An engineering budget processing method based on budget mechanical demand

Abstract

The application discloses a kind of engineering budget processing methods based on budget mechanical demand.The method is applied to server, and the method comprises the following steps: constructing mechanical configuration algorithm model;Task execution unit real-time monitoring task scheduling queue, in response to the mechanical resource optimization configuration task initiated by client, extract mechanical resource optimization configuration task from task scheduling queue;Task execution unit extracts known parameter from mechanical resource optimization configuration task;Task execution unit solves mechanical configuration algorithm model according to known parameter, obtains the optimal solution of mechanical configuration algorithm model, the optimal solution is the construction machinery configuration scheme that can meet the progress plan requirement and minimizes the total cost of construction project, and the total cost of construction project corresponding to the optimal solution is calculated.The application realizes the optimal solution of construction machinery configuration automatic calculation without relying on artificial, improves the economy of construction machinery configuration result, and multi-task concurrent calculation can also maximize the use of system resources.

Description

Technical Field

[0001] This application relates to the field of engineering cost technology, and in particular to an engineering budget processing method based on budgeted mechanical requirements. Background Technology

[0002] In modern large-scale engineering construction, the rational configuration of construction machinery is a crucial aspect of ensuring smooth construction progress. This involves the scientific and rational arrangement of the types, quantities, and scheduling of various construction machinery and equipment to meet the actual needs and construction schedule of the project. This process not only affects the construction efficiency but also directly relates to cost control and resource utilization.

[0003] The rational configuration of construction machinery is of great significance. Firstly, it improves construction efficiency, ensuring all operations proceed as planned, thereby shortening the project cycle. Secondly, scientific configuration effectively reduces equipment idle time, minimizes unnecessary investment expenditures, and ultimately enhances the project's economic benefits. Furthermore, rational configuration helps optimize overall resource utilization, avoids redundant procurement and waste, and brings greater competitive advantage to the enterprise.

[0004] However, the inventors recognized that currently, in the large-scale engineering construction industry, after a new construction project commences, the calculation of machinery configuration is generally still based on manually input data combined with demand. This traditional method is not only time-consuming and labor-intensive but also susceptible to human factors, leading to inaccurate calculation results. Furthermore, manual methods cannot achieve automated distributed calculations for efficient multi-task concurrency, resulting in low efficiency and severely restricting the improvement of construction efficiency and the efficient utilization of resources. Therefore, to improve the economic efficiency of construction machinery configuration results, there is a need to add a requirement for automatically calculating the optimal solution for construction machinery configuration based on a holistic consideration of the entire project. Summary of the Invention

[0005] This application provides an engineering budget processing method based on budgeted machinery requirements, which aims to automatically calculate the optimal solution for construction machinery configuration without relying on manual labor.

[0006] A method for processing engineering budgets based on budgeted mechanical requirements, applied to a server, wherein the server is equipped with a task scheduling queue and a task execution unit, the method comprising:

[0007] S1, Construct the mechanical configuration algorithm model;

[0008] S2, the task execution unit monitors the task scheduling queue in real time, and in response to the client initiating a mechanical resource optimization configuration task, extracts the mechanical resource optimization configuration task initiated by the client from the task scheduling queue;

[0009] S3, the task execution unit extracts the known parameters uploaded by the client from the mechanical resource optimization and configuration task;

[0010] S4, the task execution unit solves the mechanical configuration algorithm model according to the known parameters to obtain the optimal solution of the mechanical configuration algorithm model. The optimal solution is the construction machinery configuration scheme that minimizes the total cost of the construction project, and the corresponding total cost of the construction project is calculated.

[0011] In the above scheme, optionally, when constructing the machinery configuration algorithm model, the objective function of the machinery configuration algorithm model is to minimize the total cost of the construction project, the decision variables of the machinery configuration algorithm model include the number of construction machines on site and the total number of machine shifts of replacement budgeted machines, and the constraints of the machinery configuration algorithm model are multiple constraints to ensure that the construction machinery configuration scheme meets the requirements of the machinery resource optimization configuration task.

[0012] In the above scheme, optionally, the objective function of the mechanical configuration algorithm model is specifically:

[0013] minC = C0 + C1 + C 2_1 +C 2_2 +C3+C4+C5+C6;

[0014] Where C0 is the idle cost of construction machinery, C1 is the cost of using construction machinery, and C 2_1 To calculate the depreciation cost of construction machinery based on the number of days it is on-site, C 2_2 To calculate depreciation costs based on fixed depreciation costs, C3 is the cost of dispatching construction machinery to the site, C4 is the cost of dispatching construction machinery to the site, C5 is the priority penalty cost, and C6 is the penalty cost for no solution.

[0015] The constraints of the mechanical configuration algorithm model specifically include:

[0016] The first constraint is the number of times the j-th type of construction machinery can enter the site;

[0017] The second constraint is the number of times the j-th type of construction machinery will be used.

[0018] The third constraint is that the number of shifts in which construction machinery replaces budgeted machinery shall not exceed the maximum available number of shifts.

[0019] The fourth constraint is to consider the efficiency conversion factor when replacing budgeted machinery with construction machinery;

[0020] The fifth constraint is that when calculating depreciation costs based on the number of days the construction machinery was on-site, the number of depreciation days shall not exceed the remaining depreciation period; when calculating depreciation costs based on fixed depreciation costs, it shall be determined whether the construction machinery was on-site.

[0021] The sixth constraint prioritizes construction machinery based on the added penalty cost, selecting the construction machinery with the smallest penalty through the objective function of cost optimization.

[0022] The seventh constraint is that construction machinery cannot replace budget machinery with a conversion factor of 0.

[0023] In the above scheme, alternatively,

[0024]

[0025] Where, j∈[1,J] is the construction machinery index, J=J1+J2+J3, J1 represents the set of owned machinery, J2 represents the set of newly purchased machinery, and J3 represents the set of leased machinery; i∈[1,I] is the construction period index, I represents the construction period set; k∈[1,K] is the budgeted machinery index, K represents the budgeted machinery set; c 0,j Let η be the unit on-site idle cost of construction machinery of type j, j∈[1,J]; ij X represents the maximum number of working shifts for each type of construction machinery in the i-th category; ij Let X be the number of construction machines of type j on day i, i∈[0,I+1], j∈[1,J], and X be the number of construction machines of type j on site. 0j X represents the number of construction machines of type j present before the start of the construction period. I+1,j Y represents the number of construction machines of type j on site after the completion of the construction period; ijk Let i be the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, where i∈[1,I], j∈[1,J], k∈[1,K].

[0026]

[0027] Among them, c 1,j Let J be the unit cost of using the j-th type of construction machinery, where j∈[1,J].

[0028]

[0029] Among them, c 2_1,j Let z be the unit depreciation cost of the j-th type of construction machinery, j∈[1,J]; jt Let t be the actual depreciation days of the t-th construction machine in the j-th category of construction machinery, j∈[1,J], t∈[1,N] j ], N j Let N be the number of construction machines of type j, j∈[1,J]; jt Let be the maximum number of working shifts for each type of construction machinery in the i-th day, i∈[1,I],j∈[1,J];

[0030]

[0031] Among them, c 2_2,j Z represents the fixed depreciation cost of the j-th type of construction machinery, where j∈[1,J]; j Let J be the maximum number of construction machines of type j on site, where j∈[1,J].

[0032]

[0033] Among them, c 3,j Let h be the single entry cost of the j-th type of construction machinery, j∈[1,J]; ij Let be the number of construction machines of type j that enter the site on day i, where i∈[1,I+1], j∈[1,J];

[0034]

[0035] Among them, c 4,j Let g be the single-trip cost of the j-th type of construction machinery, j∈[1,J]; ij Let represent the number of construction machines of type j that appear on day i, where i∈[1,I+1], j∈[1,J];

[0036]

[0037] Among them, c 5,j,k The penalty cost for replacing the j-th type of budgeted machinery with the j-th type of construction machinery, where j∈[1,J] and k∈[1,K];

[0038]

[0039] Where c6 is the penalty cost when the k-th type of budgetary machinery demand cannot be met, k∈[1,K]; τ ik When replacing budgeted machinery with construction machinery, the amount of unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K].

