Budget analysis linear programming method and system

Abstract

The invention discloses a budget analysis linear programming method and system, and relates to the technical field of budget analysis. The method comprises the steps of collecting energy analysis parameters and external energy environment parameters, performing association binding on the energy analysis parameters and the external energy environment parameters, generating a dynamic perception energy budget parameter set, performing multi-target hierarchical constraint, constructing an energy budget optimization model, obtaining real-time production data, and solving and outputting a theoretical energy budget distribution optimal solution through the energy budget optimization model. The method comprises the following steps: calculating an energy budget deviation rate, correcting the energy budget deviation rate, generating a corrected energy budget distribution optimal solution, extracting worst and optimal scene parameter boundary values from a dynamically perceived energy budget parameter set, combining the corrected energy budget distribution optimal solution, calculating a comprehensive energy efficiency extreme value under two scenes, and forming an optimal energy budget distribution solution. According to the method, the dynamic adaptability, the risk resistance and the execution efficiency of the budget scheme can be improved, and a budget management solution with scientificity and operability is provided for enterprises.

Description

Technical Field

[0001] This invention relates to the field of budget analysis technology, and in particular to a linear programming method and system for budget analysis. Background Technology

[0002] In the industrial production sector, energy costs are a core component of enterprise operating costs, and the scientific allocation and management of energy is crucial for achieving cost reduction, efficiency improvement, and green, low-carbon development. Traditional industrial energy budgeting methods often rely on static historical data averaging or simple proportional allocation, making it difficult to cope with dynamic and complex production environments. This approach typically suffers from the following technical shortcomings: First, its budget parameters are fixed, failing to respond in real-time to the impact of dynamic external factors such as grid time-of-use pricing, equipment energy efficiency degradation, and changes in ambient temperature, leading to a disconnect between the budget and actual demand, resulting in soaring energy costs or energy waste. Second, its optimization objective is singular, often focusing solely on minimizing total cost, making it difficult to consider multiple dimensions and potentially conflicting technical objectives such as production assurance, carbon emission constraints, and load stability. Third, the model lacks quantitative assessment of the uncertainty of key parameters (such as electricity prices and equipment efficiency), resulting in budget schemes with poor robustness and weak risk resistance under extreme market or operating condition fluctuations. Furthermore, the lack of a closed-loop deviation perception and dynamic adjustment mechanism from budget preparation to execution makes it difficult to promptly and accurately correct energy allocation strategies when actual production conditions change. The aforementioned problems result in energy budget schemes generated by traditional methods lacking scientific rigor and practical feasibility, failing to provide effective support for the refined and intelligent management of modern industrial energy systems. Therefore, there is an urgent need for an energy budget planning method that can integrate multi-source dynamic data, perform multi-objective collaborative optimization, and possess dynamic adjustment and risk assessment capabilities. Summary of the Invention

[0003] This invention provides a linear programming method for budget analysis, comprising: Step S1: Collect energy analysis parameters and external energy environment parameters of the target industrial production system; Step S2: Link and bind energy analysis parameters with external energy and environmental parameters to generate a dynamically sensed energy budget parameter set; Step S3: Apply multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set and construct an energy budget optimization model; Step S4: Obtain real-time production data, solve the theoretical optimal energy budget allocation solution through the energy budget optimization model, correct it by calculating the energy budget deviation rate, and generate the corrected optimal energy budget allocation solution. Step S5: Extract the boundary values ​​of worst-case and best-case parameters from the dynamically sensed energy budget parameter set, combine them with the corrected optimal solution for energy budget allocation, and calculate the comprehensive energy efficiency extreme values ​​under the two scenarios using the boundary comprehensive allocation algorithm. Step S6: Combine the extreme values ​​of comprehensive energy efficiency under the two scenarios with the optimal solution of modified energy budget allocation to form the optimal energy budget allocation solution.

[0004] The linear programming method for budget analysis described above, wherein associating and binding energy analysis parameters with external energy and environmental parameters to generate a dynamically sensed energy budget parameter set includes the following sub-steps: Step S21: Obtain multi-dimensional dynamic correlation factors, and link and bind energy analysis parameters with external energy and environmental parameters to form an energy budget parameter set; Step S22: Set up a triple update trigger based on time period, external event threshold and early warning signal for the energy budget parameter set to generate a dynamically perceived energy budget parameter set.

