Industrial production whole-process carbon emission intelligent accounting and dynamic adjustment system

Abstract

This invention discloses an intelligent accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process. This invention relates to the field of carbon emission management technology. It uses a full-domain perception mapping module to digitally model the production line, collecting energy consumption, production rate, and carbon emission data from each production node to construct a multi-dimensional data set. Based on an energy efficiency intelligent assessment and optimization module, it performs cross-line energy efficiency evaluations on similar production nodes, identifying the optimal energy consumption range and its corresponding candidate production rate range through curve fitting and dynamic threshold analysis. Furthermore, relying on a dual-carbon synergistic control module, it constructs bidirectional fluctuation curves for energy consumption and carbon emissions in the same coordinate system. Through curve translation and overlapping area analysis, it determines the optimal balance range that considers both energy efficiency and environmental protection, ultimately outputting the optimal production rate range to guide actual production control. This achieves synergistic optimization of energy efficiency and carbon emission control, providing industrial enterprises with precise and dynamic production decision support and assisting in the green and low-carbon transformation of the manufacturing industry.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission management technology, specifically, it relates to an intelligent accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process. Background Technology

[0002] Building a synergistic optimization system that can coordinate energy efficiency and carbon emission management to achieve a win-win situation for economic and environmental benefits is an important direction for the industrial sector to explore sustainable development paths.

[0003] Carbon emission accounting and control in industrial production largely rely on end-point statistics or static quota management, making it difficult to achieve real-time sensing and dynamic optimization at the micro-level of the production process. This results in a lack of refined perception of the entire production process. For example, only the total energy consumption and total carbon emissions of a workshop or the entire production line can be obtained, without pinpointing specific production nodes. When carbon emissions exceed limits, the only recourse is often to shut down the entire production line for investigation or make rough adjustments, which are costly and inefficient. Furthermore, energy efficiency assessments typically use fixed benchmark values ​​or industry averages, lacking the ability to make horizontal comparisons and optimizations of dynamic energy consumption performance at different production rates. This leads to production parameter settings often being based on... Based on experience or equipment nameplate parameters, the dynamic optimal matching relationship between production rate and energy consumption is ignored, resulting in a large amount of hidden energy waste. Existing technologies often treat energy consumption and carbon emissions as single targets for independent optimization in the control process, or only punish excessive carbon emissions after the fact. There is a lack of a dynamic adjustment mechanism that coordinates the optimal energy consumption range with carbon emission performance. This isolated, static, and extensive management model often leads to distorted calculation results when faced with fluctuations in operating conditions, product changes, or equipment aging. Control commands are also often delayed or excessive, making it impossible to achieve truly lean production and online dynamic optimization of carbon emissions.

[0004] To address the aforementioned issues, this invention proposes an intelligent accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process, solving the problems of crude and lagging carbon emission accounting and control, and the difficulty in dynamically and collaboratively optimizing multiple objectives in existing technologies.

[0006] The objective of this invention can be achieved through the following technical solutions: A smart accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process, the system comprising: The global perception and mapping module identifies all production nodes in any production line, constructs them into a set of production nodes according to the production sequence, locks the energy consumption, production rate, and carbon emissions of all production nodes within a preset monitoring period, and constructs a set of energy consumption, production rate, and carbon emissions corresponding to the set of production nodes. The energy efficiency intelligent assessment and optimization module identifies all similar production lines. Based on the energy consumption set, production rate set, and carbon emission set of each production line within the monitoring period, it evaluates the energy consumption values ​​associated with the same production nodes at different production rates, and identifies the optimal energy consumption range and the candidate production rate range associated with the optimal energy consumption range. The dual-carbon coordinated control module determines the set of candidate carbon emissions associated with the candidate production rate range, constructs the optimal energy consumption fluctuation curve and the candidate carbon emission fluctuation curve based on the candidate production rate range, and locks the optimal balance range and the optimal production rate range corresponding to the optimal balance range by combining the energy consumption value and the carbon emission value, and controls the same type of production nodes to execute production in the optimal production rate range.

[0007] As a further aspect of the present invention, the specific method for determining all production nodes in any production line and constructing a set of production nodes according to the production sequence in the global perception mapping module is as follows: Lock onto any production line in the target industrial factory that produces product X, and simultaneously extract all production lines of the same type that produce product X, and randomly arrange them as the production line sequence PL1, PL2, ..., PLj, where X is the preset product type and j is the total number of production lines of the same type; Extract any production line PLi, where i is the counting index, 1≤i≤j; All production nodes in the production line PLi are locked according to the production process and arranged according to the production flow sequence, denoted as the production node set {Pi1,Pi2,...,Pim}, where m is the total number of production nodes in the production line that produces product X. By analogy, determine the set of production nodes associated with each production line in the production line sequence PL1, PL2, ..., PLj.

[0008] As a further aspect of the present invention, the specific method for constructing the energy consumption set, production rate set, and carbon emission set corresponding to the production node set in the global perception mapping module is as follows: Obtain the preset monitoring period T, determine the total number of time points within the monitoring period T, and denote it as o; Get any production node Pin from the set of production nodes {Pi1,Pi2,...,Pim} of the production pipeline PLi, where n is the counting index, 1≤n≤m; Based on the production system built into the target industrial factory, extract the energy consumption and production rate of the production node Pin in the production line PLi at any time within any monitoring period T, and arrange them in chronological order, summarizing them into the energy consumption set {E1,E2,...,Eo} and the production rate set {V1,V2,...,Vo} respectively; Then, based on the pre-deployed smart carbon meters, the carbon emissions of the production node Pin are collected at each moment within the monitoring period T, and arranged in chronological order to be summarized into a carbon emission set {C1,C2,...,Co}. Similarly, the energy consumption set, production rate set, and carbon emission set associated with all production nodes in the production node set {Pi1,Pi2,...,Pim} within any monitoring period T are locked and stored in the pre-built production database.