[0040] In the above scheme, optionally, the first constraint is as follows:

[0041]

[0042] h ij ≥X ij -X i-1,j i∈[1,I+1],j∈[1,J]

[0043] h ij ≥0, i∈[1,I+1],j∈[1,J]

[0044] Among them, h ij Let X be the number of construction machines of type j entering the site on day i, where i∈[1,I+1], j∈[1,J]; ijh represents the number of construction machines of type j on day i, where i∈[0,I+1], j∈[1,J]; ij and X ij All are integer variables;

[0045] The second constraint is as follows:

[0046]

[0047] g ij ≥X i-1,j -X ij i∈[1,I+1],j∈[1,J]

[0048] g ij ≥0, i∈[1,I+1],j∈[1,J]

[0049] Where, i∈[1,I+1], j∈[1,J], g ij For the number of construction machines of type j that appear on day i, g ij ≥0;

[0050] The third constraint is specifically:

[0051]

[0052] Among them, Y ijk For the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, i∈[1,I], j∈[1,J], k∈[1,K]; η ij This represents the maximum number of working shifts for each type of construction machinery in the i-th day;

[0053] The fourth constraint is as follows:

[0054]

[0055] τ ik ≥0, i∈[1,I],k∈[1,K]

[0056] Where, β jk The conversion factor for replacing the j-th type of construction machinery with the k-th type of budget machinery, where j∈[1,J], k∈[1,K]; Y ijk *β jk τ represents the effective number of shifts in which construction machinery of type j replaces budgeted machinery of type k on day i; ik When replacing budgeted machinery with construction machinery, the unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K]; D ik Let i be the number of machine shifts required for the k-th type of budgeted machinery on day i, where i∈[1,I] and k∈[1,K].

[0057] The fifth constraint specifically includes:

[0058] Depreciation costs are calculated based on the number of days on-site:

[0059] (N j +1)θ ijt ≥X ij -t+1,i∈[1,I],j∈[1,J],t∈[1,N j ]

[0060]

[0061] Where, N j Let θ be the number of construction machines of type j, j∈[1,J]; ijt This indicates whether the t-th construction machine of type j is present on day i, where i∈[1,I],j∈[1,J],t∈[1,Nj]; N jt Let v be the maximum number of working shifts for each type of construction machinery in the i-th day, i∈[1,I],j∈[1,J]; j Let J be the remaining depreciation period for the j-th type of construction machinery, where j∈[1,J].

[0062] Depreciation costs are calculated based on fixed depreciation costs:

[0063] Z j ≥X ij ,i∈[1,I],j∈[1,J],

[0064] Among them, Z j Let J be the maximum number of construction machines of type j on site, where j∈[1,J].

[0065] In the above scheme, step S4 may further include:

[0066] When solving the mechanical configuration algorithm model, it is determined whether the mechanical configuration algorithm model has a feasible solution;

[0067] If no feasible solution is determined, stop the subsequent model solving and output a prompt message to inform the recipient of the budgeted mechanical requirements that cannot be met in the mechanical resource optimization and allocation task.

[0068] In the above scheme, optionally, when determining whether the mechanical configuration algorithm model has a feasible solution, the impact of cost is ignored, and a simplified model is used to determine whether there is a feasible solution; the simplified model is expressed as:

[0069]

[0070] τ ik ≥0, i∈[1,I],k∈[1,K]

[0071] Y ijk ≥0,i∈[1,I],j∈[1,J],k∈[1,K]

[0072] Where, τ ik ≥0, τ ik When replacing budgeted machinery with construction machinery, the unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K]; Y ijk ≥0, i∈[1,I], j∈[1,J], k∈[1,K]; Y ijk For the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, i∈[1,I], j∈[1,J], k∈[1,K]; η ij N represents the maximum number of working shifts for each type of construction machinery in the i-th day; j Let β be the number of construction machines of type j, j∈[1,J]; jk The conversion factor for replacing the j-th type of construction machinery with the k-th type of budget machinery, j∈[1,J], k∈[1,K]; D ik Let i be the number of machine shifts required for the k-th type of budgeted machinery on day i, where i∈[1,I] and k∈[1,K].

[0073] When C6 is 0, it indicates that the model has a feasible solution and can continue with further optimization; when C6 is positive, it indicates that the model has no feasible solution, and in this case, the existence of τ is given. ik If the budgeted mechanical value k is greater than 0, then stop solving the model.

[0074] Optionally, in the above scheme, the server is further provided with a task execution result storage module, and after step S4, the method further includes:

[0075] S5, the task execution unit sends the obtained optimal solution and the corresponding total cost of the construction project to the task execution result storage module for storage.

[0076] Optionally, in step S2, the client initiates a mechanical resource optimization configuration task via API.

[0077] After step S4, the method further includes:

[0078] S6 receives task status query requests from clients via API.

[0079] The execution status of the mechanical resource optimization and allocation task in the task execution unit is returned to the client;

[0080] S7 receives task result query requests from clients via API.

[0081] The optimal solution obtained from the task execution unit and the corresponding total cost of the construction project are returned to the client.

[0082] In the above scheme, optionally, when there are multiple mechanical resource optimization and configuration tasks in the task scheduling queue, after extracting the mechanical resource optimization and configuration tasks from the task scheduling queue, steps S3-S4 are executed for each mechanical resource optimization and configuration task in the extraction order, or steps S3-S4 are executed for each mechanical resource optimization and configuration task according to the planned time.

[0083] Compared with the prior art, this application has at least the following beneficial effects:

[0084] The method provided in this application, by utilizing a task scheduling queue, task execution units, and a constructed machinery configuration algorithm model, can quickly provide the optimal construction machinery configuration scheme based on the machinery resource optimization configuration task initiated by the client without relying on manual intervention. This achieves automated processing of machinery resource optimization configuration tasks. Compared with the traditional method of manual data input, it can significantly improve processing speed and accuracy, and reduce the possibility of human error. Traditional methods rely on manual data input, which is easily affected by human factors, leading to inaccurate results. The method provided in this application, through real-time monitoring and automatic data extraction, eliminates manual intervention and improves processing efficiency and accuracy. The setting of the task scheduling queue and task execution units enables the system to process multiple machinery resource optimization configuration tasks simultaneously, overcoming the single-task processing bottleneck of traditional methods and improving overall construction efficiency. Not only does the system support automatic calculation of the optimal solution for construction machinery configuration, improving the economy of construction machinery configuration results, but multi-task concurrent calculation also maximizes the utilization of system resources. Attached Figure Description

[0085] Figure 1 A schematic diagram illustrating the application environment of an engineering budget processing method based on budgeted machinery requirements, provided as an embodiment of this application;

[0086] Figure 2 A flowchart illustrating an engineering budget processing method based on budgeted machinery requirements, provided as an embodiment of this application;

[0087] Figure 3 This is a schematic diagram illustrating the application of the algorithm in one embodiment of this application;

[0088] Figure 4 This is a diagram of the algorithm application service technology stack in one embodiment of this application;

[0089] Figure 5 This is an interaction diagram of a distributed task scheduling module in one embodiment of this application;

[0090] Figure 6This application provides an internal structural diagram of a computer device according to one embodiment. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0092] In the description of this application, unless otherwise stated, expressions such as "comprising," "including," and "having" also mean "not limited to" (certain units, components, materials, steps, etc.).

[0093] This application provides an engineering budget processing method based on budgeted machinery requirements, which can be applied to, for example... Figure 1 In the application environment shown, client 101 communicates with server 102 via a network. Client 101 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets, and server 102 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0094] In one embodiment, such as Figure 1 As shown, an engineering budget processing method based on budgeted machinery demand is provided, namely, a method for optimizing the allocation of machinery resources based on budgeted machinery demand. This method is applied to... Figure 1 Taking server 102 as an example, which is equipped with a task scheduling queue and a task execution unit, the method includes the following steps:

[0095] S1, Construct the mechanical configuration algorithm model.

[0096] S2, the task execution unit monitors the task scheduling queue in real time, responds to the client's initiated mechanical resource optimization configuration task, and extracts the client's initiated mechanical resource optimization configuration task from the task scheduling queue;

[0097] S3, the task execution unit extracts the known parameters uploaded by the client from the mechanical resource optimization and allocation task;

[0098] S4, the task execution unit solves the mechanical configuration algorithm model according to the known parameters, and obtains the optimal solution of the mechanical configuration algorithm model. The optimal solution is the construction machinery configuration scheme that can meet the schedule requirements and minimize the total cost of the construction project, and calculates the corresponding total cost of the construction project.

