Construction process optimization method, equipment, medium and product

Abstract

The invention provides a construction procedure optimization method, equipment, a medium and a product, and relates to the technical field of building construction. The method comprises the steps that construction process information is acquired, a preset neural network is constructed according to the construction process information, the preset neural network comprises a hopfield neural network, and the construction process information comprises a construction process network plan tree; establishing an optimization function according to the cost, the construction period and a preset algorithm, wherein the preset algorithm comprises a particle swarm optimization algorithm; and optimizing procedure matrix parameters corresponding to the preset neural network by using the optimization function, and determining a construction procedure according to the optimized procedure matrix parameters, the procedure matrix parameters including the number of components and the time sequence number. According to the embodiment of the invention, after the construction process information is obtained, the construction process related to the construction process information is optimized according to the cost and the construction period, the influence of personal experience is reduced, the reliability is high, the construction cost and the construction period can be effectively reduced, and the overall economic benefit of a construction project is improved.

Description

Technical Field

[0001] The present application relates to the field of building construction technology. Specifically, the present application relates to a construction process optimization method, equipment, medium and product. Background Art

[0002] In construction projects, the determination of construction procedures is an important part of construction plan planning, which is very important for construction-related cost control and construction period setting. In the prior art, the construction procedures are often determined based on the experience of the designers themselves. After the construction personnel obtain the formulated construction procedures, they determine the construction plan and carry out construction based on the construction procedures. The quality of the construction procedures obtained in this way is heavily dependent on the personal experience of the designers, has poor reliability, cannot effectively reduce costs and shorten construction periods, and is difficult to ensure the overall economic benefits of the construction project. Summary of the invention

[0003] The embodiments of the present application provide a construction process optimization method, device, medium and product, which can solve the problem that the quality of existing construction processes is heavily dependent on personal experience, has poor reliability, and cannot effectively reduce costs and shorten construction periods. In order to achieve this purpose, the embodiments of the present application provide the following solutions.

[0004] According to one aspect of an embodiment of the present application, a construction process optimization method is provided, the method comprising: Acquire construction process information, and construct a preset neural network according to the construction process information, wherein the preset neural network includes a Hopfield neural network, and the construction process information includes a construction process network plan tree; Establishing an optimization function according to cost, construction period and a preset algorithm, wherein the preset algorithm includes a particle swarm optimization algorithm; The optimization function is used to optimize the process matrix parameters corresponding to the preset neural network, and the construction process is determined according to the optimized process matrix parameters, wherein the process matrix parameters include the number of components and the time sequence number.

[0005] In a possible implementation, constructing a preset neural network according to the construction process information includes: Obtain a sub-process sequence of the construction process network plan tree, and construct a Hopfield neural network for the sub-process sequence.

[0006] In a possible implementation, establishing an optimization function according to cost, construction period and a preset algorithm includes: Obtaining the total cost and total construction period corresponding to the construction process network plan tree, and establishing a global particle swarm optimization function based on the weights corresponding to the total cost and total construction period; The global particle swarm optimization function is: ,in, and is the weight, Q is the total cost and F is the total duration.

[0007] In a possible implementation, the construction process network plan tree is formed based on the construction process of the high-pile wharf, and the construction process includes pile-driving ship construction. The calculation formula corresponding to the total construction period of the pile-driving ship construction is: , where n is the number of sub-processes, i represents the i-th sub-process, is the duration function of the ith sub-process, E is the environmental parameter, D is the pile position parameter, and Z is the ship type coefficient.

[0008] In a possible implementation, the total cost is calculated as follows:

[0009] in, is the cost of the ith device, is the engineering quantity function of the first equipment, middle is the construction period parameter related to the i-th equipment, is the environmental parameter related to the i-th device; is the cost of the kth person, is the engineering quantity function of the kth person; is the cost of the qth material, The cost function of Add costs to the process. is the process additional quantity function, is the number of processes, is the number of devices, is the number of personnel and engineering work, is the number of material types.

[0010] In a possible implementation, the optimizing the process matrix parameters corresponding to the preset neural network by using the optimization function includes: Establish a particle swarm optimization vector according to the process matrix corresponding to the Hopfield neural network, and obtain an energy function corresponding to the particle swarm optimization vector; Calculating local minimum points using the Hopfield neural network, wherein the local minimum points include construction procedures of similar components; The process matrix parameters are optimized according to the energy function, the local minimum point and the global particle swarm optimization function.

