Flood prevention scheduling method and system based on multi-objective optimization genetic algorithm in combination with rain condition

Abstract

The invention relates to the technical field of water conservancy flood prevention, in particular to a flood prevention scheduling method and system based on a multi-objective optimization genetic algorithm in combination with rain conditions. Constructing an initial scheduling model according to the list of operators on duty and the current water regimen information; generating a plurality of candidate scheduling schemes based on the initial scheduling model and a plurality of scheduling constraint conditions; and performing iterative optimization on the plurality of candidate scheduling schemes through a multi-target optimization model of a genetic algorithm to obtain a target scheduling scheme of a next scheduling period, and during scheduling, constructing an initial scheduling model corresponding to a list of operators on duty and a local current water regimen so as to generate the plurality of candidate scheduling schemes in combination with a scheduling constraint condition, according to the method, more possibilities are provided, the multiple generated candidate scheduling schemes are iteratively optimized through the multi-target optimization model based on the genetic algorithm, the optimal target scheduling scheme is obtained, and the workload of flood prevention scheduling is simplified.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy and flood control, and in particular to a flood control scheduling method and system based on a multi-objective optimization genetic algorithm combined with rainfall conditions. Background Art

[0002] Traditional flood control scheduling decisions based on rainfall mainly rely on manual experience and judgment, which are highly subjective and prone to errors. Especially when faced with complex flood control situations, manual decision-making is often difficult to achieve comprehensive, accurate and timely flood control decisions. Secondly, when faced with sudden flood disasters, it is difficult to quickly take effective response measures. In addition, monitoring data between different departments and systems are often difficult to share and integrate, resulting in a heavy workload for scheduling personnel to balance different parts or systems.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a flood prevention scheduling method and system based on a multi-objective optimization genetic algorithm combined with rainfall conditions, aiming to solve the technical problems in the prior art that scheduling decisions during the flood prevention period mainly rely on manual decision-making, which is highly subjective and has a large workload.

[0005] To achieve the above object, the present invention provides a flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions, the method comprising the following steps: Obtain the list of on-duty personnel and current water situation information of each department; Constructing an initial scheduling model according to the duty personnel list and the current water situation information; generating a plurality of candidate scheduling schemes based on the initial scheduling model and a plurality of scheduling constraints; The multiple candidate shift scheduling schemes are iteratively optimized through a multi-objective optimization model of a genetic algorithm to obtain a target shift scheduling scheme for the next shift scheduling cycle.

[0006] Optionally, the iterative optimization of the multiple candidate scheduling schemes by using a preset multi-objective optimization model to obtain a target scheduling scheme for the next scheduling cycle includes: Calculate the fitness data corresponding to each candidate scheduling scheme; Determine, according to the fitness data, a plurality of shift scheduling schemes to be optimized whose selection probabilities meet preset shift scheduling requirements; The multiple shift scheduling schemes to be optimized are cross-processed to obtain at least one target shift scheduling scheme.

[0007] Optionally, the calculating of the fitness data corresponding to each candidate shift scheduling scheme includes: Extracting data feature values ​​and feature importance data corresponding to each candidate scheduling plan; Constructing a feature importance index matrix and a feature importance index average value according to the feature importance data; Extracting a target feature importance index that is greater than an average value of the feature importance index in the feature importance index matrix; Constructing a fitness function according to the sum of the target feature importance indicators; The fitness data of each candidate scheduling scheme is calculated based on the fitness function.

[0008] Optionally, the determining, according to the fitness data, a plurality of shift scheduling schemes to be optimized whose selection probabilities meet preset shift scheduling requirements comprises: Calculate the corresponding selection probability according to the fitness data of each candidate scheduling plan; Determine the target candidate shift scheduling scheme with the highest probability among the candidate shift scheduling schemes; The target candidate shift scheduling scheme and the target candidate shift scheduling scheme whose selection probability is greater than a preset probability threshold are used as the shift scheduling scheme to be optimized.

[0009] Optionally, cross-processing the multiple shift scheduling schemes to be optimized to obtain at least one target shift scheduling scheme includes: Performing a variation process on the at least one new shift scheduling scheme to obtain a plurality of variation shift scheduling schemes; The multiple variant shift scheduling schemes are used as candidate shift scheduling schemes, and the multiple candidate shift scheduling schemes are iteratively optimized through the multi-objective optimization model of the genetic algorithm to obtain the target shift scheduling scheme for the next shift scheduling cycle.