[0099] Furthermore, when constructing the mechanical configuration algorithm model, the objective function of the mechanical configuration algorithm model is to minimize the total cost of the construction project. The decision variables of the mechanical configuration algorithm model include the number of construction machines on site and the total number of machine shifts of the replacement budgeted machines. The constraints of the mechanical configuration algorithm model are multiple constraints to ensure that the construction machine configuration scheme meets the requirements of the mechanical resource optimization allocation task.

[0100] In other words, the method provided in this application embodiment is based on model requirements and customer-provided data, with the goal of minimizing costs, and constrained by known parameters and various requirements provided by the customer. It designs and constructs a mathematical algorithm model, performs optimal feasibility solution on the algorithm model, and obtains: the number of a certain type of construction machinery on site on a certain day, the solution for the total number of shifts of a certain type of construction machinery replacing a certain type of budgeted machinery on a certain day, and the total project cost value under the current solution.

[0101] The mathematical model consists of the following parts:

[0102] (1) Objective function: Define the objective that the model wants to optimize, that is, to minimize the total cost of the project.

[0103] (2) Decision variables: These represent the quantities that can vary in the model, namely the number of construction machines on site and the total number of machine shifts to replace the budgeted machines.

[0104] (3) Constraints: Ensure the solution meets the customer's various needs and known parameters, such as the number of machines on site, the number of replacement shifts, time constraints, and cost budget; ensure the machine configuration algorithm model is implemented while meeting the customer's needs. For example, limit the number of machines on site to a certain threshold, the number of replacement shifts must be completed within a specified time, and the budget cannot be exceeded.

[0105] (4) Known parameters: Provide the specific values ​​required for the model, such as mechanical costs, construction requirements, time constraints and budget.

[0106] (5) Solution and results: Solve the model using optimization methods to obtain the optimal solution and calculate the total project cost.

[0107] The mathematical model to be constructed is essentially a resource optimization and allocation problem, belonging to operations research and management science. Specifically, it is a linear programming problem with linear constraints. The core of the problem lies in minimizing the total project cost by rationally allocating the number of construction machines and the total number of machine shifts to replace the budgeted machines, while satisfying a series of constraints (such as construction requirements, machinery availability, time constraints, budget, etc.).

[0108] Examples of the above methods for mapping to mathematical models are described below:

[0109] The known conditions include:

[0110] (1) The various machines used in the budget and the number of additional machine shifts required each day (i.e., the demand);

[0111] (2) The available construction machinery can be categorized as owned, to be newly purchased, or leased, depending on the channel. The costs associated with each type of construction machinery differ, even for the same type of machinery from the same channel.

[0112] (3) Variable costs of each piece of construction machinery;

[0113] (4) The maximum number of available units for each type of construction machinery;

[0114] (5) The efficiency conversion coefficient between construction machinery and budgeting machinery, such as Figure 4 As shown, the efficiency conversion coefficients of the same type of construction machinery and the same type of budgeted machinery of the same channel type are not necessarily the same;

[0115] (6) Cost calculation of construction machinery from different sources;

[0116] Total cost of a certain self-owned construction machinery:

[0117] (Unit fuel consumption * fuel price + Unit electricity consumption * electricity price) * work shifts + Repair unit price * work shifts + Staffing quantity * wages * work shifts + (Residual value / (Depreciation period * 365)) * Depreciation days)

[0118] The total cost of a newly purchased construction machine:

[0119] (Unit fuel consumption * fuel price + Unit electricity consumption * electricity price) * work shift + Repair unit price * work shift + Staffing quantity * salary * work shift + (Original value / (Depreciation period * 365)) * Depreciation days)

[0120] Total cost of renting construction machinery:

[0121] (Work shift * rental unit price + (unit fuel consumption * fuel price + unit electricity consumption * electricity price) * work shift + number of staff * salary * work shift)

[0122] (7) The depreciation days are the machine's on-site time + the dispatch period before entry + the dispatch period after exit.

[0123] Development and solution:

[0124] [Objective Function]: Based on the known parameters, find the optimal solution for project cost under the current plan.

[0125] [Decision Variables]:

[0126] X ij: The number of construction machines of type j on the i-th day (unit: unit), an integer variable, i∈[0,I+1], j∈[1,J], where X 0j X represents the number of construction machines of type j present before the start of the construction period. I+1,j This indicates the number of construction machines of type j present on-site after the completion of the construction period. By default...

[0127] Y ijk : The number of shifts (unit: shifts) of construction machinery of type j replacing budget machinery of type k on day i, a continuous variable, i∈[1,I], j∈[1,J], k∈[1,K].

[0128] [Known Parameters]:

[0129] Sets and Indexes:

[0130] j, j∈[1,J] is the index of construction machinery, where J=J1+J2+J3, J1 represents the set of owned machinery, J2 represents the set of newly purchased machinery, and J3 represents the set of leased machinery.

[0131] k, k∈[1,K], is the budget machinery index, where K represents the budget machinery set.

[0132] i, i∈[1,I] is the project duration index, where I represents the project duration set.

[0133] [Unit Cost Parameter]:

[0134] c 0,j : Unit on-site idle cost of construction machinery of type j (unit: US dollars / shift), j∈[1,J].

[0135] c 1,j : The unit usage cost of the j-th type of construction machinery (unit: US dollars / shift), j∈[1,J].

[0136] c 2_1,j : Unit depreciation cost of the j-th type of construction machinery (unit: US dollars / day / unit)2, j∈[1,J].

[0137] c 2_2,j 3. Fixed depreciation cost of construction machinery of type j (unit: US dollars / unit), j∈[1,J].

[0138] c 3,j : The single entry cost of the j-th type of construction machinery (unit: US dollars / time / unit), j∈[1,J].

[0139] c 4,j : The single-trip cost of the j-th type of construction machinery (unit: US dollars / trip / unit), j∈[1,J].

[0140] Other known parameters:

[0141] N j : The number of construction machines of type j (unit: units), j∈[1,J].

[0142] D ik : The demand for machine shifts of the k-th type of budgeted machinery on day i (unit: machine shifts), i∈[1,I], k∈[1,K].

[0143] η ij : The maximum number of working shifts for each type of construction machinery on day i (unit: shift / machine), i∈[1,I],j∈[1,J].

[0144] β jk : The conversion coefficient for replacing the j-th type of construction machinery with the k-th type of budget machinery, j∈[1,J], k∈[1,K].

[0145] v j : The remaining depreciation period of the j-th type of construction machinery (unit: days), j∈[1,J].

[0146] s j : The number of construction machines of type j on site before the start of the construction period. By default, its value is 0. It is an integer variable (unit: units), j∈[1,J].

[0147] e j : The number of construction machines of type j on site after the construction period ends. By default, its value is 0. It is an integer variable (unit: units), j∈[1,J].

[0148] c 5,j,k : Penalty cost for replacing Class k budget machinery with Class j construction machinery (unit: US dollars / shift), j∈[1,J], k∈[1,K].

[0149] c6: Penalty cost when the demand for machinery in the k-th budget cannot be met (unit: US dollars / shift), k∈[1,K].

[0150] [Intermediate Variable]:

[0151] h ij : The number of construction machines of type j entering the site on day i, an integer variable (unit: units), i∈[1,I+1],j∈[1,J].

[0152] g ij : The number of construction machines of type j that appeared on day i, an integer variable (unit: unit), i∈[1,I+1],j∈[1,J].

[0153] z jt: The actual depreciation days (in days) of the t-th construction machine in the j-th category of construction machinery, j∈[1,J],t∈[1,Nj].

[0154] μ jt : 0-1 intermediate variable, used to linearize the min function, has no practical meaning, j∈[1,J], t∈[1,Nj].

[0155] θ ijt : Whether the t-th construction machine of type j in the i-th day is present, a 0-1 variable, i∈[1,I], j∈[1,J], t∈[1,N] j ].

[0156] Z j : Maximum number of construction machines of type j on site (unit: units), j∈[1,J].

[0157] τ ik : The amount of unmet budget mechanical demand for the i-th day and the k-th type, used to determine whether there is no solution, i∈[1,I], k∈[1,K].

