Production metering topology modeling method and system based on metering point variable constraint, and storage medium

Abstract

The application relates to a production metering topology modeling method and system based on metering point variable constraints and a storage medium. By establishing clear metering point variable constraints and their inheritance relationship, combining bottom-up data linkage calculation and embedded reasonable interval verification mechanism, the structured construction and automatic calculation of the production metering model are realized. The scheme can ensure that the attributes such as the dimension, period, unit and the like of metering data remain consistent in the whole network, eliminates the error-prone nature of traditional manual statistics, and improves the accuracy and traceability of metering results. In addition, by introducing asymmetric data linkage calculation and real-time early warning, potential data anomalies in the metering process can be found and fed back in time, thereby effectively improving the automation level, transparency and management efficiency of enterprise production metering, and effectively supporting the needs of lean production and cost fine management.

Description

Technical Field

[0001] This application relates to the field of industrial production measurement technology, and in particular to a production measurement topology modeling method, system and storage medium based on measurement point variable constraints. Background Technology

[0002] In process industries such as chemical, metallurgical, pharmaceutical, and food processing, production processes typically involve a series of complex material processing and energy conversions. To achieve refined management, cost control, and efficiency improvement, enterprises need to utilize various metering instruments and control systems to accurately and in real-time measure material consumption, intermediate products, final products, and energy consumption during the production process. The widespread application of intelligent instruments, distributed control systems, weighing systems, and various intelligent equipment has dramatically increased the number of underlying real-time data points that enterprises can perceive, and the data granularity has become more refined. This provides a data foundation for the digital and transparent management of the production process.

[0003] However, despite significant improvements in underlying data perception capabilities, existing technologies still present significant challenges in effectively organizing and utilizing this data to form a complete, accurate, and traceable production measurement system. For example, real-time data at the enterprise perception layer is often scattered across different systems. This data frequently requires production department statisticians to manually copy it into spreadsheets, then write calculation formulas based on personal experience or fragmented documents, and manually summarize and generate reports. This approach is not only inefficient and prone to errors, but the separation of calculation logic from data sources also results in an opaque measurement process that is difficult to trace and verify. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a production measurement topology modeling method, system, and storage medium based on measurement point variable constraints that effectively improves the automation level, transparency, and management efficiency of enterprise production measurement.

[0005] To achieve the above objectives, in a first aspect, embodiments of this application provide a production metering topology modeling method based on metering point variable constraints, comprising the following steps:

[0006] S1: Based on the measurement requirements, determine a target measurement point, and define variable constraints for the target measurement point that include the measurement data source and calculation logic, as well as set a reasonable range for result verification.

[0007] S2: Determine whether the data source of the current metering point is the smallest metering unit. If yes, stop decomposing the branch of the metering point. If no, determine the data source as one or more sub-metering points and define variable constraints for the sub-metering points that are inherited from their parent metering points.

[0008] S3: Repeat step S2 until the end of all metering point branches is the smallest metering unit, in order to build a complete production metering topology network.

[0009] S4: Perform bottom-up data linkage calculation according to the production metering topology network; wherein, when the data of any lower-level metering point changes, all directly or indirectly related upper-level metering points are triggered to recalculate the data according to their respective preset calculation logic;

[0010] S5: During the data linkage calculation process, the calculation result of at least one metering point in the production metering topology network is compared with its preset reasonable range in real time, and an early warning signal is generated when the calculation result exceeds the reasonable range.

[0011] Preferably, the variable constraints include at least one or more of the following: measurement dimension, measurement period, measurement accuracy, measurement unit, measurement data source, and calculation logic.

[0012] Preferably, the variable constraints of the lower-level metering point inherit the metering dimension, metering period, and metering unit of its higher-level metering point.

[0013] Preferably, the smallest measurement unit is a constant data source, a real-time acquired measurement point data source, or a manually entered data source.

[0014] Preferably, the reasonable range is a numerical range set with reference to enterprise standards, industry standards, or national standards.

[0015] Preferably, the data linkage calculation is asymmetric, specifically in that changes in the data of the upper-level metering point will not trigger the recalculation of the data of its lower-level metering points.