[0005] The linear programming method for budget analysis described above, which involves applying multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set and constructing an energy budget optimization model, includes the following sub-steps: Step S31: Construct a multi-combination objective function based on the dynamically perceived energy budget parameter set; Step S32: Obtain production plans and safe operation preferences, and construct hierarchical constraints by combining them with the dynamically perceived energy budget parameter set.

[0006] The linear programming method for budget analysis described above, which involves acquiring real-time production data, solving for the theoretical optimal energy budget allocation using an energy budget optimization model, and correcting the solution by calculating the energy budget deviation rate to generate a corrected optimal energy budget allocation, includes the following sub-steps: Step S41: Obtain the optimal solution for theoretical energy budget allocation through the energy budget model; Step S42: Based on historical energy budget data, predict the execution deviation of the theoretical energy budget allocation optimal solution, and automatically correct it to generate a corrected energy budget allocation optimal solution.

[0007] The linear programming method for budget analysis described above, which aggregates the combined energy efficiency extrema under two scenarios with the modified optimal energy budget allocation solution to form the optimal energy budget allocation solution, includes the following sub-steps: Step S61: Based on the comprehensive energy efficiency extreme values ​​under the two scenarios, generate a two-way allocation interval, and perform structured aggregation with the modified energy budget allocation optimal solution to form the optimal energy budget allocation solution; Step S62: Based on the human-machine collaborative decision calibration factor, perform a multi-dimensional feasibility evaluation of the optimal energy budget allocation solution and generate evaluation calibration parameters; Step S63: Perform executable fine-tuning of the optimal energy budget allocation solution based on the evaluation calibration parameters.

[0008] The present invention also provides a linear programming system for budget analysis, comprising: The data acquisition module collects energy analysis parameters and external energy and environmental parameters of the target industrial production system. The dynamic sensing energy budget parameter set generation module associates and binds energy analysis parameters with external energy environment parameters to generate a dynamic sensing energy budget parameter set. The energy budget optimization model construction module applies multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set to construct the energy budget optimization model. The energy budget allocation optimal solution generation module acquires real-time production data, solves the theoretical energy budget allocation optimal solution through the energy budget optimization model, corrects it by calculating the energy budget deviation rate, and generates the corrected energy budget allocation optimal solution. The optimal energy budget allocation solution generation module extracts the boundary values ​​of worst-case and best-case parameters from the dynamically sensed energy budget parameter set, combines them with the corrected optimal energy budget allocation solution, and calculates the comprehensive energy efficiency extreme values ​​under the two scenarios through the boundary comprehensive allocation algorithm.

[0009] The linear programming system for budget analysis described above includes a module for generating a dynamically sensed energy budget parameter set, specifically comprising: The energy budget parameter set is formed by a sub-module that obtains multi-dimensional dynamic correlation factors and links energy analysis parameters with external energy and environmental parameters to form an energy budget parameter set. The trigger setting submodule sets up a triple update trigger for the energy budget parameter set based on time period, external event threshold and early warning signal, generating a dynamically perceived energy budget parameter set.

[0010] The linear programming system for budget analysis described above includes an energy budget optimization model construction module, which specifically comprises: A submodule for constructing multiple combined objective functions is used to construct multiple combined objective functions based on a dynamically sensed set of energy budget parameters. The hierarchical constraint construction submodule obtains production plans and safe operation preferences, and constructs hierarchical constraints in combination with dynamically perceived energy budget parameter sets.

[0011] In the aforementioned linear programming system for budget analysis, the optimal solution generation module for energy budget allocation specifically includes: The submodule for obtaining the optimal solution of theoretical energy budget allocation obtains the optimal solution of theoretical energy budget allocation through the energy budget model. The submodule for generating the optimal energy budget allocation solution is modified. Based on historical energy budget data, it predicts the execution deviation of the theoretical optimal energy budget allocation solution and automatically corrects it to generate the optimal energy budget allocation solution.