[0009] As a further aspect of the present invention, the energy efficiency intelligent assessment and optimization module evaluates the energy consumption values ​​associated with the same type of production node at different production rates, and locks in the optimal energy consumption range in the following specific way: Based on the production database, extract the energy consumption set {E1,E2,...,Eo}, production rate set {V1,V2,...,Vo}, and carbon emission set {C1,C2,...,Co} of production node Pin within any monitoring period T; Next, extract all production nodes of the same type as production node Pin on the same production line, and arrange them in the order of production line sequence PL1, PL2, ..., PLj, and summarize them into a set of production nodes of the same type {P1n, P2n, ..., Pjn}; Based on the energy consumption set {E1,E2,...,Eo}, the production rate set {V1,V2,...,Vo}, and the carbon emission set {C1,C2,...,Co}, the average energy consumption E_avg, average production rate V_avg, and average carbon emission C_avg of the production node Pin within the corresponding monitoring period T are determined. Similarly, based on all the data stored in all production nodes of the same type and the production database, lock all the average energy consumption associated with the same type of production nodes at the same average production rate, and take the average of all the average energy consumption, which is recorded as the standard average energy consumption. Arrange all average production rates in ascending order to obtain the average production rate interval [V1_avg,V2_avg,...,Vk_avg], where k is the total number of average production rates. Align all standard average energy consumptions with the average production rate within the average production rate interval to generate a standard average energy consumption interval [BE1_avg,BE2_avg,...,BEk_avg]. Construct a two-dimensional coordinate system with the average production rate value as the horizontal axis and the standard average energy consumption value as the vertical axis, and align the horizontal axis scale with the average production rate range [V1_avg,V2_avg,...,Vk_avg]. Align the standard average energy consumption interval [BE1_avg,BE2_avg,...,BEk_avg] with the average production rate interval [V1_avg,V2_avg,...,Vk_avg] and plot it in a two-dimensional coordinate system in the form of data points to obtain k data points. Fit the curve to it and denote it as the standard average energy consumption fluctuation curve S1. Construct a straight line perpendicular to the vertical axis and parallel to the horizontal axis, denoted as the interval determination line L1; Let the interval determination line L1 start from the 0 mark on the vertical axis and gradually climb towards the positive direction of the vertical axis at a preset constant rate. When the interval determination line L1 climbs to the point where the proportion of the standard average energy consumption fluctuation curve S1 located below the interval determination line L1 is equal to α%, the climbing stops, where α% is a preset proportion. Lock the standard average energy consumption fluctuation curve S1 of the α% part, extract the maximum value BE_max and minimum value BE_min of the standard average energy consumption on the standard average energy consumption fluctuation curve S1, perform merging, and mark it as the optimal energy consumption interval [BE_min, BE_max].

[0010] As a further aspect of the present invention, the specific method for locking the candidate production rate range associated with the optimal energy consumption range in the energy efficiency intelligent assessment and optimization module is as follows: Extract the α% portion of the optimal energy consumption interval [BE_min, BE_max] on the standard average energy consumption fluctuation curve S1; Align the α% portion to the horizontal axis of the two-dimensional coordinate system, determine the horizontal axis scale corresponding to the optimal energy consumption interval [BE_min, BE_max], lock the minimum value BV_min and the maximum value BV_max of the average production rate on the horizontal axis scale, perform merging, and record it as the candidate production rate interval [BV_min, BV_max] associated with the optimal energy consumption interval [BE_min, BE_max].

[0011] As a further aspect of the present invention, the specific method for determining the set of candidate carbon emissions associated with the candidate production rate range in the dual-carbon synergistic regulation module is as follows: Extract the candidate production rate interval [BV_min, BV_max]; Extract all candidate production rates in the candidate production rate interval [BV_min, BV_max], and record the total number as u; Lock all production nodes of the same type as the production node Pin on the same production pipeline line, extract the average carbon emissions of all production nodes of the same type at any candidate production rate from the production database, and take the average of all average carbon emissions as the candidate carbon emissions associated with the corresponding candidate production rate. Similarly, the candidate carbon emissions corresponding to all candidate production rates in the candidate production rate interval [BV_min, BV_max] are locked and arranged in the order from BV_min to BV_max, denoted as the candidate carbon emission set {BC1, BC2, ..., BCu}.

[0012] As a further aspect of the present invention, the specific method for constructing the optimal energy consumption fluctuation curve and the candidate carbon emission fluctuation curve based on the candidate production rate range in the dual-carbon synergistic control module is as follows: Extract candidate production rate intervals [BV_min, BV_max], optimal energy consumption intervals [BE_min, BE_max], and candidate carbon emission sets {BC1, BC2, ..., BCu}; Construct a two-dimensional coordinate system with the candidate production rate values ​​in the candidate production rate interval as the horizontal axis, the optimal energy consumption values ​​in the optimal energy consumption interval as the positive half-axis of the vertical axis, and the candidate carbon emission values ​​in the candidate carbon emission set as the negative half-axis of the vertical axis, and adjust the negative half-axis value to positive. All optimal energy consumption in the optimal energy consumption interval [BE_min, BE_max] are aligned with the candidate production rates on the horizontal axis and plotted as data points in a two-dimensional coordinate system. All data points are then fitted with a curve to obtain the optimal energy consumption fluctuation curve S2. Similarly, based on the candidate carbon emission set {BC1,BC2,...,BCu}, the candidate carbon emission fluctuation curve S3 is plotted in a two-dimensional coordinate system, and S3 and S2 do not overlap.

