Construction progress and construction cost collaborative management method, system, equipment and medium

Abstract

The invention relates to a construction progress and construction cost collaborative management method, system and device and a medium. The method comprises the following steps: inputting resource demand data, construction period constraint and cost parameters of each key node, construction progress data, resource consumption data and environmental parameters of a current pumped storage project into a pre-trained construction progress and construction cost correlation model; and associating the construction progress and the construction cost of the current pumped storage project through the pre-trained construction progress and construction cost association model. Whether the construction progress and the construction cost of the current pumped storage project have deviation or not is judged, if deviation exists, an early warning mechanism is triggered, and a current construction scheme is optimized based on a multi-target genetic algorithm. Through the method, collaborative management of the construction progress and the construction cost of the pumped storage project can be realized, the problems of unreasonable resource allocation, risk response lag and the like are avoided, and the method has the characteristics of pertinence, flexibility and capability of adapting to the pumped storage project.

Description

Technical Field

[0001] This disclosure relates to the field of engineering management technology, and in particular to a method, system, equipment and medium for collaborative management of construction progress and construction cost. Background Technology

[0002] Pumped storage power stations can convert excess electrical energy into the potential energy of water and store it during periods of low load in the power system. During periods of high load, the potential energy of the water can be converted back into electrical energy and fed back to the power grid. This system plays a role in peak shaving and valley filling, as well as ensuring the stable operation of the power system. It is an efficient and clean energy storage method that plays an important role in the power system.

[0003] Pumped storage projects typically involve huge investments and long construction periods, making schedule and cost control key factors for project success.

[0004] The current schedule and cost management model for pumped storage projects lacks real-time data interaction and dynamic collaboration mechanisms, which can easily lead to problems such as unreasonable resource allocation and delayed risk response. At the same time, the adjustment relies on static plans and manual experience, which makes it difficult to adapt to the impact of dynamic factors such as complex geological conditions and equipment supply fluctuations on the construction period and cost. In other words, it lacks pertinence and flexibility and is difficult to adapt to the characteristics of pumped storage projects. Summary of the Invention

[0005] To address the aforementioned technical problems, or at least partially address them, this disclosure provides a method, system, equipment, and medium for collaborative management of construction progress and construction costs. This method enables collaborative management of construction progress and construction costs in pumped storage projects, avoiding issues such as unreasonable resource allocation and delayed risk response. It is targeted and flexible, and adaptable to pumped storage projects.

[0006] In a first aspect, embodiments of this disclosure provide a method for collaborative management of construction progress and construction costs, the method comprising:

[0007] Construct a building information model for the current pumped storage project, and decompose the construction tasks of the current pumped storage project into key nodes based on the building information model, and associate the resource demand data, schedule constraints and cost parameters of each key node.

[0008] Real-time collection of construction progress data, resource consumption data, and environmental parameters of the current pumped storage project;

[0009] The resource demand data, schedule constraints, cost parameters, current pumped storage project construction progress data, resource consumption data, and environmental parameters of each key node are input into a pre-trained construction progress and construction cost correlation model. The construction progress and construction cost of the current pumped storage project are then correlated through the pre-trained construction progress and construction cost correlation model.

[0010] If there are any discrepancies between the current construction progress and construction cost of the pumped storage project, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

[0011] In some embodiments, the method further includes:

[0012] Obtain relevant data on historical pumped storage projects;

[0013] A construction progress and construction cost correlation model is constructed. The model is trained and tested using relevant data from historical pumped storage projects until convergence, resulting in a pre-trained construction progress and construction cost correlation model.

[0014] The relevant data of the historical pumped storage projects include the key nodes corresponding to the construction tasks of the historical pumped storage projects, the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the construction progress data, resource consumption data, and environmental parameters of the historical pumped storage projects.

[0015] In some embodiments, the construction progress and construction cost correlation model uses cost overrun rate and schedule deviation rate as dual objectives for optimization, and environmental parameters as risk factors and constraints.

[0016] The correlation model between construction progress and construction cost is expressed as follows:

[0017] min[α*(T_actual-T_plan) / T_plan+β*(C_actual-C_plan) / C_plan]

[0018] Where α and β represent weighting coefficients; (T_actual-T_plan) / T_plan represents the deviation ratio of the real-time collected construction progress data compared to the associated schedule constraints; (C_actual-C_plan) / C_plan represents the deviation ratio of the real-time collected resource consumption data compared to the associated resource demand data and cost parameters.

[0019] The constraints are: R_risk ≤ R_max, T_i ≤ T_deadline

[0020] Among them, R_risk≤R_max is a risk constraint, which means that the risk value R_risk faced by the pumped storage project cannot exceed the pre-set maximum risk threshold R_max; T_i≤T_deadline is a time constraint, which means that the task time T_i of each key node must be controlled within the final deadline T_deadline.