[0012] In the aforementioned linear programming system for budget analysis, the optimal energy budget allocation solution generation module specifically includes: The optimal energy budget allocation solution forms a sub-module. Based on the comprehensive energy efficiency extreme values ​​under the two scenarios, a bidirectional allocation interval is generated. This interval is then structurally aggregated with the modified optimal energy budget allocation solution to form the optimal energy budget allocation solution. The evaluation calibration parameter generation submodule performs a multi-dimensional feasibility evaluation of the optimal energy budget allocation solution based on human-machine collaborative decision calibration factors, and generates evaluation calibration parameters. The optimal energy budget allocation solution fine-tuning submodule performs executable fine-tuning of the optimal energy budget allocation solution based on evaluation calibration parameters.

[0013] The beneficial effects achieved by this invention are as follows: This invention can achieve precise matching between energy budget allocation and corporate strategy, improve the scientific nature and risk resistance of energy budget, ensure the feasibility of the plan from theoretical optimization to practical implementation, realize the automatic location of deviations and targeted adjustment of parameters, significantly improve the dynamic adaptability, risk resistance and execution efficiency of energy budget plan, and provide enterprises with a budget management solution that is both scientific and operable. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0015] Figure 1 This is a flowchart of a linear programming method for budget analysis provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of a budget analysis linear programming system provided in Embodiment 2 of this application. Detailed Implementation

[0016] 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, not all, of the embodiments of the present invention. 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.

[0017] Example 1 like Figure 1 As shown in Embodiment 1 of this application, a linear programming method for budget analysis is provided, which includes the following steps: Step S1: Collect energy analysis parameters and external energy environment parameters of the target industrial production system; Specifically, based on the energy budget optimization task of the target industrial production system, energy analysis parameters such as rated power of equipment, historical load curves, planned output of production lines, equipment energy efficiency ratio, and energy consumption per unit of process are collected from the factory's manufacturing execution system, energy management system, and equipment sensors via industrial communication protocols such as Industrial Ethernet. External energy and environmental parameters such as time-of-use electricity price forecasts for future periods, weather forecasts, carbon emission quotas, and real-time carbon prices are collected from data interfaces with power grid companies, weather forecast platforms, and carbon emission trading systems. Based on historical operating data for a preset period, baseline values, fluctuation ranges, and correlations between parameters are obtained through statistical methods. Based on the fluctuation characteristics of external environmental parameters, confidence levels for each energy parameter are set, and an ellipsoidal uncertainty set describing the uncertainty of the energy parameters is constructed based on the above information.

[0018] Step S2: Link and bind energy analysis parameters with external energy and environmental parameters to generate a dynamically sensed energy budget parameter set; Furthermore, linking and binding energy analysis parameters with external energy and environmental parameters to generate a dynamically sensed energy budget parameter set includes the following sub-steps: Step S21: Obtain multi-dimensional dynamic correlation factors, and link and bind energy analysis parameters with external energy and environmental parameters to form an energy budget parameter set; Specifically, the multi-dimensional dynamic correlation factors include time decay factors (efficiency changes over time), operating condition adjustment factors (adjusted according to different production modes), load matching factors (matching theoretical load according to planned output), electricity price sensitivity factors, and other dynamic correlation factors. Based on the multi-dimensional dynamic correlation factors, energy analysis parameters are linked and bound with external environmental parameters to generate a three-level energy budget parameter set of "energy analysis parameters - external energy environment parameters - uncertain fluctuation range".

[0019] Step S22: Set up a triple update trigger based on time period, external event threshold and early warning signal for the energy budget parameter set to generate a dynamically perceived energy budget parameter set; Specifically, time-triggered updaters based on fixed time intervals are set up; event-triggered updaters are set up to trigger when external electricity price fluctuations exceed a set threshold, or when predicted temperature changes exceed a set threshold; and feedback-triggered updaters are set up to trigger when the energy management system issues a load over-limit warning or a critical equipment energy anomaly warning. These triggers are embedded into the energy budget parameter set to generate a dynamically sensed energy budget parameter set for dynamic and automatic data updates.