[0013] As a further aspect of the present invention, the specific method by which the dual-carbon synergistic control module combines energy consumption values ​​and carbon emission values ​​to lock the optimal balance range and the optimal production rate range corresponding to the optimal balance range is as follows: Extract the optimal energy consumption fluctuation curve S2, the candidate carbon emission fluctuation curve S3, and their respective two-dimensional coordinate systems; The optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 are synchronously shifted to the negative half-axis and positive half-axis of the vertical axis, respectively, at a preset constant rate. The overlapping area between the optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 is monitored in real time. When the proportion of the overlapping area in the candidate carbon emission fluctuation curve S3 is equal to β%, the translation is stopped, where β% is a preset proportion. The overlapping region of the β% portion on the candidate carbon emission fluctuation curve S3 is identified as the optimal equilibrium interval. Then, in the two-dimensional coordinate system, determine the maximum value GV_max and the minimum value GV_min of the candidate production rate on the horizontal axis corresponding to the optimal equilibrium interval, and merge them, denoted as the optimal production rate interval [GV_min, GV_max].

[0014] The beneficial effects of this invention are: (1) By constructing a digital mapping of the entire production process, this invention can achieve real-time and accurate locking of energy consumption, rate and carbon emissions at each production node, transforming the original extensive carbon emission accounting into refined dynamic management based on smart carbon meters; by using the energy efficiency intelligent evaluation and optimization module to make horizontal comparisons of similar production lines, it can discover the optimal energy consumption range under different production rates, overcoming the limitations of the static energy efficiency standard of a single device, and providing dynamic optimization capabilities based on actual operating data for energy saving and consumption reduction; by coupling the fluctuation curves of energy consumption and carbon emissions, the dual carbon collaborative control module locks the balance range that takes into account the optimal energy efficiency and the lowest carbon emissions, and finally outputs the executable optimal production rate, actively achieving the dual goals of energy saving and carbon reduction by controlling the production rhythm, while ensuring production efficiency and improving the energy utilization level and carbon emission control accuracy of the entire process; (2) This invention collects data from each independent production node on the production line at high frequency and in multiple dimensions during each monitoring cycle. It not only acquires time series data of energy consumption and production rate at the same time, but also accurately captures carbon emissions at the corresponding time with the help of smart carbon meters. This creates a refined data set covering the entire production process, realizes real-time mapping and quantification of energy-efficiency-carbon in the industrial production process, decomposes production line energy consumption and carbon emissions to each specific node and time, and provides data granularity for subsequent energy efficiency diagnosis, carbon footprint tracking and anomaly location. This makes it easier to identify bottleneck links with high energy consumption, high emissions or low efficiency, which is conducive to the green and digital transformation of industrial manufacturing. (3) This invention transforms the complex problem of energy conservation and carbon reduction into an intuitive and quantifiable mathematical analysis through a data-driven approach. First, by deeply mining and standardizing the energy consumption, rate and carbon emission data of the same production node, the random errors of single equipment or short-term operation are eliminated, and a standard average energy consumption fluctuation curve with universal guiding significance is constructed. By introducing a dynamically rising interval determination line, the operating area with the lowest energy consumption is accurately locked, thereby defining the optimal energy consumption interval, which changes the ambiguity of setting target values ​​based on experience in the traditional method. Based on this, the candidate production rate interval corresponding to the optimal energy consumption interval is derived in reverse, providing the production line with the best operating window that can ensure high output and achieve energy conservation and carbon reduction. It realizes the dual optimal solution of energy efficiency and production efficiency, and provides clear and executable data support for the enterprise's refined management and technological transformation, thereby effectively reducing carbon emissions in actual production. (4) By constructing a two-dimensional coordinate system and synchronously translating the energy consumption and carbon emission fluctuation curves to find the overlapping area of ​​the two, the present invention achieves dynamic balance and synergistic optimization among production rate, energy consumption and carbon emission. It can accurately locate an optimal balance range that satisfies both optimal energy consumption and carbon emission control, thereby transforming the abstract dual-carbon goal into a specific and executable production rate range. This not only avoids the loss of balance caused by pursuing the lowest energy consumption or the lowest carbon emission, but also makes the control process scientific and precise through quantitative curve translation and proportion monitoring. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a schematic diagram of the system described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 3 of the present invention; Figure 3 This is a schematic diagram of the standard average energy consumption fluctuation curve S1 and the optimal energy consumption range calibration described in Embodiment 5 of the present invention; Figure 4 This is a schematic diagram of the combination of the optimal energy consumption curve S2 and the candidate carbon emission curve S3 in Embodiment 5 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, this application provides an intelligent accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process; As an embodiment 1 of this application, it specifically includes: The global perception and mapping module identifies all production nodes in any production line, constructs them into a set of production nodes according to the production sequence, locks the energy consumption, production rate, and carbon emissions of all production nodes within a preset monitoring period, and constructs a set of energy consumption, production rate, and carbon emissions corresponding to the set of production nodes. The energy efficiency intelligent assessment and optimization module identifies all similar production lines. Based on the energy consumption set, production rate set, and carbon emission set of each production line within the monitoring period, it evaluates the energy consumption values ​​associated with the same production nodes at different production rates, and identifies the optimal energy consumption range and the candidate production rate range associated with the optimal energy consumption range. The dual-carbon coordinated control module determines the set of candidate carbon emissions associated with the candidate production rate range, constructs the optimal energy consumption fluctuation curve and the candidate carbon emission fluctuation curve based on the candidate production rate range, and locks the optimal balance range and the optimal production rate range corresponding to the optimal balance range by combining the energy consumption value and the carbon emission value, and controls the same type of production nodes to execute production in the optimal production rate range.