[0021] In some embodiments, determining whether there is a deviation between the current construction progress and construction cost of the pumped storage project includes:

[0022] Monitor whether the cost overrun rate and schedule deviation rate are greater than their respective preset thresholds, the preset thresholds including preset overrun rate threshold and preset deviation rate threshold;

[0023] If the cost overrun rate is greater than the preset overrun rate threshold, or the schedule deviation rate is greater than the preset deviation rate threshold, then it is determined that there is a deviation in the construction progress and construction cost of the current pumped storage project.

[0024] In some embodiments, optimizing the current construction scheme based on a multi-objective genetic algorithm includes:

[0025] Two optimization objectives are set: minimizing construction time and minimizing construction cost.

[0026] The pre-set data for construction schedule and construction cost are used as decision variables and coded to obtain a coding scheme.

[0027] Based on the coding scheme, multiple construction plans are randomly generated, each of which includes a construction schedule plan and a construction cost plan.

[0028] Calculate the fitness function value for each construction scheme, and then filter based on the fitness function value of each construction scheme to obtain multiple candidate construction schemes;

[0029] Based on multiple candidate construction schemes, the target construction scheme was determined;

[0030] The current construction plan is adjusted based on the target construction plan.

[0031] In some embodiments, the step of filtering based on the fitness function value of each construction scheme to obtain multiple candidate construction schemes includes:

[0032] Construction schemes with fitness function values ​​below a preset threshold are filtered out to obtain multiple candidate construction schemes.

[0033] In some embodiments, determining the target construction scheme based on multiple candidate construction schemes includes:

[0034] Multiple candidate construction schemes are selected, cross-referenced, and mutated to generate a variety of alternative construction schemes.

[0035] The various alternative construction schemes are divided into various non-dominated levels, and each non-dominated level includes at least one alternative construction scheme.

[0036] Calculate the congestion degree of at least one candidate construction scheme at each non-dominated level, filter out candidate construction schemes with congestion degree lower than a preset congestion degree threshold, and obtain the filtered candidate construction schemes.

[0037] The filtered candidate construction schemes are identified as multiple candidate construction schemes. The steps in this method are repeated until the preset termination condition is met, and then the target construction scheme is determined from the current filtered candidate construction schemes.

[0038] Secondly, embodiments of this disclosure provide a collaborative management system for construction progress and construction cost, the system comprising:

[0039] The first construction unit is used to construct the building information model of the current pumped storage project. Based on the building information model, the construction tasks of the current pumped storage project are decomposed into key nodes, and the resource demand data, schedule constraints, and cost parameters of each key node are associated.

[0040] The data acquisition unit is used to collect real-time data on the construction progress, resource consumption, and environmental parameters of the current pumped storage project.

[0041] The association unit is used to input the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the construction progress data of the current pumped storage project, the resource consumption data, and environmental parameters into the pre-trained construction progress and construction cost association model. The pre-trained construction progress and construction cost association model is used to associate the construction progress and construction cost of the current pumped storage project.

[0042] The management unit is used to determine whether there are deviations in the construction progress and construction cost of the current pumped storage project. If there are deviations, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

[0043] Thirdly, embodiments of this disclosure provide an electronic device, including:

[0044] Memory;

[0045] Processor; and

[0046] Computer programs;

[0047] The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect.

[0048] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method as described in the first aspect.

[0049] Fifthly, embodiments of this disclosure also provide a computer program product comprising a computer program or instructions that, when executed by a processor, implement the method described in the first aspect.

[0050] The construction progress and construction cost collaborative management method, system, equipment, and medium provided in this disclosure construct a building information model (BIM) of the current pumped storage project. Based on the BIM, the construction tasks of the current pumped storage project are decomposed into key nodes, and the resource demand data, schedule constraints, and cost parameters of each key node are associated. Real-time data collection of the current pumped storage project's construction progress, resource consumption, and environmental parameters is also performed. Next, the resource demand data, schedule constraints, cost parameters, construction progress data, resource consumption data, and environmental parameters of each key node are input into a pre-trained construction progress and construction cost association model. This model links the current pumped storage project's construction progress and construction cost. The system then determines whether there is a deviation between the current pumped storage project's construction progress and construction cost. If a deviation exists, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm. This method enables coordinated management of the construction progress and cost of pumped storage projects, avoiding problems such as unreasonable resource allocation and delayed risk response. It is targeted and flexible, and can adapt to the characteristics of pumped storage projects. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0052] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart of a collaborative management method for construction progress and construction cost provided in this embodiment of the disclosure;

[0054] Figure 2 A schematic diagram of the overall process of a collaborative management method for construction progress and construction cost provided in another embodiment of this disclosure;

[0055] Figure 3 A flowchart of a collaborative management method for construction progress and construction cost provided in another embodiment of this disclosure;

[0056] Figure 4 A schematic diagram of the structure of the collaborative management system for construction progress and construction cost provided in this embodiment of the disclosure;

[0057] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0058] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0059] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0060] Pumped storage power stations can convert excess electrical energy into the potential energy of water and store it during periods of low load in the power system. During periods of high load, the potential energy of the water can be converted back into electrical energy and fed back to the power grid. This system plays a role in peak shaving and valley filling, as well as ensuring the stable operation of the power system. It is an efficient and clean energy storage method that plays an important role in the power system.