[0158] Furthermore, the objective function of the mechanical configuration algorithm model is specifically minC=C0+C1+C 2_1 +C 2_2 +C3+C4+C5+C6, where C0 is the idle cost of construction machinery, C1 is the cost of using construction machinery, and C 2_1 To calculate the depreciation cost of construction machinery based on the number of days it is on-site, C 2_2 To calculate depreciation costs based on fixed depreciation costs, C3 is the cost of dispatching construction machinery to the site, C4 is the cost of dispatching construction machinery to the site, C5 is the priority penalty cost, and C6 is the penalty cost for no solution.

[0159] Specifically:

[0160]

[0161] Where, j∈[1,J] is the construction machinery index, J=J1+J2+J3, J1 represents the set of owned machinery, J2 represents the set of newly purchased machinery, and J3 represents the set of leased machinery; i∈[1,I] is the construction period index, I represents the construction period set; k∈[1,K] is the budgeted machinery index, K represents the budgeted machinery set; c 0,j Let η be the unit on-site idle cost of construction machinery of type j, j∈[1,J]; ij X represents the maximum number of working shifts for each type of construction machinery in the i-th category; ij Let X be the number of construction machines of type j on day i, i∈[0,I+1], j∈[1,J], and X be the number of construction machines of type j on site. 0jX represents the number of construction machines of type j present before the start of the construction period. I+1,j Y represents the number of construction machines of type j on site after the completion of the construction period; ijk Let i be the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, where i∈[1,I], j∈[1,J], k∈[1,K].

[0162]

[0163] Among them, c 1,j Let J be the unit cost of using the j-th type of construction machinery, where j∈[1,J].

[0164]

[0165] Among them, c 2_1,j Let z be the unit depreciation cost of the j-th type of construction machinery, j∈[1,J]; jt Let t be the actual depreciation days of the t-th construction machine in the j-th category of construction machinery, j∈[1,J], t∈[1,N] j ], N j Let N be the number of construction machines of type j, j∈[1,J]; jt Let be the maximum number of working shifts for each type of construction machinery in the i-th day, i∈[1,I],j∈[1,J];

[0166]

[0167] Among them, c 2_2,j Z represents the fixed depreciation cost of the j-th type of construction machinery, where j∈[1,J]; j Let J be the maximum number of construction machines of type j on site, where j∈[1,J].

[0168]

[0169] Among them, c 3,j Let h be the single entry cost of the j-th type of construction machinery, j∈[1,J]; ij Let be the number of construction machines of type j that enter the site on day i, where i∈[1,I+1], j∈[1,J];

[0170]

[0171] Among them, c 4,j Let g be the single-trip cost of the j-th type of construction machinery, j∈[1,J]; ij Let represent the number of construction machines of type j that appear on day i, where i∈[1,I+1], j∈[1,J];

[0172]

[0173] Among them, c 5,j,k The penalty cost for replacing the j-th type of budgeted machinery with the j-th type of construction machinery, where j∈[1,J] and k∈[1,K];

[0174]

[0175] Where c6 is the penalty cost when the k-th type of budgetary machinery demand cannot be met, k∈[1,K]; τ ik When replacing budgeted machinery with construction machinery, the amount of unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K].

[0176] In other words, the objective function of the model is:

[0177] minC = C0 + C1 + C 2_1 +C 2_2 +C3+C4+C5+C6

[0178] Cost 0: Construction machinery idle time cost: Unit idle cost * Number of idle machine shifts

[0179]

[0180] Cost 1: Construction machinery usage cost: unit usage cost * actual number of work shifts

[0181]

[0182] Cost 2.1: Calculation of depreciation cost of construction machinery based on the number of days on site: Unit depreciation cost * Actual number of depreciation days

[0183]

[0184] Cost 2.2: Calculate depreciation cost based on fixed depreciation cost

[0185]

[0186] Cost 3: Construction machinery deployment cost: cost per deployment * number of equipment deployments

[0187]

[0188] Cost 4: Construction machinery dispatch cost: cost per dispatch * number of times equipment is dispatched

[0189]

[0190] Cost 5: Priority penalty cost

[0191]

[0192] Cost 6: The cost of an unsolvable penalty

[0193]

[0194] Furthermore, the constraints of the mechanical configuration algorithm model specifically include:

[0195] The first constraint is the number of times the j-th type of construction machinery can enter the site;

[0196] The second constraint is the number of times the j-th type of construction machinery will be used.

[0197] The third constraint is that the number of shifts in which construction machinery replaces budgeted machinery shall not exceed the maximum available number of shifts.

[0198] The fourth constraint is to consider the efficiency conversion factor when replacing budgeted machinery with construction machinery;

[0199] The fifth constraint is that when calculating depreciation costs based on the number of days the construction machinery was on-site, the number of depreciation days shall not exceed the remaining depreciation period; when calculating depreciation costs based on fixed depreciation costs, it shall be determined whether the construction machinery was on-site.

[0200] The sixth constraint prioritizes construction machinery based on the added penalty cost, selecting the construction machinery with the smallest penalty through the objective function of cost optimization.

[0201] The seventh constraint is that construction machinery cannot replace budget machinery with a conversion factor of 0.

[0202] Specifically, the first constraint is as follows:

[0203]

[0204] h ij ≥X ij -X i-1,j i∈[1,I+1],j∈[1,J]

[0205] h ij ≥0, i∈[1,I+1],j∈[1,J]

[0206] Among them, h ij Let X be the number of construction machines of type j entering the site on day i, where i∈[1,I+1], j∈[1,J]; ij h represents the number of construction machines of type j on day i, where i∈[0,I+1], j∈[1,J]; ij and X ij All are integer variables;

[0207] The second constraint is as follows:

[0208]

[0209] gij ≥X i-1,j -X ij i∈[1,I+1],j∈[1,J]

[0210] g ij ≥0, i∈[1,I+1],j∈[1,J]

[0211] Where, i∈[1,I+1], j∈[1,J], g ij For the number of construction machines of type j that appear on day i, g ij ≥0;

[0212] The third constraint is specifically:

[0213]

[0214] Among them, Y ijk For the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, i∈[1,I], j∈[1,K], k∈[1,K]; η ij This represents the maximum number of working shifts for each type of construction machinery in the i-th day;

[0215] The fourth constraint is as follows:

[0216]

[0217] τ ik ≥0, i∈[1,I],k∈[1,K]

[0218] Where, β jk The conversion factor for replacing the j-th type of construction machinery with the k-th type of budget machinery, where j∈[1,J], k∈[1,K]; Y ijk *β jk τ represents the effective number of shifts in which construction machinery of type j replaces budgeted machinery of type k on day i; ik When replacing budgeted machinery with construction machinery, the unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K]; D ik Let i be the number of machine shifts required for the k-th type of budgeted machinery on day i, where i∈[1,I] and k∈[1,K].

[0219] The fifth constraint specifically includes:

[0220] Depreciation costs are calculated based on the number of days on-site:

[0221] (N j +1)θ ijt ≥X ij -t+1,i∈[1,I],j∈[1,J],t∈[1,N j ]

[0222]

[0223] Where, N j Let θ be the number of construction machines of type j, j∈[1,J]; ijt This indicates whether the t-th construction machine of type j is present on day i, where i∈[1,I],j∈[1,J],t∈[1,Nj]; N jt Let v be the maximum number of working shifts for each type of construction machinery in the i-th day, i∈[1,I],j∈[1,J]; j Let J be the remaining depreciation period for the j-th type of construction machinery, where j∈[1,J].

[0224] Depreciation costs are calculated based on fixed depreciation costs:

[0225] Z j ≥X ij , i∈[1,I], j∈[1,J];

[0226] Among them, Z j Let J be the maximum number of construction machines of type j on site, where j∈[1,J].

[0227] In other words, the constraints of the mechanical configuration algorithm model include:

[0228] Constraint 1 (Constraint related to deployment costs): The number of deployments (unit: units) for Class j construction machinery is:

[0229]

[0230] h ij ≥X ij -X i-1,j i∈[1,I+1],j∈[1,J]

[0231] h ij ≥0, i∈[1,I+1],j∈[1,J]

[0232] Constraint 1 Analysis:

[0233] h ij and X ij All are integer variables, h ij Let represent the number of construction machines of type j entering the site on day i, and let Xij represent the number of construction machines of type j on site on day i.