[0020] Step S3: Apply multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set and construct an energy budget optimization model; Furthermore, the energy budget optimization model is constructed by applying multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set, including the following sub-steps: Step S31: Construct a multi-combination objective function based on the dynamically perceived energy budget parameter set; Specifically, the budget allocation targets are set based on the energy budget optimization task. There are energy-consuming units, and the decision variables are defined as follows: , Indicates assignment to the first Energy budget value per energy-consuming unit The range of values ​​is Based on dynamically perceived energy budget parameter sets and decision variables Constructing multiple combined objective functions ,in, This refers to finding the optimal energy budget allocation solution under the worst-case scenario of parameter fluctuations, maximizing the minimum energy efficiency benefit. For uncertain sets in the ellipsoid The parameter that results in the worst overall energy efficiency (within parentheses) , For the first Unit budget energy efficiency of an energy-consuming unit For the first Unit energy budget cost of energy-consuming units For the dynamic weights of the energy efficiency dimension, For the dynamic weight of cost dimension, The dynamic weights for production priority are adjusted dynamically based on the development stage of the energy-consuming unit. For the first Key production indices for each energy-consuming unit The number of constraints that can be broken. The range of values ​​is , For the first A penalty coefficient that can break the constraint. For the first A constraint that can be broken allows for the breaking of slack variables. For the maximum overall energy efficiency under the worst-case scenario, This represents the maximum overall energy efficiency under normal, typical scenarios. This means finding the optimal energy budget allocation solution under the optimal scenario of parameter fluctuations, thereby maximizing energy efficiency. For an uncertain set in the ellipsoid Find the parameter that maximizes the overall energy efficiency within the parentheses. , Step S32: Obtain production plans and safe operation preferences, and construct hierarchical constraints by combining the dynamically perceived energy budget parameter set; Specifically, rigid hard constraint parameters and elastic soft constraint parameters are extracted from the dynamically sensed energy budget parameter set, and rigid hard constraint conditions are constructed based on the rigid hard constraint parameters. ,in, Due to energy ceiling constraints, Indicates assignment to the first Energy budget for each energy-consuming unit This is the baseline value for safe capacity. As an energy permissible volatility factor, Safety capacity baseline value The allowable fluctuation range; As a minimum energy guarantee constraint for energy-consuming units, For the first Each energy-consuming unit maintains the minimum energy baseline required to sustain its core processes. For the first Load matching factor of an energy-consuming unit under the current production mode For the first The generation priority coefficient and strategic priority score of each energy-consuming unit. As a cost ceiling constraint, For the first Energy budget cost for an energy-consuming unit project For the first The baseline value for the upper limit of costs for an energy-consuming unit's project. For the first Time decay factor per unit of energy consumption For the first The process cycle coefficient of an energy-consuming unit As a constraint on compliance cost expenditure, For the first Compliance and regulatory costs for individual energy-consuming units This is the minimum compliance cost benchmark stipulated by the industry. For operating condition adjustment factors, This refers to the fluctuation range of compliance costs.

[0021] Constructing elastic soft constraint conditions based on elastic soft constraint parameters, production plans, and safe operation preferences. ,in, Risk load constraints for energy-consuming units for Risk level Energy budget value for each energy-consuming unit. The risk level is Benchmark value for the proportion of energy budget in energy-consuming units. This is the baseline value for the total energy budget. As an energy permissible volatility factor, for The range of fluctuation in risk level, In order to exceed the energy quota limit, This is a constraint on the growth rate of production of emerging technologies. For the first The emerging technologies used by individual energy-consuming units to produce their current energy budget. For the first The energy budget for the previous period of production using emerging technologies in individual energy-consuming units. This is the benchmark value for the growth rate multiple. For the first Traditional processes used by energy-consuming units to produce their current energy budget For the first The traditional processes used by energy-consuming units to produce the previous period's energy budget This is the difference for the production growth rate of emerging technologies not reaching a multiple. To adapt the risk constraints to the partners, For the first Energy consumption budgets associated with individual energy-consuming units and their partners. A percentage is reserved for energy budget. A benchmark value for the reserved ratio (set based on the performance rate data of external partners). The time decay factor, For the fluctuation range of the performance rate of the cooperating unit, To reserve the difference if the proportion does not reach the benchmark, For safe operation preference constraints, For the first Minimum energy budget baseline value for the safe operation preference of an energy-consuming unit. This is the difference between the actual energy budget and the minimum baseline value for safe operation preferences.