[0016] Preferably, the measurement objects corresponding to the measurement requirements include unit consumption measurement, yield measurement, benefit measurement, or allocation and balance measurement.

[0017] Secondly, embodiments of this application provide a production metering topology model construction system based on metering point variable constraints, including:

[0018] The definition and decomposition module is used to determine the target metering points based on metering requirements and to perform a top-down recursive decomposition until the end of all metering point branches is the smallest metering unit, so as to construct the production metering topology network.

[0019] The calculation module is used to perform bottom-up data linkage calculation according to the production metering topology network. When the data of any lower-level metering point changes, it triggers all directly or indirectly related upper-level metering points to recalculate the data according to the preset calculation logic.

[0020] The verification and early warning module is used to compare the calculation result of at least one metering point output by the calculation module with a preset reasonable range, and generate an early warning signal when the calculation result exceeds the reasonable range.

[0021] Preferably, the calculation module is configured to perform asymmetric linkage calculation, wherein changes in the data of the upper-level metering point will not trigger the recalculation of the data of its lower-level metering points.

[0022] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, it implements the method described in any embodiment of the first aspect.

[0023] The production measurement topology modeling method, system, and storage medium designed in this application, based on measurement point variable constraints, achieves structured construction and automated calculation of the production measurement model by establishing clear measurement point variable constraints and their inheritance relationships, combined with bottom-up data linkage calculation and an embedded reasonable interval verification mechanism. This scheme ensures the consistency of attributes such as dimension, period, and unit of measurement data throughout the network, eliminating the error-proneness of traditional manual statistics and improving the accuracy and traceability of measurement results. Furthermore, by introducing asymmetric data linkage calculation and real-time early warning, potential data anomalies during the measurement process can be detected and reported in a timely manner, thereby effectively improving the automation level, transparency, and management efficiency of enterprise production measurement, and strongly supporting the needs of lean production and refined cost management. Attached Figure Description

[0024] Figure 1 This is a flowchart of the production metering topology modeling method based on metering point variable constraints provided in the embodiments of this application. Detailed Implementation

[0025] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0026] Firstly, embodiments of this application provide a production measurement topology modeling method based on measurement point variable constraints. To more clearly illustrate the technical solution of this application, a specific, non-limiting implementation scenario will be used for explanation below. In this embodiment, the method can be executed on a server or industrial computer system, which stores corresponding instructions. When the processor executes these instructions, the following method will be implemented.

[0027] This example uses the measurement process of thermal efficiency of coal-fired steam boilers in petrochemical enterprises as a case study. Boiler thermal conversion efficiency is a core indicator for measuring how a boiler converts the chemical energy of fuel into effective thermal energy, directly affecting the enterprise's energy consumption and operating costs.

[0028] Specifically, see Figure 1 In this scenario, the method includes the following steps:

[0029] S1: Based on the measurement requirements, a target measurement point is determined, and variable constraints containing the measurement data source and calculation logic are defined for the target measurement point, as well as a reasonable range for result verification. In this embodiment, the variable constraints include at least one or more of the following: measurement dimension, measurement period, measurement accuracy, measurement unit, measurement data source, and calculation logic; the reasonable range is a numerical range set with reference to enterprise standards, industry standards, or national standards; the measurement objects corresponding to the measurement requirements include unit consumption measurement, yield measurement, benefit measurement, or allocation balance measurement.

[0030] In this embodiment, the specific scenario is as follows:

[0031] The metering requirement was defined as “calculating the hourly reverse balance thermal efficiency of a coal-fired steam boiler”, and the target metering point was set as “boiler thermal efficiency η”.

[0032] Subsequently, the variable constraints for this target measurement point are defined in detail. These constraints specifically exemplify several constraint attributes defined in this application:

[0033] Metering Dimensions: Coal-fired Boiler A (used to identify the specific metering object entity), Metering Cycle: Hours, Metering Accuracy: Two significant figures, Metering Unit: %, and Metering Data Source. The data source here is not a single direct measurement value, but a composite data source composed of a set of lower-level metering items, specifically including: flue gas heat loss Q2, incomplete combustion heat loss Q3, incomplete combustion heat loss Q4, heat dissipation loss Q5, physical heat loss of ash and slag Q6, and a constant 100.