[0234] Therefore, X ij -X i-1,j >0 indicates the number of construction machines of type j entering the site on day i, where h is the number of machines entering the site. ij =X ij -X i-1,jLet X represent the number of construction machines of type j entering the site on day i. ij -X i-1,j When X ij -X i-1,j When h ≤ 0, it means that no construction machinery of type j entered the site on day i or construction machinery of type j left the site on day i. ij ≥0, but because the objective function includes mechanical entry costs, therefore h i j must be 0, indicating that the number of construction machines of type j entering the site on day i is 0.

[0235] Constraint 2 (Constraints related to dispatch costs): The number of times (unit: units) of construction machinery of type j to be dispatched is:

[0236]

[0237] g ij ≥X i-1,j -X ij i∈[1,I+1],j∈[1,J]

[0238] g ij ≥0, i∈[1,I+1],j∈[1,J]

[0239] Constraint 2 Analysis:

[0240] Similarly, the analysis follows the same logic as constraint 1.

[0241] Constraint 3 (Constraints related to available machine shifts) The number of machine shifts for replacing budgeted machine with construction machine shall not exceed its maximum available machine shifts.

[0242]

[0243] Constraint 3 Analysis:

[0244] ∑Y ijk K k =1 indicates that the total number of working shifts for construction machinery of type j on day i must meet the maximum number of working shifts for construction machinery of type j on day i.

[0245] Constraint 4 (Constraints related to no feasible solution and the requirement that construction machinery must meet budgeted machinery requirements): When replacing budgeted machinery with construction machinery, the efficiency conversion factor must be considered.

[0246]

[0247] τ ik ≥0, i∈[1,I],k∈[1,K]

[0248] Constraint 4 Analysis:

[0249] Y ijkThis represents the number of shifts in which type j construction machinery replaces type k budget machinery on day i.

[0250] β jk This represents the conversion factor for replacing type k budget machinery with type j construction machinery.

[0251] Y ijk *β jk This represents the effective number of shifts on day i where type j construction machinery replaces type k budget machinery.

[0252] τik represents the insufficient number of machine shifts required when replacing budgeted machinery with construction machinery. ik τ is a slack variable. Due to the penalty cost of 6, τ only becomes a slack variable when the current model has no feasible solution. ik Only then will it have an effect. Therefore, the effective number of shifts on the left side, representing the number of construction machines replacing the k-th budgeted machine on day i, should not be less than the required number of shifts for the k-th budgeted machine on day i, as shown in the right side. For the scheme obtained from the model, we should first determine τ. ik Whether the value is not zero for each τ ik For budget machinery k > 0, a hint is given.

[0253] Constraint 5 (Constraints related to depreciation costs): When calculating depreciation costs based on the number of days the construction machinery is on site, the number of days of depreciation should not exceed the remaining depreciation period; when calculating depreciation costs based on fixed depreciation costs, it is necessary to determine whether the construction machinery was on site.

[0254] Constraint 5.1: Calculate depreciation costs based on the number of days the business is present.

[0255] (N j +1)θ ijt ≥X ij -t+1,i∈[1,I],j∈[1,J],t∈[1,N j ]

[0256]

[0257] Constraint 5.1 Analysis: To determine whether the number of days a certain construction machine is on-site exceeds the remaining days, we need to know the number of days the machine is on-site. Here we can observe that for construction machines with identical parameters, keeping one machine continuously in operation is more cost-effective in terms of dispatch and depreciation costs (if the depreciation period has expired) than rotating construction machines. Therefore, we add an auxiliary 0-1 variable θ. ijt This indicates whether the t-th construction machine in the j-th category of construction machinery is present on day i. (N) j +1)θ ijt N j The function of X is similar to that of M, used to control inequalities. ij In -t+1, X ijThis represents the number of construction machines of type j on day i. For example, when X... ij A value of 3 indicates that there are 3 pieces of construction machinery of type j on the i-th day, namely the 1st, 2nd and 3rd pieces.

[0258] Therefore, when t=1, t=2, t=3, X ij The values ​​of -t+1 are 3, 2, and 1 respectively, and the left-hand side θ ijt The value must be 1 when t = 1, 2, 3.

[0259] When t>3, X ij -t+1<0, considering the objective function includes depreciation costs, the left-hand side θ ijt It must be 0.

[0260] In summary, θ ijt This perfectly expresses its meaning, namely, whether the t-th construction machine in the j-th type of construction machinery is present on day i.

[0261] ∑θ ijt I i =1 indicates the number of days the t-th construction machine in the j-th type of construction machinery is on site during the entire construction period. The smaller value between the t-th machine and the parameter vj is taken as the actual number of days the machine is on site.

[0262] Constraint 5.1 Linearization (Min function linearization):

[0263]

[0264] z jt ≥v j -Mμ jt ,j∈[1,J],t∈[1,N j ]

[0265] μ jt ∈{0,1},j∈[1,J],t∈[1,N j ]

[0266] Constraint 5.2: Calculate depreciation cost based on fixed depreciation cost.

[0267] Z j ≥X ij ,i∈[1,I],j∈[1,J],

[0268] Constraint 5.2 Analysis: When calculating fixed depreciation costs, it is necessary to determine whether a certain construction machinery was ever present. Therefore, similarly to the analysis of Constraint 5.1, we can conclude that: X ij The maximum value during the construction period is the maximum number of construction machines of type j that were present during the construction period.

[0269] Constraint 6 (Priority-related constraint): Priority can be achieved by adding penalty costs. By using the objective function of cost optimization, construction machinery with smaller penalties can be selected.

[0270] Constraint 6 Explanation: Constraint 6 is reflected in Cost 5. Each replacement of a budgeted machine with a construction machine incurs a penalty cost (the lower the priority, the higher the penalty cost). Therefore, under the optimal cost-performance objective, the algorithm will select the higher-priority construction machine for replacement, unless that machine is not available or conflicts with other constraints.

[0271] Constraint 7 (Conversion Coefficient Related Constraint): Construction machinery cannot replace budget machinery with a conversion coefficient of 0.

[0272] Y ijk =0,i∈[1,I],j∈[1,J],k∈[1,K],β jk =0

[0273] In summary, this model can meet the model-related requirements of the project.

[0274] μ jt ∈{0,1},j∈[1,J],t∈[1,N j ]

[0275] Furthermore, step S4 also includes:

[0276] When solving the mechanical configuration algorithm model, determine whether the mechanical configuration algorithm model has a feasible solution;

[0277] If no feasible solution is determined, stop the subsequent model solving and output a prompt message to inform the recipient of the budgeted mechanical requirements that cannot be met in the mechanical resource optimization and allocation task.

[0278] When determining whether the mechanical configuration algorithm model has a feasible solution, the impact of cost is ignored, and a simplified model is used to determine whether there is a feasible solution; the simplified model is expressed as:

[0279]

[0280] τ ik ≥0, i∈[1,I],k∈[1,K]

[0281] Y ijk ≥0,i∈[1,I],j∈[1,J],k∈[1,K]

[0282] Where, τ ik ≥0, τ ik When replacing budgeted machinery with construction machinery, the unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K]; Y ijk≥0, i∈[1,I], j∈[1,J], k∈[1,K]; Y ijk For the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, i∈[1,I], j∈[1,J], k∈[1,K]; η ij N represents the maximum number of working shifts for each type of construction machinery in the i-th day; j Let β be the number of construction machines of type j, j∈[1,J]; jk The conversion factor for replacing the j-th type of construction machinery with the k-th type of budget machinery, j∈[1,J], k∈[1,K]; D ik Let i be the number of machine shifts required for the k-th type of budgeted machinery on day i, where i∈[1,I] and k∈[1,K].

[0283] When C6 is 0, it indicates that the model has a feasible solution and can continue with further optimization; when C6 is positive, it indicates that the model has no feasible solution, and in this case, the existence of τ is given. ik If the budgeted mechanical value k is greater than 0, then stop solving the model.

[0284] In other words, this environment represents an improvement to the model, where no feasible solution is found, and this is considered first. This corresponds to the class `Model_Judge` in the algorithm's source code.

[0285] Given the current parameters, the model may encounter situations where no solution is possible. In such cases, it's necessary to provide a notification indicating which budgeted machinery requirements cannot be met. The impact of cost can be ignored when determining whether a solution is infeasible; therefore, a simplified model can be used to identify such situations.