[0010] Optionally, the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions further includes: Broadcasting the target scheduling plan for the next scheduling cycle and receiving feedback from each on-duty personnel on the target scheduling plan; The target shift scheduling plan is verified or updated based on the feedback information.

[0011] Optionally, before generating at least one candidate scheduling solution based on the initial scheduling model and a plurality of preset scheduling model scheduling constraints, the method further includes: Get the maximum number of working days in the next shift cycle, the number of on-duty employees in each shift, and the rated number of on-duty employees in one shift day; Constructing a first scheduling constraint condition according to the maximum number of working days, so that the scheduling scheme is less than or equal to the constraint condition of the maximum number of working days; Constructing a second shift scheduling constraint condition according to the number of employees on duty, so that the shift scheduling plan meets the constraints of the maximum number of employees on duty and the minimum number of employees on duty; A third shift scheduling constraint condition is constructed according to the rated duty number, so that the shift scheduling plan meets the constraint condition of the rated duty number of each employee on each duty day.

[0012] In addition, to achieve the above purpose, the present invention also proposes a flood prevention scheduling system based on a multi-objective optimization genetic algorithm combined with rainfall conditions, and the flood prevention scheduling system based on a multi-objective optimization genetic algorithm combined with rainfall conditions includes: The acquisition module is used to obtain the list of on-duty personnel in each department and the current water situation information; A construction module, used to construct an initial scheduling model according to the duty personnel list and the current water situation information; A generation module, used to generate multiple candidate scheduling solutions based on the initial scheduling model and multiple scheduling constraints; The optimization module is used to iteratively optimize the multiple candidate shift scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target shift scheduling scheme for the next shift scheduling cycle.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also proposes a flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions, and the flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions includes: a memory, a processor, and a flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions stored in the memory and executable on the processor, and the flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions is configured to implement the steps of the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which is stored a flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions. When the flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions is executed by a processor, the steps of the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions as described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions as described above.

[0016] One or more technical solutions proposed in the present invention have at least the following technical effects: the present invention obtains the duty personnel list and current water conditions information of each department; constructs an initial scheduling model according to the duty personnel list and the current water conditions information; generates multiple candidate scheduling schemes based on the initial scheduling model and multiple scheduling constraints; iteratively optimizes the multiple candidate scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target scheduling scheme for the next scheduling cycle. When scheduling, an initial scheduling model corresponding to the duty personnel list and the local current water conditions is constructed to facilitate the generation of multiple candidate scheduling schemes in combination with the scheduling constraints to provide more possibilities. The multiple generated candidate scheduling schemes are iteratively optimized through a multi-objective optimization model based on a genetic algorithm to obtain the optimal target scheduling scheme, thereby simplifying the workload of flood prevention scheduling and avoiding the technical problem that scheduling decisions during the flood prevention period in the prior art mainly rely on manual decision-making, which is highly subjective and has a large workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 It is a flow chart of the first embodiment of the flood prevention scheduling method based on the multi-objective optimization genetic algorithm combined with rainfall conditions of the present invention; Figure 2 It is a flow chart of a second embodiment of a flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions according to the present invention; Figure 3 It is a structural block diagram of the first embodiment of the flood prevention scheduling system based on the multi-objective optimization genetic algorithm combined with rainfall conditions of the present invention; Figure 4 It is a structural schematic diagram of a flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions in a hardware operating environment involved in an embodiment of the present invention.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] Based on this, the embodiment of the present invention provides a flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions, referring to Figure 1 , Figure 1 The figure is a flow chart of a first embodiment of a flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions according to the present invention.

[0024] In this embodiment, the flood prevention scheduling method based on the multi-objective optimization genetic algorithm combined with rainfall conditions includes: Step S10: Obtain the duty personnel list and current water situation information of each department.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a control computer, etc. The following takes the control computer as an example to illustrate this embodiment and the following embodiments.

[0026] It should be noted that under the background of global climate change, the frequency and intensity of extreme weather events, especially flood disasters, are increasing, posing a severe challenge to the safety of people's lives and property and social and economic development. The traditional flood control scheduling system has many problems in practical application, which limit the efficiency and accuracy of flood control work.