[0034] Operational logic: Based on the reverse balance method, its calculation formula is: η=100-(Q2+Q3+Q4+Q5+Q6).

[0035] Meanwhile, a reasonable range for result verification was set for the target measurement point. In this embodiment, the range was set with reference to the national standard GB 24500-2020 "Energy Efficiency Limits and Energy Efficiency Grades of Industrial Boilers" and in combination with the design grade of the boiler, specifically [82, 100].

[0036] It should be understood that this method can be widely applied to various metering needs, such as unit consumption metering (e.g., coal consumption per ton of steam), yield metering (e.g., yield of chemical products), or allocation and balance metering (e.g., allocation of utility costs among workshops). This embodiment is only one illustrative application and does not constitute a limitation on the scope of protection of this application.

[0037] S2: Determine whether the data source of the current metering point is the smallest metering unit. If yes, stop decomposing the branch of that metering point; otherwise, determine the data source as one or more sub-metering points, and define variable constraints for the sub-metering points that inherit from their parent metering points. In this embodiment, the variable constraints of the lower-level metering points inherit the metering dimension, metering period, and metering unit of their parent metering points.

[0038] S3: Repeat step S2 until the end of all metering point branches is a minimum metering unit, to construct a complete production metering topology network. In this embodiment, the minimum metering unit is a constant data source, a real-time acquired measured point data source, or a manually entered data source.

[0039] In practice, steps S2 and S3 can be executed as a coherent, top-down recursive decomposition process to construct the metering topology network. The process is as follows:

[0040] First decomposition:

[0041] The process begins with the boiler thermal efficiency η at the root node. First, its data sources are analyzed, namely flue gas heat loss Q2, incomplete combustion heat loss Q3, incomplete combustion heat loss Q4, heat dissipation loss Q5, and ash and slag physical heat loss Q6. These data sources need to be obtained through further calculation or query and do not belong to the smallest metering unit. Therefore, the decomposition process must continue.

[0042] Accordingly, the flue gas heat loss Q2, the incomplete combustion heat loss Q3, the incomplete combustion heat loss Q4, the heat dissipation loss Q5, and the physical heat loss of ash and slag Q6 are determined as the first layer of sub-metering points for the target metering point boiler thermal efficiency η; subsequently, the variable constraints are defined for each sub-metering point.

[0043] Taking the flue gas heat loss Q2 at the sub-metering point as an example:

[0044] Its measurement dimension (coal-fired boiler A), measurement cycle (hours), and measurement unit (%) are inherited from the boiler thermal efficiency η of its previous measurement point to ensure the uniformity of data scope and logical consistency throughout the entire topology network. In this embodiment, the measurement data source and calculation logic for flue gas heat loss Q2 are defined according to the boiler thermal test procedures, for example:

[0045]

[0046] Among them, flue gas heat loss The data sources for measurement include: excess air coefficient at the smoke exhaust point. Measured smoke exhaust temperature Ambient temperature Measurement point for heat loss due to incomplete combustion of solids Calculate coefficient constants , At the same time, a reasonable range is set for the flue gas heat loss Q2, for example, based on experience or relevant standards, it is set to [4,8].

[0047] Similarly, the same definition process is applied to the heat loss from incomplete combustion of gases (Q3), incomplete combustion of solids (Q4), heat dissipation loss (Q5), and physical heat loss from ash and slag (Q6).

[0048] Second and subsequent decompositions:

[0049] For newly generated sub-metric points, the judgment and decomposition process in step S2 is repeated for the newly generated first-layer sub-metric points. This process continues layer by layer downwards until the end of all branches reaches the smallest metric unit. Specifically:

[0050] For the heat loss Q2 of the flue gas at the metering point, its data source is the excess air coefficient at the flue gas exhaust point. Measured smoke exhaust temperature Ambient temperature The data source is the real-time measured point data collected from field sensors, which belongs to the smallest unit of measurement. Therefore, the decomposition of these data input branches terminates here. However, its data source, the heat loss from incomplete combustion of solids Q4, is not the smallest unit of measurement, and therefore it is associated with the measurement point of heat loss from incomplete combustion of solids.

