination module 43 are connected sequentially. The material movement balancing processing device 4 provided in this embodiment can perform the following... Figures 2-5 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0131] Figure 8 A schematic diagram of an electronic device provided in one embodiment of this application, as shown below. Figure 8 As shown, the electronic device 5 provided in this embodiment includes:

[0132] Processor 51, and memory 52 communicatively connected to processor 51.

[0133] Among them, memory 52 stores computer-executed instructions;

[0134] The processor 51 executes computer execution instructions stored in the memory 52 to implement this application. Figures 2-5 The material movement balancing method provided in any of the corresponding embodiments.

[0135] The memory 52 and the processor 51 are connected via a bus 53.

[0136] For relevant instructions, please refer to the corresponding text. Figures 2-5 The relevant descriptions and effects of the steps in the corresponding embodiments are understood, and will not be elaborated on here.

[0137] One embodiment of this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement this application. Figures 2-5 The material movement balancing method provided in any of the corresponding embodiments.

[0138] The computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0139] One embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements this application. Figures 2-5 The material movement balancing method provided in any of the corresponding embodiments.

[0140] Figure 9 This is a block diagram illustrating an exemplary embodiment of the present application of a terminal device 800, which may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0141] The terminal device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0142] Processing component 802 typically controls the overall operation of terminal device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0143] Memory 804 is configured to store various types of data to support operation on terminal device 800. Examples of this data include instructions for any application or method operating on terminal device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 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 storage, flash memory, magnetic disk, or optical disk.

[0144] Power supply component 806 provides power to various components of terminal device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to terminal device 800.

[0145] Multimedia component 808 includes a screen that provides an output interface between terminal device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When terminal device 800 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0146] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when terminal device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0147] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0148] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of terminal device 800. For example, sensor assembly 814 can detect the on / off state of terminal device 800, the relative positioning of components such as the display and keypad of terminal device 800, changes in the position of terminal device 800 or a component of terminal device 800, the presence or absence of user contact with terminal device 800, the orientation or acceleration / deceleration of terminal device 800, and temperature changes of terminal device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0149] Communication component 816 is configured to facilitate wired or wireless communication between terminal device 800 and other devices. Terminal device 800 can access wireless networks based on communication standards, such as WiFi, 3G, 4G, 5G, or other standard communication networks, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0150] In an exemplary embodiment, the terminal device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the functions described in this application. Figures 2-5 The method provided in any of the corresponding embodiments.

[0151] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of a terminal device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0152] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a terminal device, enables the terminal device 800 to perform the above-described embodiments of this application. Figures 2-5 The method provided in any of the corresponding embodiments.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0154] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0155] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for material movement balancing, characterized in that, The method includes: Obtain node model information of the node model, wherein the node model consists of at least two processing nodes, the processing nodes correspond to physical nodes in the material production and supply process, and the node model information characterizes the predicted material change amount corresponding to the physical node during the process of material flow between the physical nodes corresponding to the processing nodes. A node constraint model is obtained. Based on the node model information, a preset time period, and the node constraint model, node constraint information is obtained. The node constraint model is used to constrain the flow characteristics of materials during the flow of materials between the physical nodes corresponding to the processing node. The node constraint information represents the target loss of materials corresponding to the node model during the flow of materials between the physical nodes corresponding to the processing node. The node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model. Based on the node constraint information, a target balance arbitration value is obtained. The target balance arbitration value is used to determine whether the loss of the material between the physical nodes is in a balanced state during the process of the material flowing between the physical nodes corresponding to the processing node. The method for obtaining the node constraint model includes: The first constraint model is obtained based on the node balance variable and the node true value variable. The node balance variable is at least one flow amount of material when it flows through the physical node corresponding to the processing node, and the node true value variable is the total flow amount of material when it flows through the physical node corresponding to the processing node. The second constraint model is obtained based on the node true value variable, the node measured value and the node loss variable, wherein the node loss variable is the amount of material loss when the material flows through the physical node corresponding to the processing node. The third constraint model is obtained based on the node model priority and the target loss amount, wherein the node model priority represents the allocation order when allocating the target loss amount to the physical node corresponding to the processing node; Based on the node measurement values ​​and node model accuracy, the fourth constraint model is obtained. The node model accuracy is used to quantify the accuracy of the node measurement values ​​corresponding to the processing node. The fourth constraint model is used to determine the target loss ratio of the node loss variable corresponding to the processing node in the target loss amount. Based on the node absolute loss variable and the target loss, the fifth constraint model is obtained, where the node absolute loss variable is the absolute value of the node loss variable.

2. The method according to claim 1, characterized in that, The first constraint model includes an input constraint model, an output constraint model, and an input-output constraint model. The input constraint model corresponds to a first processing node with only an input stream, the output constraint model corresponds to a second processing node with only an output stream, and the input-output constraint model corresponds to a third processing node with both an input stream and an output stream.