[0011] In a possible implementation, the construction process of the same type of components is a pile-driving ship pile sinking process, and the energy function is: , where x and y represent the pile numbers, j is the pile number, and N is the number of piles. and are the parameters in the process matrix V corresponding to the pile-driving process, A and D are preset values, is the collision function of the pile driver between pile x and pile y.

[0012] According to one aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0013] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described above are implemented.

[0014] According to one aspect of an embodiment of the present application, a computer program product is provided, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0015] The beneficial effects of the technical solution provided by the embodiment of the present application are: The construction process optimization method provided in the present application obtains construction process information, builds a preset neural network according to the construction process information; establishes an optimization function according to cost, construction period and preset algorithm; uses the optimization function to optimize the process matrix parameters corresponding to the preset neural network, and determines the construction process according to the optimized process matrix parameters. After obtaining the construction process information, the embodiment of the present application can optimize the construction process related to the construction process information according to the cost and construction period, reduce the influence of personal experience, have high reliability, and can effectively reduce construction costs and shorten construction periods, thereby improving the overall economic benefits of construction projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following is a brief introduction to the drawings required for use in describing the embodiments of the present application.

[0017] Figure 1 A flowchart of a construction process optimization method provided in an embodiment of the present application; Figure 2 A schematic diagram of a process matrix in a construction process optimization method provided in an embodiment of the present application; Figure 3 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0019] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present application refer to that the corresponding features can be implemented as the presented features, information, data, steps, operations, elements and / or components, but do not exclude the implementation of other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the present technical field. It should be understood that when we say that an element is "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or it may refer to that the one element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein indicates at least one of the items defined by the term, for example, "A and / or B" indicates that it is implemented as "A", or is implemented as "A", or is implemented as "A and B".

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0021] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present application and the technical effects produced by the technical solutions of the present application. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0022] The construction process optimization method, equipment, medium and product provided in this application are intended to solve at least one technical problem existing in the prior art.

[0023] A construction process optimization method is provided in an embodiment of the present application. The method can be used in laptop computers, servers, cloud computing, and other smart terminals that can obtain construction process information and optimize construction processes related to the construction process information based on cost and construction period information.

[0024] Alternatively, if Figure 1 , Figure 2 As shown, the construction process optimization method of this application includes: S101: Acquire construction process information, and construct a preset neural network according to the construction process information.

[0025] Optionally, the preset neural network includes a Hopfield neural network, and the construction process information includes a construction process network plan tree. The construction process information may include other information that can describe the execution order, execution time, execution duration and other parameters of each construction process during the construction process.

[0026] Optionally, the construction process network plan tree may be a tree structure, and a node in the tree structure represents a construction process, wherein the construction process may include multiple sub-construction processes, and the sub-construction processes may be represented as sub-nodes in the tree structure.

[0027] Optionally, the construction process network plan tree can be input by designers, or obtained based on construction period environmental data forecasts, construction knowledge graphs, and construction processes of construction equipment.

[0028] In one embodiment, a high-pile wharf is constructed, and the construction period and the processes to be executed related to the high-pile wharf construction are obtained. According to the construction period, the construction period environmental data forecast (such as weather data, environmental status, etc.) is obtained. According to the construction period environmental data forecast, the constructability of each process is judged, so as to plan the operation time point of the process. The construction knowledge graph of high-pile construction includes a pile foundation construction knowledge graph, a beam construction knowledge graph, and a panel construction knowledge graph. According to these knowledge graphs, the construction sequence of components (such as beams, longitudinal beams, panels, pile foundations, slopes, revetments, ancillary facilities, etc.) in the construction of high-pile wharf is determined. After obtaining the operation time point of the process and the sequence of component construction, the operation time point, the sequence and the construction process of the construction equipment are combined to obtain a construction process network plan tree. Among them, the construction process of the construction equipment can be an engineering ship construction process, and the engineering ship construction process includes a pile driving ship construction process, a pile driving platform construction process, a crane ship construction process and other processes. The type of construction equipment and the construction process of the construction equipment can be adjusted and set according to parameters such as the type of construction, construction requirements, and construction environment.

[0029] Optionally, a preset neural network is constructed according to the construction process information, including: obtaining a sub-process sequence of the construction process network plan tree, and constructing a Hopfield neural network for the sub-process sequence.