[0019] Example 2 This embodiment, based on embodiment 1, further discloses a data acquisition and structured mapping method involved in the global perception mapping module, specifically including the following: First, it is necessary to define the production topology and identify the product X to be analyzed, where X is the product type preset by the operator. Identify all production lines of the same type that produce product X within the target industrial plant of the enterprise. The same type of production line means that all production processes are the same and the production equipment used in the production line is also the same. All production lines are randomly sorted, and the sorting result is denoted as the production line sequence PL1, PL2, ..., PLj, where j is the total number of production lines of the same type that produce product X. Subsequent operations are based on the production line sequence PL1, PL2, ..., PLj for further explanation and description. Extract any one production line from the production line sequence PL1, PL2, ..., PLj and label it as PLi, where i is the counting index, 1≤i≤j; The following is an example of processing production line PLi. Other production lines will be processed in the same and synchronous manner as production line PLi. Next, the production line PLi is decomposed according to the production process to determine all the production nodes in the production line PLi, such as: material feeding node, processing node, spraying node, packaging node, etc. Next, arrange all production nodes in the production line PLi according to the order of the production process. The result of the arrangement is denoted as the production node set {Pi1,Pi2,...,Pim}, where represents the total number of production nodes in the production line that produces product X. Simultaneously process other production lines to determine the set of production nodes associated with all production lines producing product X.

[0020] Next, the monitoring period T preset by the operator is obtained, and the monitoring period T is divided into sections. All moments within the monitoring period T are determined, and the total number of moments is recorded as o. The total number of moments corresponds to the collection frequency of all subsequent data.

[0021] The production line PLi is used as an example for processing; Extract the set of production nodes {Pi1,Pi2,...,Pim} from the production pipeline PLi, and further extract any one production node Pin from the set of production nodes {Pi1,Pi2,...,Pim}, where n is the counting index, 1≤n≤m. The following is an example of processing production node Pin. All other production nodes in the set of production nodes {Pi1,Pi2,...,Pim} are processed in the same synchronous manner as production node Pin.

[0022] The system identifies the production system built into the target industrial plant and extracts the energy consumption and production rate of the production node Pin in the production line PLi at any time during any monitoring period T. The extracted energy consumption and production rate are then sorted in chronological order, and the sorted results are denoted as the energy consumption set {E1, E2, ..., Eo} and the production rate set {V1, V2, ..., Vo}, respectively. Then, the smart carbon meters pre-deployed by the operators at each production node collect the carbon emissions of the production node Pin at each moment within the monitoring period T corresponding to the energy consumption set {E1,E2,...,Eo} and the production rate set {V1,V2,...,Vo}. The collected carbon emissions are sorted in chronological order, and the sorted results are summarized as the carbon emission set {C1,C2,...,Co}. Following the steps above, process the energy consumption set, production rate set, and carbon emission set associated with all production nodes in the production node set {Pi1,Pi2,...,Pim} within any monitoring period T, and store them in the production database pre-built by the operator for subsequent retrieval. It is important to note that other production lines of the same type are also processed in the same way as the production line PLi and the set of production nodes {Pi1,Pi2,...,Pim} in the production line PLi. Finally, the energy consumption set, production rate set, and carbon emission set associated with each production node in any monitoring period T are locked and stored in the production database to complete the persistent storage of historical data.

[0023] Example 3 This embodiment, based on embodiment 2, further discloses an energy efficiency assessment and optimization method involved in the energy efficiency intelligent assessment and optimization module, such as... Figure 2 As shown, it specifically includes the following: Obtain the production database described in Example 2, and extract from the production database the energy consumption set {E1,E2,...,Eo}, the production rate set {V1,V2,...,Vo}, and the carbon emission set {C1,C2,...,Co} of the production node Pin in any monitoring period T; Then, extract all production nodes of the same type as production node Pin (e.g., assembly nodes of the same type) under the same production line. Then, arrange all production nodes of the same type as production node Pin (including production node Pin) in the order of production line sequence PL1, PL2, ..., PLj. For example, if production node P1n belongs to production line PL1, then production node P1n will be ranked first. Thus, we obtain a set of production nodes of the same type as production node Pin, {P1n, P2n, ..., Pjn}. Next, the production node Pin is processed by averaging its energy consumption set {E1,E2,...,Eo}, production rate set {V1,V2,...,Vo}, and carbon emission set {C1,C2,...,Co} to lock the average energy consumption E_avg, average production rate V_avg, and average carbon emission C_avg of the production node Pin within the corresponding monitoring period T.

[0024] It should be clarified that the above averaging process is an arithmetic average of the data of a specific production node Pin on a specific production line PLi within a specific monitoring period T. That is, the average energy consumption E_avg = (E1 + E2 + ... + Eo) / o; Average production rate V_avg = (V1 + V2 + ... + Vo) / o; Average carbon emissions C_avg = (C1 + C2 + ... + Co) / o; Therefore, each production line, production node, and monitoring cycle triplet can yield a unique set of average energy consumption E_avg, average production rate V_avg, and average carbon emissions C_avg.

[0025] In subsequent steps, all average calculations are based on this and involve higher-level aggregation.

[0026] For example, for a set of production nodes of the same type {P1n,P2n,...,Pjn}, when it is necessary to calculate all the average energy consumption associated with the same average production rate, first extract all records that satisfy the average production rate V_avg equal to a certain specific value or fall into a certain minimum neighborhood from the historical monitoring period of each production line. These records come from different production lines and different monitoring cycles, but they all belong to the same type of node. Then, the arithmetic mean of the average production rate V_avg in these records is taken to obtain the standard average energy consumption at that production rate.

[0027] Similarly, the average carbon emissions C_avg are averaged to obtain the standard average carbon emissions.

[0028] In this way, data from multiple production lines and multiple monitoring cycles are aligned and integrated into a single production rate dimension.

[0029] It should also be noted that the monitoring period T in this plan is set by the operator based on the actual situation. Generally, the production rate is kept constant within a monitoring period T.