3. The method according to claim 1, characterized in that, The node model information includes node model accuracy and node model priority. The node model accuracy is used to quantify the accuracy of the node measurement values ​​of the material corresponding to the processing node, and the node model priority represents the allocation order when allocating material losses to the physical nodes corresponding to the processing node. The priority of the node model is determined based on its accuracy.

4. A material movement balancing system, characterized in that, include: The node definition unit is used to define the physical nodes in the material production and supply process to obtain the corresponding processing nodes; The model definition unit is used to establish a node model corresponding to the material production and supply process based on the processing node. The data acquisition unit is used to obtain node model information based on the node model, wherein the node model information characterizes the predicted material change amount corresponding to the physical node during the material flow between the physical nodes corresponding to the processing node. A material movement balancing processing unit is used to acquire a node constraint model. Based on the node model information, a preset time period, and the node constraint model, node constraint information is obtained. The node constraint model is used to constrain the flow characteristics of materials during the flow of materials between the physical nodes corresponding to the processing node. The node constraint information characterizes the target loss amount of the material corresponding to the node model during the flow of materials between the physical nodes corresponding to the processing node. The node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model. Based on the node constraint information, a target balance arbitration value is obtained. The target balance arbitration value is used to determine whether the loss amount of the material between the physical nodes is in a balanced state during the flow of materials between the physical nodes corresponding to the processing node. The material movement balancing processing unit is further configured to obtain the first constraint model based on the node balance quantity variable and the node true value variable, wherein the node balance quantity variable is at least one flow quantity of the material when it flows through the physical node corresponding to the processing node, and the node true value variable is the total flow quantity of the material when it flows through the physical node corresponding to the processing node. Based on the node true value variable, node measured value, and node loss variable, the second constraint model is obtained, where the node loss variable is the amount of material loss during the flow of material through the physical node corresponding to the processing node; based on the node model priority and the target loss amount, the third constraint model is obtained, where the node model priority represents the allocation order when allocating the target loss amount to the physical node corresponding to the processing node. Based on the node measurement values ​​and node model accuracy, the fourth constraint model is obtained. The node model accuracy is used to quantify the accuracy of the node measurement values ​​corresponding to the processing node. The fourth constraint model is used to determine the target loss ratio of the node loss variable corresponding to the processing node in the target loss amount. Based on the node absolute loss variable and the target loss amount, the fifth constraint model is obtained. The node absolute loss variable is the absolute value of the node loss variable.

5. A material movement balancing device, characterized in that, include: The acquisition module is used to acquire node model information of the node model. The node model consists of at least two processing nodes, which correspond to physical nodes in the material production and supply process. The node model information represents the predicted material change of the physical node during the process of material flow between the physical nodes corresponding to the processing nodes. The processing module is used to acquire a node constraint model; based on the node model information, a preset time period, and the node constraint model, it obtains node constraint information. The node constraint model is used to constrain the flow characteristics of materials during the flow of materials between the physical nodes corresponding to the processing node; the node constraint information characterizes the target loss amount of the material corresponding to the node model during the flow of materials between the physical nodes corresponding to the processing node; the node constraint model includes a first constraint model, a second constraint model, a third constraint model, a fourth constraint model, and a fifth constraint model. The determination module is used to obtain a target balance arbitration value based on the node constraint information. The target balance arbitration value is used to determine whether the loss of the material between the physical nodes is in a balanced state during the process of the material flowing between the physical nodes corresponding to the processing node. The processing module is further configured to obtain the first constraint model based on the node balance variable and the node true value variable, wherein the node balance variable is at least one flow amount of material when it flows through the physical node corresponding to the processing node, and the node true value variable is the total flow amount of material when it flows through the physical node corresponding to the processing node. Based on the node true value variable, node measured value, and node loss variable, the second constraint model is obtained, where the node loss variable is the amount of material loss during the flow of material through the physical node corresponding to the processing node; based on the node model priority and the target loss amount, the third constraint model is obtained, where the node model priority represents the allocation order when allocating the target loss amount to the physical node corresponding to the processing node. Based on the node measurement values ​​and node model accuracy, the fourth constraint model is obtained. The node model accuracy is used to quantify the accuracy of the node measurement values ​​corresponding to the processing node. The fourth constraint model is used to determine the target loss ratio of the node loss variable corresponding to the processing node in the target loss amount. Based on the node absolute loss variable and the target loss amount, the fifth constraint model is obtained. The node absolute loss variable is the absolute value of the node loss variable.

6. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the material movement balancing method as described in any one of claims 1 to 3.

8. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the material movement balancing method according to any one of claims 1 to 3.

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