[0061] Pumped storage projects typically involve huge investments and long construction periods, making schedule and cost control key factors for project success.

[0062] The current schedule and cost management model for pumped storage projects lacks real-time data interaction and dynamic collaboration mechanisms, which can easily lead to problems such as unreasonable resource allocation and delayed risk response. At the same time, the adjustment relies on static plans and manual experience, which makes it difficult to adapt to the impact of dynamic factors such as complex geological conditions and equipment supply fluctuations on the construction period and cost. In other words, it lacks pertinence and flexibility and is difficult to adapt to the characteristics of pumped storage projects.

[0063] To address this issue, this disclosure provides a method for collaborative management of construction progress and construction costs, which will be described below with reference to specific embodiments.

[0064] Figure 1This is a flowchart illustrating the collaborative management method for construction progress and construction costs provided in this embodiment. The method is executed by an electronic device. This device can be a portable mobile device such as a smartphone, tablet, laptop, in-vehicle navigation device, or smart sports equipment; or a fixed device such as a personal computer, smart home appliance, or server. The server can be a single server, a server cluster, or a distributed or centralized cluster. This method can be applied to scenarios involving the collaborative management of construction progress and construction costs. It is understood that the collaborative management method for construction progress and construction costs provided in this embodiment can also be applied to other scenarios.

[0065] Figure 2 This is a schematic diagram of the overall process of a collaborative management method for construction progress and construction cost provided in another embodiment of this disclosure. The following is in conjunction with... Figure 2 ,right Figure 1 The following describes a collaborative management method for construction progress and construction costs, which includes the following specific steps:

[0066] S101. Construct a building information model for the current pumped storage project. Based on the building information model, decompose the construction tasks of the current pumped storage project into key nodes and associate the resource demand data, schedule constraints, and cost parameters of each key node.

[0067] In this step, such as Figure 2 As shown, before construction, a Building Information Model (BIM) of the current pumped storage project is constructed. Based on the BIM model, the construction tasks are decomposed into key nodes, and the resource requirements, schedule constraints and cost parameters of each key node are associated.

[0068] S102. Real-time collection of construction progress data, resource consumption data, and environmental parameters of the current pumped storage project.

[0069] In this step, such as Figure 2 As shown, during construction, electronic equipment will collect real-time data on the construction progress, resource consumption, and environmental parameters of the pumped storage project.

[0070] S103. Input the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the current construction progress data of the pumped storage project, the resource consumption data, and the environmental parameters into the pre-trained construction progress and construction cost correlation model. Then, the construction progress and construction cost of the current pumped storage project are correlated through the pre-trained construction progress and construction cost correlation model.

[0071] In this step, such as Figure 2As shown, during the construction process, the resource demand data, schedule constraints, and cost parameters of each key node, as well as the real-time collected construction progress data, resource consumption data, and environmental parameters of the current pumped storage project, are transmitted to the pre-trained construction progress and construction cost correlation model. The construction progress and construction cost correlation model dynamically correlates the construction progress and cost of the current pumped storage project.

[0072] S104. Determine whether there are deviations in the current construction progress and construction cost of the pumped storage project. If there are deviations, trigger the early warning mechanism and optimize the current construction plan based on a multi-objective genetic algorithm.

[0073] In this step, the electronic equipment will determine whether there are any discrepancies between the current construction progress and construction cost of the pumped storage project. For example... Figure 2 As shown, if there is a deviation between the construction progress and cost, an early warning mechanism will be triggered, and the current construction plan will be optimized and adjusted based on a multi-objective genetic algorithm. Optionally, the multi-objective genetic algorithm can be an improved NSGA-II multi-objective genetic algorithm, which is not limited here.

[0074] This embodiment of the disclosure constructs a building information model (BIM) for the current pumped storage project. Based on the BIM, the construction tasks of the current pumped storage project are decomposed into key nodes, and the resource requirements, schedule constraints, and cost parameters of each key node are associated. Real-time data collection of the construction progress, resource consumption, and environmental parameters of the current pumped storage project is also performed. Next, the resource requirements, schedule constraints, cost parameters, construction progress, resource consumption, and environmental parameters of each key node are input into a pre-trained construction progress and cost correlation model. This model correlates the construction progress and cost of the current pumped storage project. It then determines whether there is a deviation between the construction progress and cost of the current pumped storage project. If a deviation exists, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm. This method enables coordinated management of the construction progress and cost of pumped storage projects, avoiding problems such as unreasonable resource allocation and delayed risk response. It is targeted and flexible, and can adapt to the characteristics of pumped storage projects.

[0075] Figure 3 A flowchart of a collaborative management method for construction progress and construction cost provided in another embodiment of this disclosure is shown below. Figure 3 As shown, the method includes the following steps:

[0076] S201. Obtain relevant data on historical pumped storage projects.