[0030] Next, process all similar production nodes in the set of similar production nodes {P1n,P2n,...,Pjn}, and extract all data stored in the production database for all similar production nodes, namely the historical energy consumption set, production rate set, and carbon emission set. In this way, a large amount of data is obtained. All data is processed in the same way as the production node Pin. Finally, we can obtain all the average energy consumption associated with the same type of production node Pin at the same average production rate. In other words, we can obtain all the average energy consumption associated with the same type of production node at different average production rates. Next, the average energy consumption associated with the same average production rate is averaged and recorded as the standard average energy consumption associated with the production node of that type at the corresponding average production rate. Repeat the above steps to process the carbon emission set of this type of production node in the same way. Finally, we can obtain all the average carbon emissions associated with this type of production node at different average production rates. The average carbon emissions are averaged and recorded as the standard average carbon emissions associated with the corresponding average production rate. It should be noted that the subsequent processing will also involve candidate production rates and candidate carbon emissions. The candidate production rates are taken from the average production rates, and the candidate carbon emissions are taken from the standard average carbon emissions. Next, arrange all average production rates in ascending order to obtain the average production rate interval [V1_avg,V2_avg,...,Vk_avg], where k is the total number of average production rates; It should be added that the average production rate interval [V1_avg,V2_avg,...,Vk_avg] mentioned in this scheme refers to the production rate of the corresponding production node under normal operating conditions. The production rate under abnormal operating conditions and its corresponding energy consumption and carbon emissions are not included in the above calculations. Therefore, the average production rate interval [V1_avg,V2_avg,...,Vk_avg] can ensure that the corresponding production node is in normal operating condition.

[0031] Then, align all standard average energy consumptions with the average production rate within the average production rate interval to generate the standard average energy consumption interval [BE1_avg,BE2_avg,...,BEk_avg]. Then, establish a two-dimensional coordinate system with the average production rate as the horizontal axis and the standard average energy consumption as the vertical axis. The scale on the horizontal axis of the two-dimensional coordinate system is aligned with the average production rate interval [V1_avg,V2_avg,...,Vk_avg]. Extract the standard average energy consumption interval [BE1_avg,BE2_avg,...,BEk_avg] and align it with the average production rate interval [V1_avg,V2_avg,...,Vk_avg]. Plot the data points in a two-dimensional coordinate system, where the horizontal axis represents the average production rate and the vertical axis represents the standard average energy consumption. This yields k data points. By fitting these k data points to a curve, a complete curve is obtained, denoted as the standard average energy consumption fluctuation curve S1 associated with the corresponding type of production node within the average production rate interval. Actual industrial data is often not strictly linear; curve fitting can more accurately describe the trend of energy consumption changing with the rate. Then, construct a straight line perpendicular to the vertical axis and parallel to the horizontal axis in a two-dimensional coordinate system, and mark this line as the interval determination line L1; The interval determination line L1 starts from the 0 mark on the vertical axis and gradually climbs towards the positive direction of the vertical axis at a constant rate preset by the operator. The climbing position of the interval determination line L1 is monitored in real time until it intersects with the standard average energy consumption fluctuation curve S1. Then, the proportion of the portion of the standard average energy consumption fluctuation curve S1 below the interval determination line L1 is determined. When the proportion equals α%, the interval determination line L1 stops climbing. Here, α% is the proportion preset by the operator, representing the desired optimal energy consumption portion. This avoids the operator's subjective determination of energy consumption thresholds and automatically determines a statistically optimal area through data distribution, making it more objective and adaptable. Furthermore, the operator can update α% or update the time for re-determining the optimal energy consumption portion, for example, updating it once a month to re-determine the optimal energy consumption portion, dynamically adapting to energy efficiency benchmark drift caused by factors such as equipment aging and process changes.

[0032] Next, the standard average energy consumption fluctuation curve S1 of the α% portion is locked, and the maximum value BE_max and minimum value BE_min of the standard average energy consumption in this α% portion are extracted and merged to obtain the optimal energy consumption interval [BE_min, BE_max].

[0033] Next, the optimal energy consumption range [BE_min, BE_max] is determined to correspond to the α% part of the standard average energy consumption fluctuation curve S1. Each data point in this α% part corresponds to a production rate and an energy consumption value that falls within the optimal energy consumption range, which together constitute the set of operating points with optimal energy efficiency. Aligning the α% portion to the horizontal axis of a two-dimensional coordinate system and performing projection yields the horizontal axis scale corresponding to the α% portion, which is also the horizontal axis scale corresponding to the optimal energy consumption interval [BE_min, BE_max]. From this horizontal axis scale, the minimum value BV_min and the maximum value BV_max of the average production rate are locked and merged, denoted as the candidate production rate interval [BV_min, BV_max] associated with the optimal energy consumption interval [BE_min, BE_max]. The candidate production rate interval [BV_min, BV_max] is the recommended range of production rates that enables the corresponding production node to achieve optimal energy efficiency. In this way, the abstract optimal energy consumption can be transformed into specific and actionable production rate suggestions through a data-driven approach.

[0034] Example 4 This embodiment, based on embodiment 3, further discloses a dual-carbon synergistic balance method involved in the dual-carbon synergistic regulation module, specifically including the following: The candidate production rate range and candidate carbon emission set involved in this embodiment are based on the same type of production nodes that have been identified in Embodiment 3, and all average carbon emissions are derived from the measured data of the same type of nodes in each production line and each monitoring period.

[0035] Therefore, the optimal production rate range obtained can be directly applied to the actual production control of the corresponding type of node. It is not related to the dynamic changes of a specific production line or a specific monitoring cycle, but is a general optimization range based on a large amount of historical data.

[0036] In practical applications, this range is recalculated periodically to accommodate long-term changes such as equipment aging and process adjustments.