[0022] Step S4: Obtain real-time production data, solve the theoretical optimal energy budget allocation solution through the energy budget optimization model, correct it by calculating the energy budget deviation rate, and generate the corrected optimal energy budget allocation solution. Furthermore, real-time production data is acquired, and the theoretical optimal energy budget allocation solution is obtained through an energy budget optimization model. This solution is then corrected by calculating the energy budget deviation rate, generating a corrected optimal energy budget allocation solution. The process includes the following sub-steps: Step S41: Obtain the optimal solution for theoretical energy budget allocation through the energy budget model; Specifically, the latest production schedule and external parameters are input into the model for solving, and the theoretical optimal energy budget allocation solution and theoretical optimal slack variables are output for each energy-consuming unit under each scenario.

[0023] Step S42: Based on historical energy budget data, predict the execution deviation of the theoretical optimal energy budget allocation solution, and automatically correct it to generate a corrected optimal energy budget allocation solution; Specifically, historical theoretical energy budget values ​​minus actual execution values ​​are extracted from the industrial production system for a preset period. The historical energy budget execution deviation rate for each energy-consuming unit is calculated. Based on this deviation rate, dynamic sensing parameters such as the risk level of the energy-consuming unit and the contract fulfillment rate of its partners are statistically analyzed. A deviation prediction formula is then used to... The predicted energy budget deviation rate for each energy-consuming unit in this period is as follows: For the first Energy budget deviation rate of each energy-consuming unit To dynamically sense the number of parameters, The range of values ​​is , For the first The first energy-consuming unit Adjustment coefficient for deviation of dynamic sensing parameters For the first The first energy-consuming unit A dynamic sensing parameter fluctuation value. This represents the historical energy budget execution deviation rate. The energy budget correction formula is then applied based on this deviation rate. The optimal solution for the revised theoretical energy budget allocation. To correct the optimal solution for energy budget allocation, the slack variables are corrected simultaneously.

[0024] Step S5: Extract the boundary values ​​of worst-case and best-case parameters from the dynamically sensed energy budget parameter set, combine them with the corrected optimal solution for energy budget allocation, and calculate the comprehensive energy efficiency extreme values ​​under the two scenarios using the boundary comprehensive allocation algorithm. Specifically, from the ellipsoidal uncertainty set of the dynamically perceived energy budget parameter set, based on the pre-set confidence level and parameter fluctuation amplitude threshold, the boundary values ​​of the core budget parameters are extracted. Extremely unfavorable parameter values ​​such as the upper limit of unit energy budget cost and the upper limit of compliance and regulatory expenditure of energy-consuming units are selected to form the worst-case boundary parameter set. Extremely favorable parameter values ​​such as the lower limit of unit budget cost and the lower limit of compliance and regulatory expenditure of energy-consuming units are selected to form the optimal scenario boundary parameter set.

[0025] The modified optimal solution for energy budget allocation, along with the worst-case boundary parameter set and the best-case boundary parameter set, are respectively substituted into the boundary comprehensive allocation algorithm. The optimal comprehensive energy efficiency value is calculated under two scenarios. The optimal solution of the modified energy budget allocation and the boundary parameter set of the worst scenario are then used to calculate the optimal comprehensive energy efficiency value under the worst scenario. The optimal solution of the modified budget allocation and the boundary parameter set of the optimal scenario are used to calculate the comprehensive energy efficiency extremum under the optimal scenario. , For dynamic weights in the production dimension, The total number of energy-consuming units. The range of values ​​is , For the first Production priority index for each energy-consuming unit For the first Production matching factor for each energy-consuming unit For the first The optimal solution for adjusting the energy budget allocation for each energy-consuming unit. For cost dimension, dynamic weights, For the first Unit energy budget cost for an energy-consuming unit This is the compliance cost weighting coefficient. For the first Compliance and regulatory costs for individual energy-consuming units To allow for the breaking of the constraint quantity, The range of values ​​is , For the first A penalty coefficient that can break the constraint. For the first A constraint that can be broken is a slack variable.