[0077] In this step, such as Figure 2 As shown, historical pumped storage project data will be collected before construction. The relevant data of historical pumped storage projects can include the resource requirements, schedule constraints and cost parameters of each key node of the construction task decomposition of historical pumped storage projects, as well as the construction progress data, resource consumption data and environmental parameters of each related key node. These are not limited here.

[0078] Optionally, the key nodes of the construction task decomposition can be set according to the actual situation of the pumped storage project, including but not limited to underground powerhouse excavation, pressure steel pipe installation and unit commissioning; the resource requirements, schedule constraints and cost parameters of each key node can be obtained by technical personnel based on experience analysis or by calculation model, without limitation; the correlation of resource requirement data, schedule constraints and cost parameters of each key node can be manually entered by technical personnel.

[0079] Optionally, construction progress data can be set according to the actual situation of the pumped storage project, including but not limited to excavation volume and concrete pouring progress; resource consumption data can be set according to the actual situation of the pumped storage project, including but not limited to materials, machinery and manpower; the equipment for collecting construction progress data and resource consumption data can be set according to the actual situation of the pumped storage project, including but not limited to drone inspection and RFID equipment; environmental parameters can be set according to the actual situation of the pumped storage project, including but not limited to geological changes and weather; the equipment for collecting environmental parameters can be set according to the actual situation of the pumped storage project, including but not limited to on-site sensors.

[0080] S202. Construct a model linking construction progress and construction cost. Train and test the model using relevant data from historical pumped storage projects until convergence, thus obtaining a pre-trained model linking construction progress and construction cost.

[0081] In this step, such as Figure 2 As shown, before construction, a construction progress and construction cost correlation model is constructed. The construction progress and construction cost correlation model is trained and tested by collecting resource demand, cost parameters and real-time resource consumption data associated with key nodes of historical pumped storage projects, as well as associated schedule constraints and real-time construction progress data, until convergence.

[0082] In some embodiments, the construction progress and construction cost correlation model uses cost overrun rate and schedule deviation rate as dual objectives for optimization, and environmental parameters as risk factors and constraints.

[0083] Optionally, the correlation model between construction progress and construction cost is expressed as follows:

[0084] min[α*(T_actual-T_plan) / T_plan+β*(C_actual-C_plan) / C_plan]

[0085] Where α and β represent weighting coefficients, which can be dynamically adjusted using the entropy weighting method; (T_actual-T_plan) / T_plan represents the deviation ratio of the real-time collected construction progress data compared to the associated schedule constraints; (C_actual-C_plan) / C_plan represents the deviation ratio of the real-time collected resource consumption data compared to the associated resource demand data and cost parameters.

[0086] The constraints are: R_risk ≤ R_max, T_i ≤ T_deadline

[0087] Among them, R_risk≤R_max is a risk constraint, which means that the risk value R_risk faced by the pumped storage project cannot exceed the pre-set maximum risk threshold R_max; T_i≤T_deadline is a time constraint, which means that the task time T_i of each key node must be controlled within the final deadline T_deadline.

[0088] S203. Construct a building information model for the current pumped storage project. Based on the building information model, decompose the construction tasks of the current pumped storage project into key nodes and associate the resource demand data, schedule constraints, and cost parameters of each key node.

[0089] Specifically, the implementation process and principle of S203 and S101 are the same, and will not be repeated here.

[0090] S204. Real-time collection of construction progress data, resource consumption data, and environmental parameters of the current pumped storage project.

[0091] Specifically, the implementation process and principle of S204 and S102 are the same, and will not be repeated here.

[0092] S205. Input the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the current construction progress data of the pumped storage project, the resource consumption data, and the environmental parameters into the pre-trained construction progress and construction cost correlation model. Then, use the pre-trained construction progress and construction cost correlation model to correlate the current construction progress and construction cost of the pumped storage project.

[0093] Specifically, the implementation process and principle of S205 and S103 are the same, and will not be repeated here.

[0094] S206. Monitor whether the cost overrun rate and schedule deviation rate are greater than their respective preset thresholds, wherein the preset thresholds include a preset overrun rate threshold and a preset deviation rate threshold.

[0095] In this step, the electronic equipment monitors whether the cost overrun rate and schedule deviation rate exceed their respective preset thresholds. These preset thresholds include a preset overrun rate threshold and a preset deviation rate threshold. The preset overrun rate threshold and preset deviation rate threshold can be adjusted according to actual conditions. Preferably, the preset overrun rate threshold can be 3%, and the preset deviation rate threshold can be 5%, without specific limitations.

[0096] S207. If the cost overrun rate is greater than the preset overrun rate threshold, or the schedule deviation rate is greater than the preset deviation rate threshold, then it is determined that there is a deviation in the construction progress and construction cost of the current pumped storage project.

[0097] In this step, if the cost overrun rate is found to be greater than the preset overrun rate threshold, or the schedule deviation rate is found to be greater than the preset deviation rate threshold, then it is determined that there is a deviation in the construction progress and construction cost of the current pumped storage project.