[0051] The heat loss Q3 from incomplete combustion of gas at the metering point can be determined by looking up a table, and its calculation logic is a conditional judgment:

[0052] project unit numerical values numerical values numerical values CO % CO≤0.05 0.05<CO≤0.1 0.1<CO <![CDATA[Q3]]> % 0.2 0.5 1

[0053] Heat loss due to incomplete combustion of gas The data source for measurement is the volume percentage of CO in flue gas and a constant. This value can be measured by sensors or manually entered, and it belongs to the smallest measurement unit.

[0054] The heat loss from incomplete combustion of solids at the metering point, Q4, is calculated using a formula whose operation logic involves algebraic operations of addition, subtraction, multiplication, and division.

[0055]

[0056] Heat loss from incomplete combustion of solids The metering data sources include: received base ash content The lower heating value of the fuel received The mass fraction of fly ash content relative to the total ash content of coal fed into the furnace. Combustible content of fly ash The mass fraction of ash content in slag relative to the total ash content of coal fed into the furnace. slag combustible content The constant is 328.66. These data typically come from data sources manually entered after laboratory analysis and represent the smallest unit of measurement.

[0057] The heat loss Q5 at the metering point can be obtained from a table based on the boiler tonnage:

[0058] Rated evaporation capacity (t / h) (or rated thermal power (MW)) ≤4 (or ≤2.8) 6 (or 4.2) 10 (or 7.0) 15 (or 10.5) 20 (or 14) 35 (or 29) 65 (or 46) <![CDATA[Heat loss q 5lt / %]]> 2.9 2.4 1.7 1.5 1.3 1.1 0.8

[0059] Its data source, rated evaporation capacity, is an inherent equipment parameter of the boiler and can be regarded as a constant data source, belonging to the smallest metering unit.

[0060] For the physical heat loss of ash Similarly, it is calculated using a formula, and its operational logic is algebraic operations of subtraction, multiplication, and division:

[0061]

[0062] Physical heat loss of ash The metering data sources include: received base ash content The lower heating value of the fuel received The mass fraction of ash content in slag relative to the total ash content of coal fed into the furnace. slag combustible content Slag enthalpy These data typically come from data sources manually entered after laboratory analysis, and represent the smallest unit of measurement.

[0063] Through the above recursive decomposition steps, the final branches all point to the smallest measurement units: constants, measured points, or manually entered values. Thus, a hierarchical and closely related production metering topology network is constructed, with boiler thermal efficiency η as the root node, Q2 to Q6 as intermediate nodes, and various basic data as leaf nodes.

[0064] S4: Perform bottom-up data linkage calculation according to the production metering topology network; wherein, when the data of any lower-level metering point changes, all directly or indirectly related upper-level metering points are triggered to recalculate the data according to their respective preset calculation logic.

[0065] Specifically, once the topology network is constructed, the computation process is the reverse of the construction process. Let's take a specific computational scenario as an example:

[0066] The lab technician entered a new received ash content A. ar The value, this is a data update event for a minimum unit of measurement, due to the received base ash A. arIt is one of the data sources for the incomplete combustion heat loss of solids Q4 at the metering point. When the data of the base ash content Aar received by this smallest metering unit changes, it will trigger the recalculation of the incomplete combustion heat loss of solids Q4 at its direct superior node.

[0067] When the calculation result of the incomplete combustion heat loss Q4 is updated, since Q4 is one of the data sources of the flue gas heat loss Q2 at the metering point, the recalculation of the flue gas heat loss Q2 is triggered. And when the calculation result of the flue gas heat loss Q2 is updated, since Q2 is one of the data sources of the final target metering point boiler thermal efficiency η, the recalculation of the top-level target metering point boiler thermal efficiency η is triggered.

[0068] Furthermore, in this embodiment, the data linkage calculation is asymmetric, specifically: changes in data at higher-level metering points will not trigger recalculation of data at lower-level metering points. For example, if a manager manually modifies the displayed value of the thermal efficiency η of the top-level boiler for a specific purpose, this modification will not trigger a recalculation of data such as flue gas heat loss Q2 and incomplete solid combustion heat loss Q4 at lower-level nodes. This ensures that the calculation logic strictly follows the physical causal relationships of the production process, guaranteeing the authenticity and reliability of the data.