[0027] First, traditional flood prevention scheduling decisions during the flood season mainly rely on manual experience and judgment, which is highly subjective and prone to errors. Especially in the face of complex flood prevention situations, manual decisions are often difficult to make comprehensive, accurate and timely. Secondly, the traditional system lacks intelligent and automated support, and is unable to conduct in-depth mining and analysis of the collected data, let alone automatically adjust the flood prevention scheduling plan based on real-time data. This makes it difficult for the system to quickly respond to sudden flood disasters. In addition, monitoring data between different departments and systems is often difficult to share and integrate, and there is a data island phenomenon. This problem of poor information sharing leads to a lack of sufficient data support for decision-making departments when responding to disasters, making it difficult to form a unified dispatching and command system.

[0028] Based on this, this embodiment obtains the duty personnel list and current water conditions information of each department; constructs an initial scheduling model based on the duty personnel list and the current water conditions information; generates multiple candidate scheduling schemes based on the initial scheduling model and multiple scheduling constraints; iteratively optimizes the multiple candidate scheduling schemes through the multi-objective optimization model of the genetic algorithm to obtain the target scheduling scheme for the next scheduling cycle, which greatly reduces the scheduling planning workload of each department during flood prevention and improves the information linkage of each department.

[0029] Current water conditions information includes but is not limited to the local flood season status, river and lake water conditions, and rainfall forecast data at the current moment, which can be used to formulate subsequent constraints and dynamically adjust duty shifts and duty personnel.

[0030] Step S20: constructing an initial scheduling model according to the duty personnel list and the current water situation information.

[0031] In the specific implementation, the objective function corresponding to the initial scheduling model is:

[0032] Where n represents the total number of on-duty shifts, m represents the total number of shifts, T represents the total number of on-duty days, i = 1, 2, ... n, j = 1, 2, ... m, k = 1, 2, ... T, Indicates the preset parameters. It means that the i-th employee works on the j-th shift on the k-th duty day. It can be set according to the actual situation. represents the time that the i-th employee works on the j-th shift on the k-th duty day, represents the working time of the i-th employee on the k-th duty day, It represents the rest time of the i-th employee on the j-th shift on the k-th duty day.

[0033] The initial scheduling model can be used to construct multiple scheduling schemes, but due to the lack of constraints for each shift, the generated scheduling schemes may not meet actual needs.

[0034] Step S30: generating a plurality of candidate scheduling schemes based on the initial scheduling model and a plurality of scheduling constraints.

[0035] It should be understood that the scheduling constraints can be used to constrain the number of duty days in the next scheduling cycle, which can be associated with the local flood situation and rainfall forecast results. It can also constrain the number of duty shifts on each duty day, as well as the maximum or minimum number of duty persons in each duty shift. This embodiment does not impose specific restrictions on this.

[0036] Furthermore, before generating at least one candidate scheduling scheme based on the initial scheduling model and a plurality of preset scheduling model scheduling constraints, the method further includes: Get the maximum number of working days in the next shift cycle, the number of on-duty employees in each shift, and the rated number of on-duty employees in one shift day; Constructing a first scheduling constraint condition according to the maximum number of working days, so that the scheduling scheme is less than or equal to the constraint condition of the maximum number of working days; Constructing a second shift scheduling constraint condition according to the number of employees on duty, so that the shift scheduling plan meets the constraints of the maximum number of employees on duty and the minimum number of employees on duty; A third shift scheduling constraint condition is constructed according to the rated duty number, so that the shift scheduling plan meets the constraint condition of the rated duty number of each employee on each duty day.

[0037] In the specific implementation, the scheduling model of the first constraint condition is constructed:

[0038] in , which is the maximum number of working days in the next duty cycle.

[0039] Construct a scheduling model for the second constraint:

[0040] Indicates that each shift needs to have Employees work.

[0041] Construct a scheduling model for the third constraint:

[0042] Indicates that each employee can work at most i shifts on each duty day, where The default is 1.

[0043] Step S40: Iteratively optimize the multiple candidate shift scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target shift scheduling scheme for the next shift scheduling cycle.

[0044] The multi-objective optimization model of genetic algorithm is an iterative search process. By evaluating the fitness and selecting the scheduling scheme, the optimal solution that meets the constraints is finally obtained, thereby generating a scheduling scheme that can link different departments and improve flood control emergency capabilities.

[0045] Furthermore, the flood prevention scheduling method based on the multi-objective optimization genetic algorithm combined with rainfall conditions also includes: Broadcasting the target scheduling plan for the next scheduling cycle and receiving feedback from each on-duty personnel on the target scheduling plan; The target shift scheduling plan is verified or updated based on the feedback information.