[0098] S208. If a deviation exists, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

[0099] In this step, when it is determined that there is a deviation between the current construction progress and construction cost of the pumped storage project, an early warning mechanism will be triggered, and the current construction plan will be optimized based on a multi-objective genetic algorithm.

[0100] In some embodiments, S208 may include, but is not limited to, S2081, S2082, S2083, S2084, S2085, and S2086:

[0101] S2081. Set two optimization objectives: minimizing construction progress and minimizing construction cost;

[0102] S2082. Encode the pre-set data of construction progress and pre-set data of construction cost as decision variables to obtain a coding scheme;

[0103] The construction progress preset data is the pre-set construction progress, and the construction cost preset data is the pre-set construction cost.

[0104] S2083. Based on the coding scheme, multiple construction schemes are randomly generated. Each construction scheme includes a construction schedule scheme and a construction cost scheme.

[0105] In this step, N individuals (i.e. construction plans) are randomly generated as the initial population according to the coding scheme. Each individual is a random construction schedule plan and construction cost plan.

[0106] S2084. Calculate the fitness function value of each construction scheme, and filter according to the fitness function value of each construction scheme to obtain multiple candidate construction schemes;

[0107] Furthermore, the fitness function value of each construction scheme is calculated, and multiple candidate construction schemes are obtained by screening based on the fitness function value of each construction scheme.

[0108] Optionally, the fitness function is:

[0109] F(i)=ω T f T (i)+ω C f C (i)

[0110] Where, ω T and ω C The weights for construction schedule and construction cost are represented by f. T (i) represents the fitness function of the construction schedule; f C (i) represents the fitness function of construction cost;

[0111] f T (i)=1 / (|T i -T0|+ε)

[0112] Among them, T i Let Ti represent the construction period for the i-th individual; T0 represent the standard construction period preset for the i-th individual; ε represents a positive number.

[0113] f C (i)=1 / (C i +ε)

[0114] Among them, C i Let represent the total construction cost of the i-th individual; ε represents a positive number.

[0115] In some embodiments, the step of filtering based on the fitness function value of each construction scheme to obtain multiple candidate construction schemes includes: filtering out construction schemes with fitness function values ​​lower than a preset threshold in each construction scheme to obtain multiple candidate construction schemes.

[0116] In this embodiment, the electronic device filters out construction schemes with fitness function values ​​below a preset threshold for each construction scheme, resulting in multiple candidate construction schemes. Essentially, it eliminates inferior individuals with fitness function values ​​below the preset threshold.

[0117] S2085. Based on multiple candidate construction schemes, determine the target construction scheme;

[0118] In some embodiments, S2085 may include steps A, B, C, and D:

[0119] Step A: Based on multiple candidate construction schemes, select, cross, and mutate to generate multiple alternative construction schemes;

[0120] In this step, selection, crossover, and mutation are performed based on multiple candidate construction schemes to generate various alternative construction schemes. Essentially, it generates a new population based on the selection, crossover, and mutation among the retained individuals.

[0121] Step B: Divide the multiple candidate construction schemes into various non-dominated levels, and each non-dominated level includes at least one candidate construction scheme;

[0122] In this step, the various candidate construction schemes will be divided into different non-dominated levels. For example, first, all non-dominated individuals in the new population are found. A non-dominated individual is an individual that is superior to any other individual in all objectives. This individual is classified into the first level. Then, non-dominated individuals are found among the remaining individuals and classified into the second level, and so on, until all populations are classified into different non-dominated levels.

[0123] Step C: Calculate the congestion degree of at least one candidate construction scheme at each non-dominated level, filter out candidate construction schemes with congestion degree lower than the preset congestion degree threshold, and obtain the filtered candidate construction schemes.

[0124] Furthermore, the congestion degree of at least one candidate construction scheme in each non-dominated level is calculated. Candidate construction schemes with congestion degrees below a preset congestion threshold are filtered out, resulting in filtered candidate construction schemes. Each candidate construction scheme can be considered as an individual, essentially eliminating inferior individuals with congestion degrees below the preset threshold in different non-dominated levels. The congestion degree expression is:

[0125]

[0126] In the formula: m represents the total number of individuals in the non-dominated hierarchy; k represents the number of individuals in the non-dominated hierarchy; d k (i) represents the crowding distance of the i-th individual.

[0127] Step D: Determine multiple candidate construction schemes from the filtered alternative construction schemes, repeat the steps in this method until the preset termination condition is met, and then determine the target construction scheme from the current filtered alternative construction schemes.

[0128] Furthermore, the filtered candidate construction schemes are identified as multiple candidate construction schemes. The steps in this method are repeated until a preset termination condition is met, at which point the target construction scheme is determined from the current filtered candidate construction schemes. For example, based on the selection, crossover, and mutation among the retained individuals, a new population is generated, and the above steps are repeated until a preset termination condition is met, at which point the optimal solution in the current population is output as the optimized target construction scheme.