[0286] Assuming all construction machinery is present, and the original model is modified to contain only objective functions and constraints that may render the model infeasible, it can be determined whether the original model is infeasible.

[0287] Therefore, the original model can be expressed as:

[0288]

[0289] τ ik ≥0, i∈[1,I],k∈[1,K]

[0290] Y ijk ≥0,i∈[1,I],j∈[1,J],k∈[1,K]

[0291] When the C6 value is 0, it indicates that the model has a feasible solution and further optimization can continue.

[0292] When C6 is positive, it indicates that the model has no feasible solution, and in this case, it is necessary to provide the existence of τ. ikIf the budgeted mechanical value k is greater than 0, then stop solving the model.

[0293] In the case where there is no solution, the solution speed can be accelerated by dividing the project period into one-day segments and performing parallel computation. Therefore, the original model is updated to (which holds true for every i):

[0294] min C=C6

[0295]

[0296] τ k ≥0, k∈[1,K]

[0297] Y jk ≥0, j∈[1,J],k∈[1,K]

[0298] There is an equation relating the number of entries to the number of exits. Therefore, the number of entries is used to replace the number of exits.

[0299] By observing the data structure, we can see that the number of times the j-th type of construction machinery appears on site = the number of times the j-th type of construction machinery enters the site + the number of the j-th type of construction machinery on site before the start of the construction period - the number of the j-th type of construction machinery on site after the end of the construction period. That is: ∑g ij I+1i=1=∑h ij I+1i=1+s j -e j Therefore, h can be used. ij Linear substitution g ij .

[0300] At the same time, s j -e j Since the cost is constant, even when combined with the cost of a single deployment, although it will affect the objective function value, it will not affect the choice of solution. Therefore, the constant cost can be removed during algorithm solution.

[0301] On the other hand, the optimized solution will only be given if the model has a feasible solution. Therefore, we only need to consider the case where the model has a feasible solution.

[0302] In summary, the original expression is equivalent to the following mathematical model: (The model corresponds to the Model_Optimal in the algorithm source code.)

[0303] min C = C0 + C1 + C2 + C3 + C5

[0304]

[0305] X ij ≤N j i∈[1,I+2],j∈[1,J]

[0306] hij ≥X ij -X i-1,j i∈[1,I+1],j∈[1,J]

[0307]

[0308] (N j +1)θ ijt ≥X ij -t+1,i∈[1,I],j∈[1,J],t∈[1,N j ]

[0309] Z j ≥X ij i∈[1,I],j∈[1,J]

[0310]

[0311] z jt ≤v j ,j∈[1,J],t∈[1,N j ]

[0312]

[0313] z jt ≥v j -Mμ jt ,j∈[1,J],t∈[1,N j ]

[0314] Y ijk =0,i∈[1,I],j∈[1,J],k∈[1,K],β jk =0

[0315] μ jt ∈{0,1},j∈[1,J],t∈[1,N j ]

[0316] X ij ∈[0,N j ], i∈[1,I], j∈[1,J]

[0317] h ij ≥0, i∈[1,I+1],j∈[1,J]

[0318] The source code corresponding to the above method is as follows:

[0319] class AlgoModel(object):

[0320] Algorithm Model Main Class

[0321] dataset_files=["attribution.csv","zhuanhuanxishu.csv","youxianji.csv","jihe.csv","worktime.csv","demand.csv"]

[0322] file_name_no_solution="result_no_solution.csv"

[0323] file_name_cost="result_cost.csv"

[0324] file_name_machine_status="result_machine_status.csv"

[0325] file_name_machine_worktime="result_machine_worktime.csv"

[0326] The algorithm model class includes: data acceptance, data validity verification, data splitting, parallel computing, merging computing results, and returning results.

[0327] def run_algo(self, base_path=None, split_judge_interval_days=1, split_compute_interval_days=30):

[0328] The unified function entry point for running the algorithm calculations connects the various steps and returns the calculation results.

[0329] defcheck_data(self,dataset_path=None):

[0330] """

[0331] Check the dataset

[0332] Compliance

[0333] """

[0334] Check the reasonableness and compliance of the data.

[0335] defsplit_dataset(self,base_path,sub_dirname="sub_A",days=1):

[0336] """

[0337] Cutting the dataset

[0338] Cutting particle size in units of specified days

[0339] """

[0340] Data splitting and efficient computation based on computational units.

[0341] defparallel_processes(self,func,args_list,timeout=1024):

[0342] Parallel computing - multi-process (multi-core) computing model

[0343] Parallel computing improves computational efficiency.

[0344] defmerge_dataset(self,sub_path_list=None, base_path=None, merge_type="has_solution"):

[0345] """

[0346] Merging Subdatasets

[0347] """

[0348] Combine the calculation results and return whether there is a solution.

[0349] Furthermore, the server is also equipped with a task execution result storage module. After step S4, the method further includes:

[0350] S5, the task execution unit sends the obtained optimal solution and the corresponding total cost of the construction project to the task execution result storage module for storage.

[0351] Furthermore, in step S2, the client specifically initiates a mechanical resource optimization configuration task via API.

[0352] After step S4, the method further includes:

[0353] S6 receives task status query requests from clients via API.

[0354] The execution status of the mechanical resource optimization and allocation task in the task execution unit is returned to the client.

[0355] S7 receives task result query requests from clients via API.

[0356] The optimal solution obtained from the task execution unit and the corresponding total cost of the construction project are returned to the client.

[0357] It provides API interfaces for initiating computing tasks, querying their status, and retrieving results to facilitate system integration and user access.

[0358] Furthermore, when there are multiple mechanical resource optimization and configuration tasks in the task scheduling queue, after extracting the mechanical resource optimization and configuration tasks from the task scheduling queue, steps S3-S4 are executed for each mechanical resource optimization and configuration task in the extraction order, or steps S3-S4 are executed for each mechanical resource optimization and configuration task according to the planned time.

[0359] In other words, the method provided in this application embodiment is divided into the following parts:

[0360] On the mechanical resource efficiency conversion page of the engineering cost management system, the client clicks the "Automatically calculate the quantity of machinery" function button. The program calculates and processes the data such as the type, quantity, efficiency conversion coefficient, and priority of budgeted machinery and construction machinery into the format required by the algorithm, and then uploads it to the server for linear programming calculation.

[0361] The core of the method consists of the following parts:

[0362] File application service: Provides file upload and download, mainly including: files uploaded by the client such as the number of owned machines (the quantity, type and status information of existing machines) and the daily budgeted machine requirements (daily machine requirement plan, including the expected construction tasks and the required machines); and files provided by the algorithm application service such as the on-site construction machine plan and the budgeted machine replacement plan.

[0363] Algorithm application services: such as Figure 3 As shown, it provides functions such as initiating computing tasks, querying status, and querying results via API, enabling efficient interaction between different parts of the system.

[0364] The distributed task scheduling module can be roughly divided into three parts: task scheduling queue, task execution unit, and task execution result storage.

[0365] (1) The task scheduling queue is an independent service and a producer-consumer pattern. Producers put tasks into the queue, and consumers take tasks out of the task queue to execute. Tasks can be executed sequentially or according to the planned time.

[0366] It supports sequential execution and scheduled execution of tasks, improving flexibility.

[0367] (2) The task execution unit is the program that executes the task, and there can be multiple concurrent processes. It monitors the message queue in real time, retrieves the scheduled tasks in the queue, and executes them.

[0368] This is the program module that actually performs the computation. Multiple concurrent instances can be deployed to monitor the message queue in real time and retrieve tasks to be executed. Each instance can run independently, improving overall processing efficiency.

[0369] (3) The task execution result storage module exists because task execution is separate from the main program. If the main program wants to obtain the task execution result, it must store it through middleware. If it does not need to save the execution result, this module can be omitted. Storing the execution result in middleware makes it convenient for the main program to retrieve it when needed. The design of this module enables the system to effectively manage the calculation results and support subsequent analysis and decision-making.

[0370] Using APIs to initiate computational tasks, query status, and query results means that the system provides a set of interfaces that allow users or other systems to interact with it programmatically. Specifically:

[0371] Task initiation: Users can submit new computing tasks via API.