[0030] Optionally, the construction process network plan tree includes a process matrix of sub-process nodes corresponding to the sub-process sequence. After obtaining the sub-process sequence, the process matrix corresponding to the sub-process sequence is obtained, and a Hopfield neural network is constructed according to the data in the process matrix.

[0031] In one embodiment, the sub-construction process is a pile foundation construction process, and the related process matrix (such as the pile foundation construction process matrix) can be as follows: Figure 2As shown, the process matrix parameters of the process matrix are expressed in the form of (number of components, time sequence number).

[0032] S102: Establishing an optimization function according to cost, construction period and preset algorithm.

[0033] Optionally, the preset algorithm includes a particle swarm optimization algorithm. An optimization function is established according to the cost, construction period and the preset algorithm, including: obtaining the total cost and total construction period corresponding to the construction process network plan tree, and establishing a global particle swarm optimization function based on the weights corresponding to the total cost and the total construction period; the global particle swarm optimization function is: ,in, and is the weight (preset), Q is the total cost and F is the total duration. and The value can be preset or determined according to the type of construction, construction environment, cost requirements, construction period requirements and other requirements.

[0034] Optionally, the construction process network plan tree is formed based on the construction process of the high-pile wharf, and the construction process includes pile-driving ship construction. The calculation formula corresponding to the total construction period of the pile-driving ship construction is: , where n is the number of sub-processes, i represents the i-th sub-process, is the duration function of the ith sub-process, E is the environmental parameter, D is the pile position parameter, and Z is the ship type coefficient. The duration of each sub-process is calculated based on the environmental parameter, pile position parameter, and crawler crane influencing parameter. The total duration of each construction process is added together to obtain the total duration of the high-pile wharf construction.

[0035] In one embodiment, the construction process of the high-pile dock includes the key construction processes of the piling platform, the piling ship, and the crane ship, and the total construction period can be calculated according to the influencing factors in the construction period calculation table of these key processes. Among them, the construction period calculation table of the piling ship construction is shown in Table 1, the construction period calculation table of the piling platform construction is shown in Table 2, and the construction period calculation table of the crane ship construction is shown in Table 3. The table includes the basic quantity and influencing factors of the construction period of the sub-process corresponding to each process, and the construction period of each sub-process is calculated according to the influencing factors.

[0036] Table 1. Calculation table of construction period of pile driving ship

[0037] Table 2. Calculation table of construction period of piling platform

[0038] Table 3. Calculation table of construction period of crane ship

[0039] Alternatively, when constructing a high-pile wharf, the total cost can be calculated as follows: in, is the cost of the ith device, is the engineering quantity function of the first equipment, which represents the total cost of the first equipment. is the construction period parameter related to the i-th equipment, is the environmental parameter related to the i-th device; is the cost of the kth person, is the engineering quantity function of the kth person; is the cost of the qth material, The cost function of Add costs to the process. is the process additional quantity function, is the number of processes, is the number of devices, is the number of personnel and engineering work, is the number of material types. , , T represents the construction period, and E represents the environment. These functions can be determined based on the actual construction period and environment.

[0040] In one embodiment, the equipment used in the construction includes ship machinery equipment, and the cost quota summary table of the ship machinery equipment is shown in Table 4, and the cost calculation of the ship machinery equipment is performed based on Table 4.

[0041] Table 4. Summary of Ship Machinery and Equipment Cost Quotas

[0042] S103: Optimizing the process matrix parameters corresponding to the process sequence in the preset neural network using an optimization function, and determining the construction process according to the optimized process matrix parameters.

[0043] Optionally, the process matrix parameters include the number of components and the time sequence number. The process matrix parameters corresponding to the process sequence in the preset neural network are optimized using an optimization function, including: establishing a particle swarm optimization vector according to the process matrix corresponding to the Hopfield neural network, and obtaining an energy function corresponding to the particle swarm optimization vector; using the Hopfield neural network to calculate local minimum points, the local minimum points include the construction process of the same type of components; optimizing the process matrix parameters according to the energy function, the local minimum points and the global particle swarm optimization function, the process matrix parameters include the number of components and the time sequence number.

[0044] Optionally, the number of components and the time sequence number in the process matrix are used to form an array [number of components, time sequence number], and multiple arrays corresponding to the sub-process sequence are combined (which can be combined in the order of the sequence) to form a particle swarm optimization vector.

[0045] Optionally, the construction process network plan tree is a tree structure, and the local minimum points are calculated starting from the root node of the tree structure until the local minimum points corresponding to each leaf node are calculated.