[0046] During the specific implementation process, since the on-duty personnel of some departments in the specified target scheduling plan need to change shifts or have time conflicts, it is difficult to perform flood prevention duty according to the target scheduling plan. Therefore, in this embodiment, the target scheduling plan will be modified or verified based on the feedback information of each on-duty personnel on the target scheduling plan to promote normal flood prevention duty work.

[0047] This embodiment obtains the duty personnel list and current water conditions information of each department; constructs an initial scheduling model according to the duty personnel list and the current water conditions information; generates multiple candidate scheduling schemes based on the initial scheduling model and multiple scheduling constraints; iteratively optimizes the multiple candidate scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target scheduling scheme for the next scheduling cycle. When scheduling, an initial scheduling model corresponding to the duty personnel list and the local current water conditions is constructed to generate multiple candidate scheduling schemes in combination with the scheduling constraints to provide more possibilities. The generated multiple candidate scheduling schemes are iteratively optimized through a multi-objective optimization model based on a genetic algorithm to obtain the optimal target scheduling scheme, thereby simplifying the workload of flood prevention scheduling and avoiding the technical problem that scheduling decisions during the flood prevention period in the prior art mainly rely on manual decision-making, which is highly subjective and has a large workload.

[0048] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , step S40, comprising: Step S401: Calculate the fitness data corresponding to each candidate scheduling scheme.

[0049] It should be noted that, during the iteration process, fitness data is a key component used in the genetic algorithm to evaluate the ability of individuals to adapt to the environment. It is different from the loss function, cost function, and objective function calculated when the conventional model converges. The loss function, cost function, and objective function are the optimization results that are expected to be obtained. Generally, their maximum or minimum values ​​are calculated, and they will not directly affect the speed of model convergence. In this embodiment, the fitness data is a non-negative value. Since the genetic algorithm does not use external information for convergence during the iteration process, the fitness function directly affects the speed of model convergence and the probability of selection of different schemes in subsequent iterative optimization.

[0050] Furthermore, the calculation of the fitness data corresponding to each candidate shift scheduling scheme includes: Extracting data feature values ​​and feature importance data corresponding to each candidate scheduling plan; Constructing a feature importance index matrix and a feature importance index average value according to the feature importance data; Extracting a target feature importance index that is greater than an average value of the feature importance index in the feature importance index matrix; Constructing a fitness function according to the sum of the target feature importance indicators; The fitness data of each candidate scheduling scheme is calculated based on the fitness function.

[0051] In the specific implementation, in the multi-objective iterative optimization model constructed based on the genetic algorithm, the data eigenvalues ​​and the corresponding feature importance data are mainly used to measure the rationality of the scheduling plan. Since there are many indicators that affect the rationality of the scheduling plan, such as the number of shifts, the number of shifts process will lead to a lot of manpower waste, but at the same time it will also bring better water situation monitoring effects, the personnel ratio of each department, and the personnel ratio of each department will greatly improve the linkage between different departments and improve the ability to resist risks. Therefore, this embodiment needs to extract features for each candidate scheduling plan and perform quantitative calculations.

[0052] In the specific implementation, let A and B be a set of values ​​measuring two different classes of features, then the sum of the variances of features A and B is defined as:

[0053] Where A and B are sets of values ​​measuring features of two different classes, and n1 and n2 are the corresponding numbers of samples.

[0054] The feature importance data is:

[0055] According to the feature importance data, the feature importance indicator matrix F is constructed:

[0056] Among them, C is the number of sample categories, and the selected features are M.

[0057] From the feature importance matrix F, the feature importance index average F can be calculated AVG for:

[0058] Step S402: Determine, based on the fitness data, a plurality of shift scheduling schemes to be optimized whose selection probabilities meet preset shift scheduling requirements.

[0059] The preset scheduling requirement means that the probability of selecting a candidate scheduling solution is greater than a certain threshold, for example, 70%, etc. This embodiment does not impose any specific restrictions on this.

[0060] Furthermore, the step of determining, according to the fitness data, a plurality of shift scheduling schemes to be optimized whose selection probabilities meet preset shift scheduling requirements comprises: Calculate the corresponding selection probability according to the fitness data of each candidate scheduling plan; Determine the target candidate shift scheduling scheme with the highest probability among the candidate shift scheduling schemes; The target candidate shift scheduling scheme and the target candidate shift scheduling scheme whose selection probability is greater than a preset probability threshold are used as the shift scheduling scheme to be optimized.