[0069] S5: During the data linkage calculation process, the calculation result of at least one metering point in the production metering topology network is compared with its preset reasonable range in real time, and an early warning signal is generated when the calculation result exceeds the reasonable range.

[0070] In practice, this verification step and the data linkage calculation process described in S4 can be executed synchronously. That is, whenever the value of a metering point in the network is recalculated, the verification mechanism is immediately triggered.

[0071] Continuing the calculation scenario in S4, when the boiler thermal efficiency η is recalculated, for example, to a new value of 79.8%, 79.8% is immediately compared with the preset reasonable range [82, 100] for the boiler thermal efficiency η. Since 79.8% is lower than the lower limit of the range (82%), the system judges it as abnormal and generates a warning signal. In this embodiment, the warning signal can take various forms, such as highlighting the value in red on the monitoring interface, popping up an alarm window, or sending a notification with the content "Boiler thermal efficiency is low, please check" to designated personnel via a message service.

[0072] Understandably, this verification mechanism is distributed and can be deployed on any intermediate node of the topology network, thus providing more refined fault diagnosis capabilities. For example, if in the linkage calculation, the calculated value of the flue gas heat loss Q2 at an intermediate metering point is 9.5%, exceeding its reasonable range [4,8], the system will generate a specific warning for the flue gas heat loss Q2. This multi-level warning mechanism can help technicians quickly locate the root cause of the problem. For example, this warning directly points to a possible anomaly in the flue gas process, such as excessively high flue gas temperature, rather than just a general notification that the final efficiency is not up to standard, effectively improving the efficiency of problem investigation.

[0073] Secondly, corresponding to the aforementioned method embodiments, this application also provides a production metering topology model construction system based on metering point variable constraints.

[0074] In practice, the system can be deployed on one or more servers as an integrated hardware and software platform to execute the aforementioned methods. Its internal logical structure can be divided into the following cooperative functional modules:

[0075] Definition and Decomposition Module: Used to determine target metering points based on metering requirements and perform top-down recursive decomposition until the end of all metering point branches is the smallest metering unit, so as to construct the production metering topology network.

[0076] Specifically, this module is responsible for implementing steps S1 to S3 in the aforementioned method. In one possible implementation, this module provides a graphical or tabular user interface to users such as process engineers or metrology administrators, allowing users to establish connections between metrology points and set variable constraints through visual operations such as dragging and dropping nodes, configuring parameters, and defining computational logic.

[0077] Calculation module: Used to perform bottom-up data linkage calculation according to the production metering topology network. When the data of any lower-level metering point changes, it triggers all directly or indirectly related upper-level metering points to recalculate the data according to the preset calculation logic.

[0078] Specifically, this module is configured to perform the bottom-up data linkage calculation described in S4. In one possible implementation, this module is typically implemented as a backend service or computing engine. It monitors data changes in all the smallest units of measurement in real time. Once a change is detected, it initiates a bottom-up chained computing task based on the constructed topology network structure and updates the calculation results of the affected nodes in the network. In this embodiment, the computing module is configured to perform asymmetric linkage calculation, where changes in the data of the upper-level measurement point do not trigger recalculation of the data of its lower-level measurement points, thus ensuring the logical correctness of the calculation.

[0079] Verification and early warning module: used to compare the calculation result of at least one metering point output by the calculation module with a preset reasonable range, and generate an early warning signal when the calculation result exceeds the reasonable range.

[0080] Specifically, this module is responsible for implementing the real-time verification and early warning functions described in S5. In one possible implementation, this module is tightly coupled to or integrated with the computing module. Whenever the computing module updates the value of a node, this module immediately obtains the preset reasonable range of that node for comparison. If the early warning conditions are met, it will push the structured early warning information to the front-end user interface or a third-party alarm system through a message queue, API call, or other communication mechanism.

[0081] Thirdly, embodiments of this application also provide a computer-readable storage medium. This computer-readable storage medium can be any form of non-transitory memory, such as a server's hard disk, solid-state drive (SSD), flash memory, or read-only memory (ROM), etc., on which computer program instructions are permanently stored. When these instructions are loaded and executed by one or more processors, they enable the computing device where the processor resides, such as the aforementioned system, to completely execute all the steps of the production metrology topology modeling method based on metrology point variable constraints described in any embodiment of the first aspect.