[0026] Step S6: Combine the extreme values ​​of comprehensive energy efficiency under the two scenarios with the optimal solution of modified energy budget allocation to form the optimal energy budget allocation solution; Furthermore, the combined energy efficiency extreme values ​​under the two scenarios are aggregated with the modified energy budget allocation optimal solution to form the optimal energy budget allocation solution, which includes the following sub-steps: Step S61: Based on the comprehensive energy efficiency extreme values ​​under the two scenarios, generate a two-way allocation interval, and perform structured aggregation with the modified energy budget allocation optimal solution to form the optimal energy budget allocation solution; Specifically, based on the comprehensive energy efficiency extreme values ​​under two scenarios, an optimal energy efficiency-worst energy efficiency interval is constructed. The comprehensive energy efficiency extreme value under the optimal scenario is used as the upper limit of the interval, and the comprehensive energy efficiency extreme value under the worst scenario is used as the lower limit of the interval, generating a two-way allocation interval of "optimal energy efficiency extreme value - worst energy efficiency extreme value". This clarifies the risk bottom line (minimum acceptable energy efficiency level) and potential upper limit (optimal energy efficiency level) of energy budget allocation. The details of the modified optimal solution for energy budget allocation, the two-way interval range, the parameter boundary descriptions of the two scenarios, and the constraint satisfaction are structured and aggregated to form an optimal budget allocation solution that includes "budget + interval range + parameter basis + constraint compliance".

[0027] Step S62: Based on the human-machine collaborative decision calibration factor, perform a multi-dimensional feasibility evaluation of the optimal energy budget allocation solution and generate evaluation calibration parameters; Specifically, the human-machine collaborative decision-making calibration factors include resource availability factors, system controllability factors, and external environment factors. Resource availability factors are generated by collecting theoretical data on resource availability from industrial production enterprises, such as manpower load rate, equipment availability rate, and capital occupancy rate, and by generating the resource carrying capacity of energy-consuming units based on historical performance and human experience. System controllability factors are generated by acquiring theoretical data on team performance, such as historical energy management team personnel turnover and energy control scores, and by generating the performance level of energy-consuming units based on historical performance and human experience. External environment factors are obtained from industrial production industry databases, such as the intensity of industry rule changes and market demand fluctuation indices, and the impact of external environment data on energy-consuming units is calculated using human experience. Based on the above-mentioned human-machine collaborative decision-making calibration factors, a human-machine collaborative decision-making calibration formula is used. Calculate the evaluation and calibration parameters for each dimension of each energy-consuming unit, among which, For the first The factor of energy consumption unit Evaluation calibration parameters, , For resource availability factor, As a system controllability factor, External environmental factors As a factor The credibility of human experience For the first Factor per energy consumption unit The normalized score of human experience, As a factor The credibility of theoretical books For the first Factor per energy consumption unit The normalized score of the theoretical data.

[0028] Step S63: Perform executable fine-tuning of the optimal energy budget allocation solution based on the evaluation calibration parameters; Specifically, based on the evaluation and calibration parameters of each factor, the energy budget parameters of each energy-consuming unit in the optimal energy budget allocation solution are fine-tuned, and the basis for the fine-tuning and the risk warnings for implementation are marked.

[0029] Example 2 like Figure 2 As shown, Embodiment 2 of this application provides a linear programming system for budget analysis, comprising: Data acquisition module 21 collects energy analysis parameters and external energy environment parameters of the target industrial production system; The dynamic sensing energy budget parameter set generation module 22 associates and binds energy analysis parameters with external energy environment parameters to generate a dynamic sensing energy budget parameter set. Furthermore, the dynamic sensing energy budget parameter set generation module 22 includes the following sub-modules: The energy budget parameter set is formed by a sub-module that obtains multi-dimensional dynamic correlation factors and links energy analysis parameters with external energy and environmental parameters to form an energy budget parameter set. The trigger setting submodule sets up a triple update trigger for the energy budget parameter set based on time period, external event threshold and early warning signal, and generates a dynamically perceived energy budget parameter set; The energy budget optimization model construction module 23 applies multi-objective hierarchical constraints to the dynamically perceived energy budget parameter set to construct an energy budget optimization model. Furthermore, the energy budget optimization model construction module 23 includes the following sub-modules: A submodule for constructing multiple combined objective functions is used to construct multiple combined objective functions based on a dynamically sensed set of energy budget parameters. The hierarchical constraint construction submodule obtains production plans and safe operation preferences, and constructs hierarchical constraints in combination with dynamically perceived energy budget parameter sets; The energy budget allocation optimal solution generation module 24 acquires real-time production data, solves and outputs the theoretical energy budget allocation optimal solution through the energy budget optimization model, corrects it by calculating the energy budget deviation rate, and generates the corrected energy budget allocation optimal solution. Furthermore, the energy budget allocation optimal solution generation module 24 includes the following sub-modules: The submodule for obtaining the optimal solution of theoretical energy budget allocation obtains the optimal solution of theoretical energy budget allocation through the energy budget model. The submodule for generating the optimal energy budget allocation solution is modified. Based on historical energy budget data, it predicts the execution deviation of the theoretical optimal energy budget allocation solution and automatically corrects it to generate the optimal energy budget allocation solution. The integrated energy efficiency extreme value generation module 25 extracts the boundary values ​​of worst and best scenario parameters from the dynamically sensed energy budget parameter set, combines them with the corrected energy budget allocation optimal solution, and calculates the integrated energy efficiency extreme values ​​under the two scenarios through the boundary integrated allocation algorithm. The optimal energy budget allocation solution generation module 26 aggregates the comprehensive energy efficiency extreme values ​​under the two scenarios with the modified optimal energy budget allocation solution to form the optimal energy budget allocation solution. Furthermore, the optimal energy budget allocation solution generation module 26 includes the following sub-modules: The optimal energy budget allocation solution forms a sub-module. Based on the comprehensive energy efficiency extreme values ​​under the two scenarios, a bidirectional allocation interval is generated. This interval is then structurally aggregated with the modified optimal energy budget allocation solution to form the optimal energy budget allocation solution. The evaluation calibration parameter generation submodule performs a multi-dimensional feasibility evaluation of the optimal energy budget allocation solution based on human-machine collaborative decision calibration factors, and generates evaluation calibration parameters. The optimal energy budget allocation solution fine-tuning submodule performs executable fine-tuning of the optimal energy budget allocation solution based on evaluation and calibration parameters. Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor is used to run one or more program instructions to perform a budgetary linear programming method.