[0129] S2086. Adjust the current construction plan based on the target construction plan.

[0130] In this step, the electronic equipment will adjust the current construction plan based on the target construction plan. The construction plan can be adjusted according to actual circumstances, including but not limited to adding night-time construction teams and replacing materials with more cost-effective alternatives.

[0131] Specifically, taking the construction of an underground powerhouse for a pumped storage power station as an example, the project used a BIM model to divide the site into 20 key construction nodes, setting an initial construction period of 180 days and a budget of 230 million yuan. Vibration sensors were deployed to monitor surrounding rock deformation, and RFID was used to track the arrival time of concrete transport vehicles. On the 30th day, a geological fault was detected, causing a 15% decrease in excavation efficiency. This automatically triggered the following adjustments to the construction plan: 1) Increase the number of night shifts for the TBM (increasing costs by 800,000 yuan and shortening the construction period by 12 days); 2) Optimize the concrete mix ratio to reduce the curing period (saving 500,000 yuan in costs and shortening the construction period by 5 days). The final actual construction period was 172 days, with a total cost of 228 million yuan, and the overall optimization rate improved by 8.7%.

[0132] This embodiment of the disclosure acquires relevant data from historical pumped storage projects to construct a construction progress and construction cost correlation model. The model is then trained and tested using this historical data until convergence, resulting in a pre-trained construction progress and construction cost correlation model. Further, a building information model (BIM) for the current pumped storage project is constructed. Based on this BIM model, the construction tasks of the current pumped storage project are decomposed into key nodes, and the resource requirements, schedule constraints, and cost parameters of each key node are associated with them. Real-time data collection is also performed on the construction progress, resource consumption, and environmental parameters of the current pumped storage project. Then, the resource requirements, schedule constraints, cost parameters, construction progress, resource consumption, and environmental parameters of each key node are input into the pre-trained construction progress and construction cost correlation model. This model then correlates the construction progress and construction cost of the current pumped storage project. Next, the system monitors whether the cost overrun rate and schedule deviation rate exceed their respective preset thresholds. These preset thresholds include a preset overrun rate threshold and a preset deviation rate threshold. If the cost overrun rate exceeds the preset overrun rate threshold, or the schedule deviation rate exceeds the preset deviation rate threshold, it is determined that there is a deviation in the construction progress and cost of the current pumped storage project. If a deviation exists, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm. This method optimizes the current construction plan based on a multi-objective genetic algorithm when a correlation exists between the construction progress and cost of a pumped storage project. This addresses the problem that adjustments relying on static plans and manual experience are difficult to adapt to the impact of dynamic factors such as complex geological conditions and equipment supply fluctuations on the schedule and cost. It enhances the targeting and flexibility of the system, better adapting to the characteristics of pumped storage projects.

[0133] Figure 4 This is a schematic diagram of the structure of the construction progress and construction cost collaborative management system provided in this embodiment. The construction progress and construction cost collaborative management system can be the electronic device described in the above embodiment, or it can be a component or assembly within the electronic device. The construction progress and construction cost collaborative management system provided in this embodiment can execute the processing flow provided in the embodiments of the construction progress and construction cost collaborative management method, such as... Figure 4As shown, the construction progress and construction cost collaborative management system 40 includes: a first construction unit 41, a data acquisition unit 42, an association unit 43, and a management unit 44; wherein, the first construction unit 41 is used to construct the building information model of the current pumped storage project, decompose the construction tasks of the current pumped storage project into various key nodes based on the building information model, and associate the resource demand data, schedule constraints, and cost parameters of each key node; the data acquisition unit 42 is used to collect the construction progress data, resource consumption data, and environmental parameters of the current pumped storage project in real time; the association unit 43... Unit 43 is used to input the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the construction progress data of the current pumped storage project, the resource consumption data, and the environmental parameters into the pre-trained construction progress and construction cost association model. The pre-trained construction progress and construction cost association model is used to associate the construction progress and construction cost of the current pumped storage project. The management unit 44 is used to determine whether there is a deviation between the construction progress and construction cost of the current pumped storage project. If there is a deviation, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

[0134] Optionally, the construction progress and construction cost collaborative management system 40 further includes: an acquisition unit 45 and a second construction unit 46; the acquisition unit 45 is used to acquire relevant data of historical pumped storage projects; the second construction unit 46 is used to construct a construction progress and construction cost correlation model, and to train and test the construction progress and construction cost correlation model through relevant data of historical pumped storage projects until convergence, thereby obtaining a pre-trained construction progress and construction cost correlation model; wherein, the relevant data of historical pumped storage projects includes each key node corresponding to the construction task of historical pumped storage projects, the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the construction progress data, resource consumption data, and environmental parameters of historical pumped storage projects.

[0135] Optionally, the construction progress and construction cost correlation model uses cost overrun rate and schedule deviation rate as dual objectives for optimization, and environmental parameters as risk factors as constraints.