[0082] The production measurement topology modeling method, system, and storage medium based on measurement point variable constraints provided in this application achieve structured construction and automated calculation of the production measurement model by establishing clear measurement point variable constraints and their inheritance relationships, combined with bottom-up data linkage calculation and an embedded reasonable interval verification mechanism. This solution ensures the consistency of attributes such as dimension, period, and unit of measurement data throughout the network, eliminating the error-proneness of traditional manual statistics and improving the accuracy and traceability of measurement results. Furthermore, by introducing asymmetric data linkage calculation and real-time early warning, potential data anomalies during the measurement process can be detected and reported in a timely manner, thereby effectively improving the automation level, transparency, and management efficiency of enterprise production measurement, and strongly supporting the needs of lean production and refined cost management.

[0083] In the description of this application, it should be noted that the terms "vertical", "up", "down", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0084] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0085] Finally, it should be noted that the above descriptions are merely preferred embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A production measurement topology modeling method based on measurement point variable constraints, characterized in that, Includes the following steps: S1: Based on the measurement requirements, determine a target measurement point, and define variable constraints for the target measurement point that include the measurement data source and calculation logic, as well as set a reasonable range for result verification. S2: Determine whether the data source of the current metering point is the smallest metering unit. If yes, stop decomposing the branch of the metering point. If no, determine the data source as one or more sub-metering points and define variable constraints for the sub-metering points that are inherited from their parent metering points. S3: Repeat step S2 until the end of all metering point branches is the smallest metering unit, in order to build a complete production metering topology network. S4: Perform bottom-up data linkage calculation according to the production metering topology network; wherein, when the data of any lower-level metering point changes, all directly or indirectly related upper-level metering points are triggered to recalculate the data according to their respective preset calculation logic; S5: During the data linkage calculation process, the calculation result of at least one metering point in the production metering topology network is compared with its preset reasonable range in real time, and an early warning signal is generated when the calculation result exceeds the reasonable range. The variable constraints of the lower-level metering point inherit the metering dimension, metering period and metering unit of its higher-level metering point. The data linkage calculation is asymmetric, specifically: changes in the data of the upper-level metering point will not trigger the recalculation of the data of its lower-level metering points.

2. The production measurement topology modeling method based on measurement point variable constraints according to claim 1, characterized in that, The variable constraints include at least one or more of the following: measurement dimension, measurement period, measurement accuracy, measurement unit, measurement data source, and calculation logic.

3. The production measurement topology modeling method based on measurement point variable constraints according to claim 1, characterized in that, The smallest unit of measurement is a constant data source, a real-time data source of measured points, or a manually entered data source.

4. The production measurement topology modeling method based on measurement point variable constraints according to claim 1, characterized in that, The reasonable range is a numerical range set with reference to enterprise standards, industry standards, or national standards.

5. The production measurement topology modeling method based on measurement point variable constraints according to claim 1, characterized in that, The measurement objects corresponding to the measurement requirements include unit consumption measurement, yield measurement, benefit measurement, or allocation and balance measurement.

6. A production measurement topology model construction system based on measurement point variable constraints, applied to the method as described in any one of claims 1 to 5, characterized in that, include: The definition and decomposition module is used to determine the target metering points based on metering requirements and to perform a top-down recursive decomposition until the end of all metering point branches is the smallest metering unit, so as to construct the production metering topology network. The calculation module is used to perform bottom-up data linkage calculation according to the production metering topology network. When the data of any lower-level metering point changes, it triggers all directly or indirectly related upper-level metering points to recalculate the data according to the preset calculation logic. The verification and early warning module is used to compare the calculation result of at least one metering point output by the calculation module with a preset reasonable range, and generate an early warning signal when the calculation result exceeds the reasonable range.

7. The production measurement topology model construction system based on measurement point variable constraints according to claim 6, characterized in that, The calculation module is configured to perform asymmetric linkage calculations, in which changes in the data of the upper-level metering point will not trigger the recalculation of the data of its lower-level metering points.

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

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