[0037] Based on the content described in Example 3, the candidate production rate interval [BV_min, BV_max] is extracted, and all discrete candidate production rates are extracted from it. The total number is counted and denoted as u. Next, lock all production nodes of the same type as production node Pin in the production pipeline (including production node Pin), and extract several average carbon emissions corresponding to all production nodes of the same type at any candidate production rate from the production database, and perform averaging on several average carbon emissions. The result is recorded as the candidate carbon emissions associated with the corresponding candidate production rate, as described in Example 2. In this way, we can determine the candidate carbon emissions corresponding to all candidate production rates in the candidate production rate interval [BV_min, BV_max], totaling u candidate carbon emissions. We then arrange and summarize the u candidate carbon emissions in the order from BV_min to BV_max, and the summarized result is denoted as the candidate carbon emission set {BC1, BC2, ..., BCu}. This completes the alignment process between carbon emission data and production rate.

[0038] Then, based on the candidate production rate range, an optimal energy consumption fluctuation curve and a candidate carbon emission fluctuation curve are constructed, as follows: Based on the above steps, the constructed candidate production rate interval [BV_min, BV_max], optimal energy consumption interval [BE_min, BE_max], and candidate carbon emission set {BC1, BC2, ..., BCu} are extracted. A new two-dimensional coordinate system is then constructed (hereinafter, all two-dimensional coordinate systems refer to this coordinate system). The horizontal axis of the two-dimensional coordinate system corresponds to the candidate production rate value in the candidate production rate range, the positive half-axis of the vertical axis corresponds to the optimal energy consumption value in the optimal energy consumption range, and the negative half-axis of the vertical axis corresponds to the candidate carbon emission value in the candidate carbon emission set. At the same time, the value of the negative half-axis of the vertical axis is adjusted to be positive so as to compare carbon emissions and energy consumption in the same two-dimensional coordinate system. In addition, it can also visually display the trend of energy consumption and carbon emissions changing with production rate for operators.

[0039] Next, align all the optimal energy consumption data in the optimal energy consumption interval [BE_min,BE_max] with the candidate production rates on the horizontal axis (the same as the total number of discrete candidate production rates in the candidate production rate interval [BV_min,BV_max], totaling u), and plot all the candidate production rates in the form of data points in a two-dimensional coordinate system. By fitting all the plotted data points with a curve, the optimal energy consumption fluctuation curve S2 is obtained. Next, align all candidate carbon emission data in the candidate carbon emission set {BC1,BC2,...,BCu} with the candidate production rate on the horizontal axis, plot them in the form of data points in a two-dimensional coordinate system, and fit them with a curve to obtain the candidate carbon emission fluctuation curve S3. At this time, S3 and S2 do not overlap because S2 is in the first quadrant and S3 is in the fourth quadrant.

[0040] Then extract the two-dimensional coordinate system and the optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 within the two-dimensional coordinate system; The following operations are performed on the optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3; The optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 are synchronously shifted towards each other at a constant rate preset by the operator. Specifically, the optimal energy consumption fluctuation curve S2 is shifted towards the negative half-axis of the vertical axis, and the candidate carbon emission fluctuation curve S3 is shifted towards the positive half-axis of the vertical axis. During the translation process, the overlapping area (overlapping part) of the optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 is monitored in real time. When the proportion of the overlapping area in the candidate carbon emission fluctuation curve S3 is equal to β%, the translation operation of the optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 is stopped. Here, β% is the proportion preset by the operator. The larger the β% is, the more overlapping area there is, which means that the translation process requires a larger translation amount.

[0041] The area overlapping with the β% portion of the candidate carbon emission fluctuation curve S3 and the optimal energy consumption fluctuation curve S2 is defined as the optimal equilibrium interval. Then, the β% part is projected onto the horizontal axis of the two-dimensional coordinate system to obtain a curve. The maximum value GV_max and the minimum value GV_min of the candidate production rate corresponding to the horizontal axis scale within this interval are locked, and a merging operation is performed to finally obtain a production rate interval, which is denoted as the optimal production rate interval [GV_min, GV_max].

[0042] It should be added that after the operator obtains the optimal production rate range [GV_min, GV_max], they can combine the optimal production rate range [GV_min, GV_max] to adjust the actual production nodes in order to achieve dual-carbon synergistic optimization.

[0043] Example 5 This embodiment, based on the foregoing embodiments, further provides an implementation example including specific numerical calculation processes and curve image presentation methods, to more clearly illustrate the specific operational procedures for data aggregation from the production line-production node-monitoring cycle triplet to the production rate dimension, curve fitting, and interval calibration, as follows: In this embodiment, taking a certain type of assembly node as an example, the assembly node is denoted as node A; There are three identical production lines, namely PL1, PL2 and PL3, each of which includes node A.

[0044] If the monitoring period is selected as T=1 hour and the sampling frequency is once per minute, then o=60.

[0045] During a certain monitoring period, 60 energy consumption and production rate values ​​were collected from node A of PL1. The average energy consumption E_avg=2.5, the average production rate V_avg=120, and the average carbon emissions C_avg=1.8 were calculated and denoted as (2.5,120,1.8). Similarly, the data for PL2 within the same monitoring period are (2.6, 118, 1.9), and for PL3 it is (2.4, 122, 1.7). By summarizing the data from multiple continuous monitoring periods, such as 10 periods, multiple average energy consumption values ​​corresponding to different average production rates can be obtained. The average energy consumption at the same rate is taken to obtain the standard average energy consumption; carbon emissions are treated in the same way.

[0046] The final standard average energy consumption sequence is determined and arranged in ascending order of average production rate as follows: average production rate 110 corresponds to standard average energy consumption of 2.8, average production rate 115 corresponds to standard average energy consumption of 2.6, average production rate 120 corresponds to standard average energy consumption of 2.4, average production rate 125 corresponds to standard average energy consumption of 2.5, and average production rate 130 corresponds to standard average energy consumption of 2.7.