[0372] Status query: Users can check the current status of a task, such as whether it is being executed or has been completed.

[0373] Results Query: Once the task is completed, users can retrieve the calculation results via API.

[0374] This approach enhances the system's flexibility and automation, making integration and operation more efficient. The overall solution implements a multi-task concurrent computation scheme that integrates algorithm task-based computation, a customized algorithm scheduling framework, algorithm engineering, and data feedback via HTTP.

[0375] The algorithm application service technology stack diagram of the method provided in this application embodiment can be found in [reference needed]. Figure 4 The interaction diagram of the distributed task scheduling module can be found in [reference needed]. Figure 5 .

[0376] Based on the method provided in the embodiments of this application, the distributed computing method is used to realize multi-task concurrent computing of the solution algorithm. This can help engineering construction companies to efficiently and automatically complete the distributed budget of construction machinery in engineering cost management, while maximizing the utilization of system resources and dynamically expanding the computing nodes, thus making full use of system resources and improving computing efficiency and system performance.

[0377] This application realizes the application of distributed computing method for multi-task concurrent computing in the engineering cost management system by processing the calculation process into tasks, executing concurrent calculations according to business scheduling rules, and obtaining calculation results.

[0378] This application realizes the distributed computation and deployment of linear programming algorithms. It breaks away from the traditional single-task, single-process approach used in similar computations, thereby maximizing the utilization of system resources and enabling dynamic expansion of computing nodes.

[0379] Because the engineering cost management system integrates multi-task concurrent computation of the solution algorithm, users can not only support the automatic calculation of the optimal solution for construction machinery configuration, thus improving the economy of the construction machinery configuration results, but also maximize the utilization of system resources through multi-task concurrent computation.

[0380] This application is integrated into the "Mechanical Resource Efficiency Conversion" page of the engineering cost management system. The file application service is developed using the Java programming language and frameworks such as Spring Boot and Spring Framework, while the algorithm application service is developed using the Python programming language and frameworks such as Flask and SQLAlchemy. It can be deployed on any Windows or Linux system on a virtual machine server.

[0381] Using the method provided in the embodiments of this application, a random overseas project was tested, and the results are as follows:

[0382] 1) Small sample size data test results: Replacing 5 types of budgeted machinery with 6 types of construction machinery took 18 working days. The algorithm took 2.23 seconds, and the cost was reduced by 1.08% compared to the original.

[0383] 2) Project test results: Cost calculation was reduced by 0.8% compared to the original, improving efficiency while reducing project costs.

[0384] The method provided in this application embodiment coordinates and manages all mechanical equipment through algorithms, outputting the distribution quantity and total cost of the machinery, which can further promote the refined management of various unit costs (unit usage cost, unit on-site idle cost, unit depreciation cost, single entry and exit cost, etc.).

[0385] By applying algorithmic constraints to meet the project's budgeted machinery requirements, optimal resource allocation is achieved while minimizing project costs, providing machinery schedulers with cost-optimized configuration references. The use of mixed-integer programming algorithms based on operations research enhances the automation and intelligence of cost management software.

[0386] This application uses a mathematical model to solve problems in an engineering cost management system based on known data and constraints. The algorithm needs to optimize and replace the budgeted machinery with existing construction machinery to meet the budgeted machinery requirements while minimizing the total project cost.

[0387] The method provided in this application, by utilizing a task scheduling queue, a task execution unit, and a constructed mechanical configuration algorithm model, can quickly provide the optimal construction machinery configuration scheme based on the mechanical resource optimization configuration task initiated by the client without relying on manual intervention. This achieves automated processing of mechanical resource optimization configuration tasks. Compared with the traditional method of manually inputting data, it can significantly improve processing speed and accuracy and reduce the possibility of human error.

[0388] Traditional methods rely on manual data input, which is susceptible to human error and leads to inaccurate results. The method provided in this application, however, eliminates manual intervention through real-time monitoring and automatic data extraction, improving processing efficiency and accuracy. The task scheduling queue and task execution unit setup enable the system to handle multiple mechanical resource optimization and allocation tasks simultaneously, overcoming the single-task processing bottleneck of traditional methods and improving overall construction efficiency.

[0389] By using a mechanical configuration algorithm model, this method can comprehensively consider various parameters and quickly provide the optimal solution for the configuration of construction machinery, avoiding the problem of unreasonable configuration caused by the lack of systematicity in traditional methods.

[0390] In summary, this engineering budget processing method based on budget machinery requirements achieves efficient automated calculations by setting up task scheduling queues and task execution units on the server. The constructed machinery configuration algorithm model can respond in real time to the client's machinery resource optimization configuration tasks, extract known parameters, and then quickly calculate the optimal construction machinery configuration scheme and the corresponding total construction project cost. This process significantly improves work efficiency, reduces errors caused by manual input, and the automated data monitoring and processing mechanism ensures the real-time nature and accuracy of information, thus overcoming the inefficiency and inaccuracy problems caused by reliance on manual input in traditional methods. Furthermore, this method flexibly adapts to different project needs, optimizes resource allocation, effectively reduces engineering costs, and provides enterprises with a scientific and reasonable budget management solution.

[0391] In other words, the method provided in the embodiments of this application has the following characteristics:

[0392] High efficiency: Through automated calculation and distributed processing, the efficiency of construction machinery configuration is greatly improved and manual intervention is reduced.

[0393] Economic efficiency: The mechanical distribution algorithm can provide the optimal mechanical configuration scheme for the project, thereby reducing the overall project cost.

[0394] Flexibility: The system can quickly respond to changing needs and conditions and adapt to dynamic construction environments.

[0395] Scalability: The API-based architecture facilitates future feature expansion and integration, and supports interoperability with other management systems.

[0396] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0397] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities, and the network interface enables communication with external terminals via a network connection. The computer device loads and runs computer programs to implement the aforementioned engineering budget processing method based on budgetary mechanical requirements.

[0398] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0399] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored relating to all or part of the processes in the methods of the above embodiments.

[0400] In one embodiment, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement all or part of the processes in the methods of the above embodiments.

[0401] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for processing engineering budgets based on budgeted mechanical requirements, characterized in that, Applied to a server, wherein the server is equipped with a task scheduling queue and a task execution unit, the method includes: S1, Construct the mechanical configuration algorithm model; S2, the task execution unit monitors the task scheduling queue in real time, and in response to the client initiating a mechanical resource optimization configuration task, extracts the mechanical resource optimization configuration task initiated by the client from the task scheduling queue; S3, the task execution unit extracts the known parameters uploaded by the client from the mechanical resource optimization and configuration task; S4, the task execution unit solves the mechanical configuration algorithm model according to the known parameters to obtain the optimal solution of the mechanical configuration algorithm model. The optimal solution is the construction machinery configuration scheme that minimizes the total cost of the construction project, and calculates the corresponding total cost of the construction project for the optimal solution. When constructing the mechanical configuration algorithm model, the objective function of the mechanical configuration algorithm model is to minimize the total cost of the construction project. The decision variables of the mechanical configuration algorithm model include the number of construction machines on site and the total number of machine shifts of the replacement budgeted machines. The constraints of the mechanical configuration algorithm model are multiple constraints to ensure that the construction machine configuration scheme meets the requirements of the mechanical resource optimization configuration task. The objective function of the mechanical configuration algorithm model is specifically: minC=C0+C1+C 2_1 +C 2_2 +C3+C4+C5+C6; Where C0 is the idle cost of construction machinery, C1 is the cost of using construction machinery, and C 2_1 To calculate the depreciation cost of construction machinery based on the number of days it is on-site, C 2_2 To calculate depreciation costs based on fixed depreciation costs, C3 is the cost of dispatching construction machinery to the site, C4 is the cost of dispatching construction machinery to the site, C5 is the priority penalty cost, and C6 is the penalty cost for no solution. The constraints of the mechanical configuration algorithm model specifically include: The first constraint is the number of times the j-th type of construction machinery can enter the site; The second constraint is the number of times the j-th type of construction machinery will be used. The third constraint is that the number of shifts in which construction machinery replaces budgeted machinery shall not exceed the maximum available number of shifts. The fourth constraint is to consider the efficiency conversion factor when replacing budgeted machinery with construction machinery; The fifth constraint is that when calculating depreciation costs based on the number of days the construction machinery was on-site, the number of depreciation days shall not exceed the remaining depreciation period; when calculating depreciation costs based on fixed depreciation costs, it shall be determined whether the construction machinery was on-site. The sixth constraint prioritizes construction machinery based on the added penalty cost, selecting the construction machinery with the smallest penalty through the objective function of cost optimization. The seventh constraint is that construction machinery cannot replace budget machinery with a conversion factor of 0.