[0136] The correlation model between construction progress and construction cost is expressed as follows:

[0137] min[α*(T_actual-T_plan) / T_plan+β*(C_actual-C_plan) / C_plan]

[0138] Where α and β represent weighting coefficients; (T_actual-T_plan) / T_plan represents the deviation ratio of the real-time collected construction progress data compared to the associated schedule constraints; (C_actual-C_plan) / C_plan represents the deviation ratio of the real-time collected resource consumption data compared to the associated resource demand data and cost parameters.

[0139] The constraints are: R_risk ≤ R_max, T_i ≤ T_deadline

[0140] Among them, R_risk≤R_max is a risk constraint, which means that the risk value R_risk faced by the pumped storage project cannot exceed the pre-set maximum risk threshold R_max; T_i≤T_deadline is a time constraint, which means that the task time T_i of each key node must be controlled within the final deadline T_deadline.

[0141] Optionally, when the management unit 44 determines whether there is a deviation in the construction progress and construction cost of the current pumped storage project, it is specifically used to: monitor whether the cost overrun rate and the schedule deviation rate are greater than their respective preset thresholds, the preset thresholds including a preset overrun rate threshold and a preset deviation rate threshold; if the cost overrun rate is greater than the preset overrun rate threshold, or the schedule deviation rate is greater than the preset deviation rate threshold, then it is determined that there is a deviation in the construction progress and construction cost of the current pumped storage project.

[0142] Optionally, when the management unit 44 optimizes the current construction plan based on the multi-objective genetic algorithm, it is specifically used to: set two optimization objectives: minimizing construction progress and minimizing construction cost; encode the preset data of construction progress and the preset data of construction cost as decision variables to obtain an encoding scheme; randomly generate multiple construction plans based on the encoding scheme, each construction plan including a construction progress scheme and a construction cost scheme; calculate the fitness function value of each construction plan, and filter according to the fitness function value of each construction plan to obtain multiple candidate construction plans; determine the target construction plan based on the multiple candidate construction plans; and adjust the current construction plan based on the target construction plan.

[0143] Optionally, when the management unit 44 filters according to the fitness function value of each construction scheme to obtain multiple candidate construction schemes, it is specifically used to: filter out construction schemes with fitness function values ​​lower than a preset threshold in each construction scheme to obtain multiple candidate construction schemes.

[0144] Optionally, when the management unit 44 determines the target construction scheme based on multiple candidate construction schemes, it specifically performs the following steps: selecting, crossing, and mutating multiple candidate construction schemes to generate multiple alternative construction schemes; dividing the multiple alternative construction schemes into various non-dominated levels, each non-dominated level including at least one alternative construction scheme; calculating the congestion degree of at least one alternative construction scheme in each non-dominated level, filtering out alternative construction schemes with congestion degrees lower than a preset congestion degree threshold, and obtaining filtered alternative construction schemes; determining the filtered alternative construction schemes as multiple candidate construction schemes, repeating the steps in this method until a preset termination condition is reached, and then determining the target construction scheme from the current filtered alternative construction schemes.

[0145] Figure 4 The construction progress and construction cost collaborative management system of the embodiment shown can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0146] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. See below for details. Figure 5 It shows a schematic diagram of a structure suitable for implementing the electronic device 600 in the embodiments of this disclosure. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0147] like Figure 5 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603 to implement the construction progress and construction cost collaborative management method as described in the embodiments of this disclosure. The RAM 603 also stores various programs and data required for the operation of the electronic device 600. The processing device 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0148] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0149] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts, thereby implementing the construction progress and construction cost collaborative management method as described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of embodiments of this disclosure.

[0150] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0151] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0152] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0153] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to:

[0154] Construct a building information model for the current pumped storage project, and decompose the construction tasks of the current pumped storage project into key nodes based on the building information model, and associate the resource demand data, schedule constraints and cost parameters of each key node.

[0155] Real-time collection of construction progress data, resource consumption data, and environmental parameters of the current pumped storage project;

[0156] The resource demand data, schedule constraints, cost parameters, current pumped storage project construction progress data, resource consumption data, and environmental parameters of each key node are input into a pre-trained construction progress and construction cost correlation model. The construction progress and construction cost of the current pumped storage project are then correlated through the pre-trained construction progress and construction cost correlation model.

[0157] If there are any discrepancies between the current construction progress and construction cost of the pumped storage project, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

[0158] Optionally, when one or more of the above-described procedures are executed by the electronic device, the electronic device may also perform other steps described in the above embodiments.