[0047] The corresponding standard average carbon emission sequence, arranged from smallest to largest average production rate, is as follows: average production rate 110 corresponds to standard average carbon emission of 2.0, average production rate 115 corresponds to standard average carbon emission of 1.9, average production rate 120 corresponds to standard average carbon emission of 1.8, average production rate 125 corresponds to standard average carbon emission of 1.9, and average production rate 130 corresponds to standard average carbon emission of 2.1.

[0048] Next, a scatter plot was drawn with production rate as the horizontal axis and standard average energy consumption as the vertical axis, and cubic spline interpolation was used to fit the plot to obtain the standard average energy consumption fluctuation curve S1.

[0049] Set α=30%, that is, select the 30% portion with the lowest energy consumption.

[0050] Let the interval defining line L1 move upward from the vertical axis 0, and stop when the proportion of the curve below L1 reaches 30%.

[0051] At this point, the energy consumption range corresponding to this part of the curve is the optimal energy consumption interval.

[0052] The production rate range corresponding to the optimal energy consumption range is denoted as the candidate production rate range.

[0053] Within the candidate production rate range, the standard average carbon emissions corresponding to each rate are extracted to obtain the candidate carbon emission set.

[0054] Construct a two-dimensional coordinate system, with the horizontal axis representing production rate, the positive half of the vertical axis representing energy consumption (i.e., the optimal energy consumption range), and the negative half of the vertical axis representing carbon emissions, taking their absolute value.

[0055] Plot the optimal energy consumption fluctuation curve S2 in the first quadrant and the candidate carbon emission fluctuation curve S3 in the fourth quadrant, as follows: Figure 4 As shown.

[0056] Set β=50%, meaning the overlapping region accounts for 50% of S3.

[0057] Shift S2 downwards and S3 upwards until the overlapping area accounts for 50% of S3. At this point, the projection of the overlapping area onto the horizontal axis is the optimal production rate range.

[0058] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0059] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0060] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. An intelligent accounting and dynamic adjustment system for carbon emissions throughout the entire industrial production process, characterized in that: The system includes: The global perception and mapping module identifies all production nodes in any production line, constructs them into a set of production nodes according to the production sequence, locks the energy consumption, production rate, and carbon emissions of all production nodes within a preset monitoring period, and constructs a set of energy consumption, production rate, and carbon emissions corresponding to the set of production nodes. The energy efficiency intelligent assessment and optimization module identifies all similar production lines. Based on the energy consumption set, production rate set, and carbon emission set of each production line within the monitoring period, it evaluates the energy consumption values ​​associated with the same production nodes at different production rates, and identifies the optimal energy consumption range and the candidate production rate range associated with the optimal energy consumption range. The dual-carbon coordinated control module determines the set of candidate carbon emissions associated with the candidate production rate range, constructs the optimal energy consumption fluctuation curve and the candidate carbon emission fluctuation curve based on the candidate production rate range, and locks the optimal balance range and the optimal production rate range corresponding to the optimal balance range by combining the energy consumption value and the carbon emission value, and controls the same type of production nodes to execute production in the optimal production rate range.

2. The system according to claim 1, characterized in that, In the global perception mapping module, the specific method for determining all production nodes in any production line and constructing a set of production nodes according to the production sequence is as follows: Lock onto any production line in the target industrial factory that produces product X, and simultaneously extract all production lines of the same type that produce product X, and randomly arrange them as the production line sequence PL1, PL2, ..., PLj, where X is the preset product type and j is the total number of production lines of the same type; Extract any production line PLi, where i is the counting index, 1≤i≤j; All production nodes in the production line PLi are locked according to the production process and arranged according to the production flow sequence, denoted as the production node set {Pi1,Pi2,...,Pim}, where m is the total number of production nodes in the production line that produces product X. By analogy, determine the set of production nodes associated with each production line in the production line sequence PL1, PL2, ..., PLj.

3. The system according to claim 2, characterized in that, In the global perception mapping module, the specific method for constructing the energy consumption set, production rate set, and carbon emission set corresponding to the production node set is as follows: Obtain the preset monitoring period T, determine the total number of time points within the monitoring period T, and denote it as o; Get any production node Pin from the set of production nodes {Pi1,Pi2,...,Pim} of the production pipeline PLi, where n is the counting index, 1≤n≤m; Based on the production system built into the target industrial factory, extract the energy consumption and production rate of the production node Pin in the production line PLi at any time within any monitoring period T, and arrange them in chronological order, summarizing them into the energy consumption set {E1,E2,...,Eo} and the production rate set {V1,V2,...,Vo} respectively; Then, based on the pre-deployed smart carbon meters, the carbon emissions of the production node Pin are collected at each moment within the monitoring period T, and arranged in chronological order to be summarized into a carbon emission set {C1,C2,...,Co}. Similarly, the energy consumption set, production rate set, and carbon emission set associated with all production nodes in the production node set {Pi1,Pi2,...,Pim} within any monitoring period T are locked and stored in the pre-built production database.