2. The engineering budget processing method based on budgeted machinery requirements according to claim 1, characterized in that, Where, j∈[1,J] is the construction machinery index, J=J1+J2+J3, J1 represents the set of owned machinery, J2 represents the set of newly purchased machinery, and J3 represents the set of leased machinery; i∈[1,I] is the construction period index, I represents the construction period set; k∈[1,K] is the budgeted machinery index, K represents the budgeted machinery set; c 0,j Let η be the unit on-site idle cost of construction machinery of type j, j∈[1,J]; ij X represents the maximum number of working shifts for each type of construction machinery in the i-th category; ij Let X be the number of construction machines of type j on day i, i∈[0,I+1], j∈[1,J], and X be the number of construction machines of type j on site. 0j X represents the number of construction machines of type j present before the start of the construction period. I+1,j Y represents the number of construction machines of type j on site after the completion of the construction period; ijk Let i be the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, where i∈[1,I], j∈[1,J], k∈[1,K]. Among them, c 1,j Let J be the unit cost of using the j-th type of construction machinery, where j∈[1,J]. Among them, c 2_1,j Let z be the unit depreciation cost of the j-th type of construction machinery, j∈[1,J]; jt Let t be the actual depreciation days of the t-th construction machine in the j-th category of construction machinery, j∈[1,J], t∈[1,N] j ], N j Let N be the number of construction machines of type j, j∈[1,J]; jt Let be the maximum number of working shifts for each type of construction machinery in the i-th day, i∈[1,I],j∈[1,J]; Among them, c 2_2,j Z represents the fixed depreciation cost of the j-th type of construction machinery, where j∈[1,J]; j Let J be the maximum number of construction machines of type j on site, where j∈[1,J]. Among them, c 3,j Let h be the single entry cost of the j-th type of construction machinery, j∈[1,J]; ij Let be the number of construction machines of type j that enter the site on day i, where i∈[1,I+1], j∈[1,J]; Among them, c 4,j Let g be the single-trip cost of the j-th type of construction machinery, j∈[1,J]; ij Let represent the number of construction machines of type j that appear on day i, where i∈[1,I+1], j∈[1,J]; Among them, c 5,j,k The penalty cost for replacing the j-th type of budgeted machinery with the j-th type of construction machinery, where j∈[1,J] and k∈[1,K]; Where c6 is the penalty cost when the k-th type of budgetary machinery demand cannot be met, k∈[1,K]; τ ik When replacing budgeted machinery with construction machinery, the amount of unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K].

3. The engineering budget processing method based on budgeted machinery requirements according to claim 1, characterized in that, The first constraint is specifically: h ij ≥X ij -X i-1,j ,i∈[1,I+1],j∈[1,J] h ij ≥0,i∈[1,I+1],j∈[1,J] Among them, h ij Let X be the number of construction machines of type j entering the site on day i, where i∈[1,I+1], j∈[1,J]; ij h represents the number of construction machines of type j on day i, where i∈[0,I+1], j∈[1,J]; ij and X ij All are integer variables; The second constraint is as follows: g ij ≥X i-1,j -X ij ,i∈[1,I+1],j∈[1,J] g ij ≥0,i∈[1,I+1],j∈[1,J] Where, i∈[1,I+1], j∈[1,J], g ij For the number of construction machines of type j that appear on day i, g ij ≥0; The third constraint is specifically: Among them, Y ijk For the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, i∈[1,I], j∈[1,J], k∈[1,K]; η ij This represents the maximum number of working shifts for each type of construction machinery in the i-th day; The fourth constraint is as follows: t ik ≥0,i∈[1,I],k∈[1,K] Where, β jk The conversion factor for replacing the j-th type of construction machinery with the k-th type of budget machinery, where j∈[1,J], k∈[1,K]; Y ijk *β jk τ represents the effective number of shifts in which construction machinery of type j replaces budgeted machinery of type k on day i; ik When replacing budgeted machinery with construction machinery, the unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K]; D ik Let i be the number of machine shifts required for the k-th type of budgeted machinery on day i, where i∈[1,I] and k∈[1,K]. The fifth constraint specifically includes: Depreciation costs are calculated based on the number of days on-site: (N j +1)θ ijt ≥X ij -t+1,i∈[1,I],j∈[1,J],t∈[1,N j ] Where, N j Let θ be the number of construction machines of type j, j∈[1,J]; ijt This indicates whether the t-th construction machine of type j is present on day i, where i∈[1,I],j∈[1,J],t∈[1,Nj]; N jt Let v be the maximum number of working shifts for each type of construction machinery in the i-th day, i∈[1,I],j∈[1,J]; j Let J be the remaining depreciation period for the j-th type of construction machinery, where j∈[1,J]. Depreciation costs are calculated based on fixed depreciation costs: Z j ≥X ij ,i∈[1,I],j∈[1,J], Among them, Z j Let J be the maximum number of construction machines of type j on site, where j∈[1,J].

4. The engineering budget processing method based on budgeted machinery requirements according to claim 2, characterized in that, Step S4 also includes: When solving the mechanical configuration algorithm model, it is determined whether the mechanical configuration algorithm model has a feasible solution; If no feasible solution is determined, stop the subsequent model solving and output a prompt message to inform the recipient of the budgeted mechanical requirements that cannot be met in the mechanical resource optimization and allocation task.

5. The engineering budget processing method based on budgeted machinery requirements according to claim 4, characterized in that, When determining whether the mechanical configuration algorithm model has a feasible solution, the impact of cost is ignored, and a simplified model is used to determine whether there is a feasible solution; the simplified model is expressed as: min C=C6 t ik ≥0,i∈[1,I],k∈[1,K] Y ijk ≥0,i∈[1,I],j∈[1,J],k∈[1,K] Where, τ ik ≥0, τ ik When replacing budgeted machinery with construction machinery, the unmet demand for budgeted machinery of type k on day i, i∈[1,I],k∈[1,K]; Y ijk ≥0, i∈[1,I], j∈[1,J], k∈[1,K]; Y ijk For the number of shifts in which the j-th type of construction machinery replaces the k-th type of budgeted machinery on day i, i∈[1,I], j∈[1,J], k∈[1,K]; η ij N represents the maximum number of working shifts for each type of construction machinery in the i-th day; j Let β be the number of construction machines of type j, j∈[1,J]; jk The conversion factor for replacing the j-th type of construction machinery with the k-th type of budget machinery, j∈[1,J], k∈[1,K]; D ik Let be the shift requirement of the k-th type of budgeted machinery on day i, where i∈[1,I] and k∈[1,K]. When C6 is 0, it indicates that the model has a feasible solution and can continue with further optimization; when C6 is positive, it indicates that the model has no feasible solution, and in this case, the existence of τ is given. ik If the budgeted mechanical value k is greater than 0, then stop solving the model.

6. The engineering budget processing method based on budgeted machinery requirements according to claim 1, characterized in that, The server is also equipped with a task execution result storage module. After step S4, the method further includes: S5, the task execution unit sends the obtained optimal solution and the corresponding total cost of the construction project to the task execution result storage module for storage.

7. The engineering budget processing method based on budgeted machinery requirements according to claim 1, characterized in that, In step S2, the client initiates a mechanical resource optimization configuration task via API. After step S4, the method further includes: S6 receives task status query requests from clients via API. The execution status of the mechanical resource optimization and allocation task in the task execution unit is returned to the client; S7 receives task result query requests from clients via API. The optimal solution obtained from the task execution unit and the corresponding total cost of the construction project are returned to the client.

8. The engineering budget processing method based on budgeted machinery requirements according to claim 1, characterized in that, When there are multiple mechanical resource optimization and configuration tasks in the task scheduling queue, after extracting the mechanical resource optimization and configuration tasks from the task scheduling queue, steps S3-S4 are executed for each mechanical resource optimization and configuration task in the extraction order, or steps S3-S4 are executed for each mechanical resource optimization and configuration task according to the planned time.

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