[0159] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0161] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0162] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0163] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0164] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0165] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0166] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for collaborative management of construction progress and construction cost, characterized in that, The method includes: Construct a building information model for the current pumped storage project, and decompose the construction tasks of the current pumped storage project into key nodes based on the building information model, and associate the resource demand data, schedule constraints and cost parameters of each key node. Real-time collection of construction progress data, resource consumption data, and environmental parameters of the current pumped storage project; The resource demand data, schedule constraints, cost parameters, current pumped storage project construction progress data, resource consumption data, and environmental parameters of each key node are input into a pre-trained construction progress and construction cost correlation model. The construction progress and construction cost of the current pumped storage project are then correlated through the pre-trained construction progress and construction cost correlation model. If there are any discrepancies between the current construction progress and construction cost of the pumped storage project, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

2. The method according to claim 1, characterized in that, The method further includes: Obtain relevant data on historical pumped storage projects; A construction progress and construction cost correlation model is constructed. The model is trained and tested using relevant data from historical pumped storage projects until convergence, resulting in a pre-trained construction progress and construction cost correlation model. The relevant data of the historical pumped storage projects include the key nodes corresponding to the construction tasks of the historical pumped storage projects, the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the construction progress data, resource consumption data, and environmental parameters of the historical pumped storage projects.

3. The method according to claim 2, characterized in that, The construction progress and construction cost correlation model uses cost overrun rate and schedule deviation rate as dual objectives for optimization, and environmental parameters as risk factors and constraints. The correlation model between construction progress and construction cost is expressed as follows: min[α*(T_actual-T_plan) / T_plan+β*(C_actual-C_plan) / C_plan] Where α and β represent weighting coefficients; (T_actual-T_plan) / T_plan represents the deviation ratio of the real-time collected construction progress data compared to the associated schedule constraints; (C_actual-C_plan) / C_plan represents the deviation ratio of the real-time collected resource consumption data compared to the associated resource demand data and cost parameters. The constraints are: R_risk ≤ R_max, T_i ≤ T_deadline Among them, R_risk≤R_max is a risk constraint, which means that the risk value R_risk faced by the pumped storage project cannot exceed the pre-set maximum risk threshold R_max; T_i≤T_deadline is a time constraint, which means that the task time T_i of each key node must be controlled within the final deadline T_deadline.

4. The method according to claim 1, characterized in that, The determination of whether there are deviations in the current construction progress and construction cost of the pumped storage project includes: Monitor whether the cost overrun rate and schedule deviation rate are greater than their respective preset thresholds, the preset thresholds including preset overrun rate threshold and preset deviation rate threshold; If the cost overrun rate is greater than the preset overrun rate threshold, or the schedule deviation rate is greater than the preset deviation rate threshold, then it is determined that there is a deviation in the construction progress and construction cost of the current pumped storage project.

5. The method according to claim 1, characterized in that, The optimization of the current construction plan based on a multi-objective genetic algorithm includes: Two optimization objectives are set: minimizing construction time and minimizing construction cost. The pre-set data for construction schedule and construction cost are used as decision variables and coded to obtain a coding scheme. Based on the coding scheme, multiple construction plans are randomly generated, each of which includes a construction schedule plan and a construction cost plan. Calculate the fitness function value for each construction scheme, and then filter based on the fitness function value of each construction scheme to obtain multiple candidate construction schemes; Based on multiple candidate construction schemes, the target construction scheme was determined; The current construction plan is adjusted based on the target construction plan.

6. The method according to claim 5, characterized in that, The process of filtering based on the fitness function value of each construction scheme yields multiple candidate construction schemes, including: Construction schemes with fitness function values ​​below a preset threshold are filtered out to obtain multiple candidate construction schemes.

7. The method according to claim 5, characterized in that, The process of determining the target construction scheme based on multiple candidate construction schemes includes: Multiple candidate construction schemes are selected, cross-referenced, and mutated to generate a variety of alternative construction schemes. The various alternative construction schemes are divided into various non-dominated levels, and each non-dominated level includes at least one alternative construction scheme. Calculate the congestion degree of at least one candidate construction scheme at each non-dominated level, filter out candidate construction schemes with congestion degree lower than a preset congestion degree threshold, and obtain the filtered candidate construction schemes. The filtered candidate construction schemes are identified as multiple candidate construction schemes. The steps in this method are repeated until the preset termination condition is met, and then the target construction scheme is determined from the current filtered candidate construction schemes.

8. A collaborative management system for construction progress and construction cost, characterized in that, include: The first construction unit is used to construct the building information model of the current pumped storage project. Based on the building information model, the construction tasks of the current pumped storage project are decomposed into key nodes, and the resource demand data, schedule constraints, and cost parameters of each key node are associated. The data acquisition unit is used to collect real-time data on the construction progress, resource consumption, and environmental parameters of the current pumped storage project. The association unit is used to input the resource demand data of each key node, the schedule constraints of each key node, the cost parameters of each key node, the construction progress data of the current pumped storage project, the resource consumption data, and environmental parameters into the pre-trained construction progress and construction cost association model. The pre-trained construction progress and construction cost association model is used to associate the construction progress and construction cost of the current pumped storage project. The management unit is used to determine whether there are deviations in the construction progress and construction cost of the current pumped storage project. If there are deviations, an early warning mechanism is triggered, and the current construction plan is optimized based on a multi-objective genetic algorithm.

9. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-7.

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