[0030] Corresponding to the above embodiments, the present invention provides a computer-readable storage medium containing one or more program instructions, which are used by a processor to implement a budget analysis linear programming method.

[0031] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions that, when executed on a computer, cause the computer to perform the aforementioned budget analysis linear programming method.

[0032] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0033] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0034] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0035] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0036] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0037] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0038] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0039] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A linear programming method for budget analysis, characterized in that, include: Step S1: Collect energy analysis parameters and external energy environment parameters of the target industrial production system; Step S2: Link and bind energy analysis parameters with external energy and environmental parameters to generate a dynamically sensed energy budget parameter set; Step S3: Apply multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set and construct an energy budget optimization model; Step S4: Obtain real-time production data, solve the theoretical optimal energy budget allocation solution through the energy budget optimization model, correct it by calculating the energy budget deviation rate, and generate the corrected optimal energy budget allocation solution. Step S5: Extract the boundary values ​​of worst-case and best-case parameters from the dynamically sensed energy budget parameter set, combine them with the corrected optimal solution for energy budget allocation, and calculate the comprehensive energy efficiency extreme values ​​under the two scenarios using the boundary comprehensive allocation algorithm. Step S6: Combine the extreme values ​​of comprehensive energy efficiency under the two scenarios with the optimal solution of modified energy budget allocation to form the optimal energy budget allocation solution.

2. The linear programming method for budget analysis as described in claim 1, characterized in that, Linking and binding energy analysis parameters with external energy and environmental parameters to generate a dynamically sensed energy budget parameter set includes the following sub-steps: Step S21: Obtain multi-dimensional dynamic correlation factors, and link and bind energy analysis parameters with external energy and environmental parameters to form an energy budget parameter set; Step S22: Set up a triple update trigger based on time period, external event threshold and early warning signal for the energy budget parameter set to generate a dynamically perceived energy budget parameter set.

3. The linear programming method for budget analysis as described in claim 1, characterized in that, The construction of an energy budget optimization model by applying multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set includes the following sub-steps: Step S31: Construct a multi-combination objective function based on the dynamically perceived energy budget parameter set; Step S32: Obtain production plans and safe operation preferences, and construct hierarchical constraints by combining them with the dynamically perceived energy budget parameter set.

4. The linear programming method for budget analysis as described in claim 1, characterized in that, The process of acquiring real-time production data, solving for the theoretical optimal energy budget allocation using an energy budget optimization model, correcting the optimal energy budget allocation by calculating the energy budget deviation rate, and generating the corrected optimal energy budget allocation solution includes the following sub-steps: Step S41: Obtain the optimal solution for theoretical energy budget allocation through the energy budget model; Step S42: Based on historical energy budget data, predict the execution deviation of the theoretical energy budget allocation optimal solution, and automatically correct it to generate a corrected energy budget allocation optimal solution.