[0046] Optionally, the energy function is obtained according to the data of the process matrix corresponding to the Hopfield neural network. Wherein, the construction type is high-pile dock construction, and the sub-process corresponding to the Hopfield neural network is pile-driving ship construction, and the energy function is: , where x and y represent the pile numbers, j is the pile number, and N is the number of piles. and is the parameter in the process matrix V corresponding to the pile sinking process of the pile driving ship, Where x represents the row of the process matrix, j represents the column of the process matrix, A and D are preset values, is the collision function of the pile driver between pile x and pile y.

[0047] Optionally, , which is a function of cost, efficiency and time. is the distance between components x and y to be constructed continuously, is the failure probability of the jth control indicator, which is less than the preset value , AND means that all variables must meet the requirements, is the control value of the jth control index, which is less than the preset value , AND means that all variables must meet the requirements. Among them, the control indicators include the control indicators of the pile hanging process (Table 5), the control indicators of the pile sinking process (Table 6) and the control indicators of the component lifting and installation process (Table 7).

[0048] Table 5

[0049] Table 6

[0050] Table 7

[0051] Among them, the effective hammering energy of the pile body is used to measure whether the working performance of the pile hammer system is normal. During the test, the hammering efficiency =Effective hammer energy / rated energy (energy corresponding to the hammer gear position), the consistency and stability of hammer efficiency are used to evaluate the normal performance of the pile hammer. The change of pile bearing capacity with penetration or burial depth can be determined by recording the penetration and measured bearing capacity corresponding to the burial depth, and the pile bearing capacity can be compared with the design bearing capacity to determine whether it meets the design requirements.

[0052] The influence of pile driving is evaluated through the apparent phenomena or deformation and displacement measurement results of the monitoring points. Monitoring of the tensile and compressive stresses of the pile body caused by hammering is a necessary means to ensure the quality of the foundation pile body and improve the efficiency of pile driving. It is an especially important part of the dynamic measurement of the entire pile driving process. Excessive compressive stress caused by hammering can easily cause the pile body to break, while too small compressive stress can lead to low pile driving efficiency. For concrete piles, excessive tensile stress caused by hammering can easily cause circumferential cracking of the pile body.

[0053] Regarding the control of pile body stress during pile sinking, the American ASTM standard: 5.1 of D4945-08 requires that the pile body stress cannot exceed the yield stress value of the pile body material multiplied by a reduction factor. For steel, the reduction factor is 0.9, and for concrete, the reduction factor is 0.85. my country's "Design and Construction Code for Prestressed Concrete Large Diameter Pipe Piles for Port Engineering" 3.4.1 and "Port Engineering Pile Foundation Code" JTJ254-98 5.2.4.1 only stipulate the pile sinking tensile and compressive stress standards for ordinary prestressed concrete piles, reinforced concrete piles, and post-tensioned prestressed concrete large diameter pipe piles, but have not mentioned the currently widely used PHC pre-tensioned prestressed concrete large diameter pipe piles. At the same time, a large number of engineering practices have shown that the maximum tensile and compressive stress of concrete piles under dynamic hammering often exceeds the specified limit value without breaking, indicating that the specified value is on the safe side.

[0054] Based on the above considerations, the formula for the tensile stress limit value during the hammering process of prestressed concrete piles including PHC (prestressed high-strength concrete) piles and prestressed concrete hollow square piles is as follows: .

[0055] In the formula, is the tensile stress limit of hammer pile driving (MPa); is the concrete prestressing partial coefficient, which can be taken as 1.0; is the effective precompression stress value of concrete (MPa); is the tensile strength enhancement factor of dynamic concrete strength, which can be taken as 1.3; is the design value of concrete axial tensile strength (MPa).

[0056] According to the tensile stress limit value calculation formula, the tensile stress limit value of the concrete pile can be obtained as shown in Table 8.

[0057] Table 8

[0058] Under the dynamic stress of pile driving, the material yield strength of steel will be greater than its static yield strength. The stress calculation formula of steel pipe pile based on dynamic effect is:

[0059] In the formula, is the standard value of hammer pile stress (MPa); The strength (static) yield stress of steel (MPa). The stress level of the pile body is an important factor related to the quality of the project, especially for concrete piles. If the compressive stress value exceeds the upper limit of concrete, it is easy to cause the pile head to explode and the pile body to crack longitudinally. Excessive compressive stress values ​​will cause vertical cracks in the pile body and easily cause broken piles. When steel pipe piles encounter hard rock formations, the penetration rate will be very small, and the energy and stress of the pile body will increase rapidly, which is easy to cause curling. Controlling the stress value of the pile body during the pile sinking process is the key.