4. The system according to claim 3, characterized in that, The energy efficiency intelligent assessment and optimization module evaluates the energy consumption values ​​associated with the same type of production node at different production rates, and the specific method for locking in the optimal energy consumption range is as follows: Based on the production database, extract the energy consumption set {E1,E2,...,Eo}, production rate set {V1,V2,...,Vo}, and carbon emission set {C1,C2,...,Co} of production node Pin within any monitoring period T; Next, extract all production nodes of the same type as production node Pin on the same production line, and arrange them in the order of production line sequence PL1, PL2, ..., PLj, and summarize them into a set of production nodes of the same type {P1n, P2n, ..., Pjn}; Based on the energy consumption set {E1,E2,...,Eo}, the production rate set {V1,V2,...,Vo}, and the carbon emission set {C1,C2,...,Co}, the average energy consumption E_avg, average production rate V_avg, and average carbon emission C_avg of the production node Pin within the corresponding monitoring period T are determined. Similarly, based on all the data stored in all production nodes of the same type and the production database, lock all the average energy consumption associated with the same type of production nodes at the same average production rate, and take the average of all the average energy consumption, which is recorded as the standard average energy consumption. Arrange all average production rates in ascending order to obtain the average production rate interval [V1_avg,V2_avg,...,Vk_avg], where k is the total number of average production rates. Align all standard average energy consumptions with the average production rate within the average production rate interval to generate a standard average energy consumption interval [BE1_avg,BE2_avg,...,BEk_avg]. Construct a two-dimensional coordinate system with the average production rate value as the horizontal axis and the standard average energy consumption value as the vertical axis, and align the horizontal axis scale with the average production rate range [V1_avg,V2_avg,...,Vk_avg]. Align the standard average energy consumption interval [BE1_avg,BE2_avg,...,BEk_avg] with the average production rate interval [V1_avg,V2_avg,...,Vk_avg] and plot it in a two-dimensional coordinate system in the form of data points to obtain k data points. Fit the curve to it and denote it as the standard average energy consumption fluctuation curve S1. Construct a straight line perpendicular to the vertical axis and parallel to the horizontal axis, denoted as the interval determination line L1; Let the interval determination line L1 start from the 0 mark on the vertical axis and gradually climb towards the positive direction of the vertical axis at a preset constant rate. When the interval determination line L1 climbs to the point where the proportion of the standard average energy consumption fluctuation curve S1 located below the interval determination line L1 is equal to α%, the climbing stops, where α% is a preset proportion. Lock the standard average energy consumption fluctuation curve S1 of the α% part, extract the maximum value BE_max and minimum value BE_min of the standard average energy consumption on the standard average energy consumption fluctuation curve S1, perform merging, and mark it as the optimal energy consumption interval [BE_min, BE_max].

5. The system according to claim 4, characterized in that, In the energy efficiency intelligent assessment and optimization module, the specific method for locking the candidate production rate range associated with the optimal energy consumption range is as follows: Extract the α% portion of the optimal energy consumption interval [BE_min, BE_max] on the standard average energy consumption fluctuation curve S1; Align the α% portion to the horizontal axis of the two-dimensional coordinate system, determine the horizontal axis scale corresponding to the optimal energy consumption interval [BE_min, BE_max], lock the minimum value BV_min and the maximum value BV_max of the average production rate on the horizontal axis scale, perform merging, and record it as the candidate production rate interval [BV_min, BV_max] associated with the optimal energy consumption interval [BE_min, BE_max].

6. The system according to claim 5, characterized in that, In the aforementioned dual-carbon synergistic regulation module, the specific method for determining the set of candidate carbon emissions associated with the candidate production rate range is as follows: Extract the candidate production rate interval [BV_min, BV_max]; Extract all candidate production rates in the candidate production rate interval [BV_min, BV_max], and record the total number as u; Lock all production nodes of the same type as the production node Pin on the same production pipeline line, extract the average carbon emissions of all production nodes of the same type at any candidate production rate from the production database, and take the average of all average carbon emissions as the candidate carbon emissions associated with the corresponding candidate production rate. Similarly, the candidate carbon emissions corresponding to all candidate production rates in the candidate production rate interval [BV_min, BV_max] are locked and arranged in the order from BV_min to BV_max, denoted as the candidate carbon emission set {BC1, BC2, ..., BCu}.

7. The system according to claim 6, characterized in that, In the aforementioned dual-carbon synergistic control module, the specific method for constructing the optimal energy consumption fluctuation curve and the candidate carbon emission fluctuation curve based on the candidate production rate range is as follows: Extract candidate production rate intervals [BV_min, BV_max], optimal energy consumption intervals [BE_min, BE_max], and candidate carbon emission sets {BC1, BC2, ..., BCu}; Construct a two-dimensional coordinate system with the candidate production rate values ​​in the candidate production rate interval as the horizontal axis, the optimal energy consumption values ​​in the optimal energy consumption interval as the positive half-axis of the vertical axis, and the candidate carbon emission values ​​in the candidate carbon emission set as the negative half-axis of the vertical axis, and adjust the negative half-axis value to positive. All optimal energy consumption in the optimal energy consumption interval [BE_min, BE_max] are aligned with the candidate production rates on the horizontal axis and plotted as data points in a two-dimensional coordinate system. All data points are then fitted with a curve to obtain the optimal energy consumption fluctuation curve S2. Similarly, based on the candidate carbon emission set {BC1,BC2,...,BCu}, the candidate carbon emission fluctuation curve S3 is plotted in a two-dimensional coordinate system, and S3 and S2 do not overlap.

8. The system according to claim 7, characterized in that, In the aforementioned dual-carbon synergistic control module, the specific method for locking the optimal equilibrium range and the corresponding optimal production rate range by combining energy consumption and carbon emission values ​​is as follows: Extract the optimal energy consumption fluctuation curve S2, the candidate carbon emission fluctuation curve S3, and their respective two-dimensional coordinate systems; The optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 are synchronously shifted to the negative half-axis and positive half-axis of the vertical axis, respectively, at a preset constant rate. The overlapping area between the optimal energy consumption fluctuation curve S2 and the candidate carbon emission fluctuation curve S3 is monitored in real time. When the proportion of the overlapping area in the candidate carbon emission fluctuation curve S3 is equal to β%, the translation is stopped, where β% is a preset proportion. The overlapping region of the β% portion on the candidate carbon emission fluctuation curve S3 is identified as the optimal equilibrium interval. Then, in the two-dimensional coordinate system, determine the maximum value GV_max and the minimum value GV_min of the candidate production rate on the horizontal axis corresponding to the optimal equilibrium interval, and merge them, denoted as the optimal production rate interval [GV_min, GV_max].

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