5. The linear programming method for budget analysis as described in claim 1, characterized in that, The process of combining the extreme values ​​of combined energy efficiency under the two scenarios with the optimal solution of modified energy budget allocation to form the optimal energy budget allocation solution includes the following sub-steps: Step S61: Based on the comprehensive energy efficiency extreme values ​​under the two scenarios, generate a two-way allocation interval, and perform structured aggregation with the modified energy budget allocation optimal solution to form the optimal energy budget allocation solution; Step S62: Based on the human-machine collaborative decision calibration factor, perform a multi-dimensional feasibility evaluation of the optimal energy budget allocation solution and generate evaluation calibration parameters; Step S63: Perform executable fine-tuning of the optimal energy budget allocation solution based on the evaluation calibration parameters.

6. A linear programming system for budget analysis, characterized in that, include: The data acquisition module collects energy analysis parameters and external energy and environmental parameters of the target industrial production system. The dynamic sensing energy budget parameter set generation module associates and binds energy analysis parameters with external energy environment parameters to generate a dynamic sensing energy budget parameter set. The energy budget optimization model construction module applies multi-objective hierarchical constraints to the dynamically sensed energy budget parameter set to construct the energy budget optimization model. The energy budget allocation optimal solution generation module acquires real-time production data, solves the theoretical energy budget allocation optimal solution through the energy budget optimization model, corrects it by calculating the energy budget deviation rate, and generates the corrected energy budget allocation optimal solution. The integrated energy efficiency extreme value generation module extracts the boundary values ​​of worst-case and best-case parameters from the dynamically sensed energy budget parameter set, combines them with the corrected optimal solution of energy budget allocation, and calculates the integrated energy efficiency extreme values ​​under the two scenarios through the boundary integrated allocation algorithm. The optimal energy budget allocation solution generation module aggregates the comprehensive energy efficiency extreme values ​​under the two scenarios with the modified optimal energy budget allocation solution to form the optimal energy budget allocation solution.

7. A linear programming system for budget analysis as described in claim 6, characterized in that, The dynamic sensing energy budget parameter set generation module specifically includes: The energy budget parameter set is formed by a sub-module that obtains multi-dimensional dynamic correlation factors and links energy analysis parameters with external energy and environmental parameters to form an energy budget parameter set. The trigger setting submodule sets up a triple update trigger for the energy budget parameter set based on time period, external event threshold and early warning signal, generating a dynamically perceived energy budget parameter set.

8. A linear programming system for budget analysis as described in claim 6, characterized in that, The energy budget optimization model construction module specifically includes: A submodule for constructing multiple combined objective functions is used to construct multiple combined objective functions based on a dynamically sensed set of energy budget parameters. The hierarchical constraint construction submodule obtains production plans and safe operation preferences, and constructs hierarchical constraints in combination with dynamically perceived energy budget parameter sets.

9. A linear programming system for budget analysis as described in claim 6, characterized in that, The energy budget allocation optimal solution generation module specifically includes: The submodule for obtaining the optimal solution of theoretical energy budget allocation obtains the optimal solution of theoretical energy budget allocation through the energy budget model. The submodule for generating the optimal energy budget allocation solution is modified. Based on historical energy budget data, it predicts the execution deviation of the theoretical optimal energy budget allocation solution and automatically corrects it to generate the optimal energy budget allocation solution.

10. A linear programming system for budget analysis as described in claim 6, characterized in that, The optimal energy budget allocation solution generation module specifically includes: The optimal energy budget allocation solution forms a sub-module. Based on the comprehensive energy efficiency extreme values ​​under the two scenarios, a bidirectional allocation interval is generated. This interval is then structurally aggregated with the modified optimal energy budget allocation solution to form the optimal energy budget allocation solution. The evaluation calibration parameter generation submodule performs a multi-dimensional feasibility evaluation of the optimal energy budget allocation solution based on human-machine collaborative decision calibration factors, and generates evaluation calibration parameters. The optimal energy budget allocation solution fine-tuning submodule performs executable fine-tuning of the optimal energy budget allocation solution based on evaluation calibration parameters.

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