[0060] The maximum tensile stress tends to occur in the initial stage of pile sinking when the upper silt layer is relatively soft and the pile tip passes through a hard layer such as a sand layer while the lower soil layer is a softer clay layer. When the pile tip encounters a softer soil layer, the hammer jump height should be adjusted to reduce the energy of the pile body and control the tensile stress of the pile body. The compressive stress is closely related to the energy and penetration of the pile body. When the rock layer is harder, the penetration is smaller, the pile body energy is larger, and the compressive stress value of the pile body will be larger. The gear position of the pile hammer should be controlled to reduce the input of pile body energy and improve the quality of the project. In one embodiment, the process corresponding to the local minimum point is pile sinking construction by a pile-driving ship, and the calculation process of the local minimum point is as follows: By differentiating the energy function, the change in the neural network is calculated. The relevant calculation formula is as follows: .in, is the number of piles j constructed, represents the amount of change in the neural network, represents a local minimum point.

[0061] Based on the differentiation results, the values ​​of the process matrix are updated using the following formula: t; ; In the formula, represents the time t+1 , It represents the parameters of the process matrix at time t+1. The current construction working surface (i.e., the working area corresponding to the currently constructed pile foundation) is determined based on the parameters, and whether the current construction working surface conflicts with the constructed pile foundation is detected. If it is determined that there is no conflict, it is determined that the calculated local minimum point meets the requirements, and the construction process corresponding to the process matrix at time t+1 is determined as the construction process of the pile-driving ship; if it is determined that there is a conflict, the local minimum point can be recalculated (such as calculating the parameters of the process matrix at time t+2) until the calculated local minimum point meets the requirements, and the pile-sinking construction process of the pile-driving ship is obtained.

[0062] Optionally, The initialization value of can be calculated by the following formula.

[0063] , ; N is the number of piles, for A random value in the interval.

[0064] Optionally, after obtaining the local minimum point corresponding to each node in the construction process network plan tree (i.e., obtaining the construction process of different types of components), the local minimum point is used as the initialization position of the global particle swarm optimization function, and the global particle swarm optimization function is used to continue searching for the process matrix parameters corresponding to the smaller energy function, and the process matrix is ​​updated based on the process matrix parameters to obtain the optimal sub-process sequence, and the construction process network plan tree is updated using the optimal sub-process sequence, and the process sequence corresponding to the updated construction process network plan tree is used as the construction process required for construction.

[0065] In one embodiment, when searching for a smaller energy function through a global particle swarm optimization function, the local minimum point is used as a particle of the global particle swarm optimization function, all local minimum points are initialized, that is, their speed and position are assigned, and the individual's historical optimal position (pBest) is set as the current position, and the optimal individual in the group is used as the optimal position (gBest) experienced by the current particle swarm. In each generation of evolution, the fitness function value of each particle is calculated. If the current fitness function value is better than the historical optimal value, pBest is updated. If the current fitness function value is better than the global historical optimal value, gBest is updated. The speed and position of the dth dimension of each particle i are updated according to the following formulas.

[0066] ; ; In the formula, The velocity of particle i in the dth dimension (which can be regarded as the current step), The velocity of particle i in the d-1th dimension (which can be regarded as the previous step), is the inertia weight, For the self-growth part, is the global growth part, and is the acceleration factor, and are two random numbers in the interval [0,1], is the optimal position of particle i in the dth dimension, is the optimal position of the particle swarm in the dth dimension, is the position of particle i in the dth dimension.

[0067] The above formula is continuously iterated to detect whether the number of iterations reaches the preset number of iterations. If so, it is determined that the iteration conditions are met and the optimal solution is output. If not, it is continued until the iteration conditions are met. The process sequence is obtained according to the local minimum point corresponding to the optimal position after iteration, and the process sequence is determined as the optimized construction process.

[0068] The construction process optimization method of the present application obtains construction process information, builds a preset neural network according to the construction process information; establishes an optimization function according to cost, construction period and preset algorithm; uses the optimization function to optimize the process matrix parameters corresponding to the preset neural network, and determines the construction process according to the optimized process matrix parameters. After obtaining the construction process information, the embodiment of the present application can optimize the construction process related to the construction process information according to the cost and construction period, reduce the influence of personal experience, have high reliability, and can effectively reduce construction costs and shorten construction periods, thereby improving the overall economic benefits of construction projects.

[0069] In an optional embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present application.

[0070] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0071] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0072] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disc, optical disk, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0073] The memory 4003 is used to store the computer program for executing the embodiment of the present application, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer program stored in the memory 4003 to implement the steps shown in the above method embodiment.

[0074] Among them, the electronic device can be any electronic product that can interact with an object, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0075] The electronic device may also include a network device and / or an object device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud consisting of a large number of hosts or network servers based on cloud computing.

[0076] The network where the electronic device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), etc.

[0077] An embodiment of the present application provides a computer-readable storage medium, which includes a computer program. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0078] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0079] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that shown or described in the drawings.

[0080] It should be understood that, although each operation step is indicated by arrows in the flowchart of the embodiment of the present application, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present application, the implementation steps in each flowchart can be performed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on actual implementation scenarios. Some or all of these sub-steps or stages may be executed at the same time, and each sub-step or stage in these sub-steps or stages may also be executed at different times respectively. In different scenarios of execution time, the execution order of these sub-steps or stages may be flexibly configured according to demand, and the embodiment of the present application does not limit this.

[0081] The above is only an optional implementation method for some implementation scenarios of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the solution of the present application, other similar implementation methods based on the technical ideas of the present application are also within the protection scope of the embodiments of the present application.

Claims

1. A construction process optimization method, characterized in that: The method comprises: Acquire construction process information, and construct a preset neural network according to the construction process information, wherein the preset neural network includes a Hopfield neural network, and the construction process information includes a construction process network plan tree; Establishing an optimization function according to cost, construction period and a preset algorithm, wherein the preset algorithm includes a particle swarm optimization algorithm; The optimization function is used to optimize the process matrix parameters corresponding to the preset neural network, and the construction process is determined according to the optimized process matrix parameters, wherein the process matrix parameters include the number of components and the time sequence number.

2. The construction process optimization method according to claim 1, characterized in that: The method of constructing a preset neural network according to the construction process information includes: Obtain a sub-process sequence of the construction process network plan tree, and construct a Hopfield neural network for the sub-process sequence.

3. The construction process optimization method according to claim 2, characterized in that: The optimization function is established according to the cost, construction period and preset algorithm, including: Obtaining the total cost and total construction period corresponding to the construction process network plan tree, and establishing a global particle swarm optimization function based on the weights corresponding to the total cost and total construction period; The global particle swarm optimization function is: ,in, and is the weight, Q is the total cost and F is the total duration.

4. The construction process optimization method according to claim 3, characterized in that: The construction process network plan tree is formed based on the construction process of the high-pile wharf, and the construction process includes the construction of the pile-driving ship. The calculation formula corresponding to the total construction period of the pile-driving ship construction is: , where n is the number of sub-processes, i represents the i-th sub-process, is the duration function of the ith sub-process, E is the environmental parameter, D is the pile position parameter, and Z is the ship type coefficient.

5. The construction process optimization method according to claim 4, characterized in that: The total cost is calculated as follows: in, is the cost of the ith device, is the engineering quantity function of the first equipment, middle is the construction period parameter related to the i-th equipment, is the environmental parameter related to the i-th device; is the cost of the kth person, is the engineering quantity function of the kth person; is the cost of the qth material, The cost function of Add costs to the process. is the process additional quantity function, is the number of processes, is the number of devices, is the number of personnel and engineering work, is the number of material types.

6. The construction process optimization method according to claim 4, characterized in that: The step of optimizing the process matrix parameters corresponding to the preset neural network by using the optimization function includes: Establish a particle swarm optimization vector according to the process matrix corresponding to the Hopfield neural network, and obtain an energy function corresponding to the particle swarm optimization vector; Calculating local minimum points using the Hopfield neural network, wherein the local minimum points include construction procedures of similar components; The process matrix parameters are optimized according to the energy function, the local minimum point and the global particle swarm optimization function.

7. The construction process optimization method according to claim 6, characterized in that: The construction process of the same type of components is the pile sinking process of a pile driving ship, and the energy function is: , where x and y represent the pile numbers, j is the pile number, and N is the number of piles. and are the parameters in the process matrix V corresponding to the pile-driving process, A and D are preset values, is the collision function of the pile driver between pile x and pile y.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which implements the steps of the method according to any one of claims 1 to 7 when executed by a processor.

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