[0061] It can be understood that the larger the average value of the feature importance index is, the stronger the feature classification ability of the selected feature is. At the same time, the sum of the feature importance indexes above the average value also reflects the importance of the selected feature. In this embodiment, the largest single extraction corresponding to the fitness data can be used as the optimal scheduling plan within the local iteration, so as to retain the duty strategy with the most obvious features in the subsequent cross-processing, thereby improving the reliability of the duty plan.

[0062] Specifically, the relationship between the selection probability and the fitness data is:

[0063] Among them, fi is the fitness data.

[0064] Step S403: cross-processing the multiple shift scheduling schemes to be optimized to obtain at least one target shift scheduling scheme.

[0065] Cross-processing the selected shift scheduling plans to be optimized refers to cross-operating the shifts, on-duty personnel and number of people on the shift scheduling plans to be optimized to generate new shift scheduling plans, thereby generating diversified plans and improving the reliability of the on-duty plans.

[0066] Furthermore, after cross-processing the plurality of shift scheduling schemes to be optimized to obtain at least one target shift scheduling scheme, the method further includes: Performing a variation process on the at least one new shift scheduling scheme to obtain a plurality of variation shift scheduling schemes; The multiple variant shift scheduling schemes are used as candidate shift scheduling schemes, and the multiple candidate shift scheduling schemes are iteratively optimized through the multi-objective optimization model of the genetic algorithm to obtain the target shift scheduling scheme for the next shift scheduling cycle.

[0067] The new solutions are mutated to ensure the diversification of the problems and avoid falling into the local optimal results during the iterative optimization process, so as to obtain the best duty plan in terms of shifts, duty personnel, and number of duty personnel.

[0068] This embodiment calculates the fitness data corresponding to each candidate scheduling scheme; determines multiple scheduling schemes to be optimized whose selection probabilities meet the requirements according to the fitness data; cross-processes the multiple scheduling schemes to be optimized to obtain at least one target scheduling scheme, and cross-processes the scheduling schemes to be optimized according to the fitness data to quantify the selection probability of each candidate scheduling scheme, thereby improving the diversity and reliability of the scheduling schemes.

[0069] This application also provides a flood prevention scheduling system based on a multi-objective optimization genetic algorithm combined with rainfall conditions, please refer to Figure 3 The flood prevention scheduling system based on multi-objective optimization genetic algorithm combined with rainfall conditions includes: The acquisition module 10 is used to obtain the duty personnel list of each department and the current water situation information.

[0070] The construction module 20 is used to construct an initial scheduling model according to the duty personnel list and the current water situation information.

[0071] The generating module 30 is used to generate a plurality of candidate shift scheduling schemes based on the initial shift scheduling model and a plurality of shift scheduling constraints.

[0072] The optimization module 40 is used to iteratively optimize the multiple candidate shift scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target shift scheduling scheme for the next shift scheduling cycle.

[0073] This embodiment obtains the duty personnel list and current water conditions information of each department; constructs an initial scheduling model according to the duty personnel list and the current water conditions information; generates multiple candidate scheduling schemes based on the initial scheduling model and multiple scheduling constraints; iteratively optimizes the multiple candidate scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target scheduling scheme for the next scheduling cycle. When scheduling, an initial scheduling model corresponding to the duty personnel list and the local current water conditions is constructed to generate multiple candidate scheduling schemes in combination with the scheduling constraints to provide more possibilities. The generated multiple candidate scheduling schemes are iteratively optimized through a multi-objective optimization model based on a genetic algorithm to obtain the optimal target scheduling scheme, thereby simplifying the workload of flood prevention scheduling and avoiding the technical problem that scheduling decisions during the flood prevention period in the prior art mainly rely on manual decision-making, which is highly subjective and has a large workload.

[0074] In one embodiment, the optimization module 40 is further used to calculate the fitness data corresponding to each candidate scheduling scheme; determine a plurality of scheduling schemes to be optimized whose selection probability meets the preset scheduling requirements according to the fitness data; and cross-process the plurality of scheduling schemes to be optimized to obtain at least one target scheduling scheme.

[0075] In one embodiment, the optimization module 40 is also used to extract the data characteristic values ​​and corresponding feature importance data corresponding to each candidate scheduling scheme; construct a feature importance index matrix and a feature importance index average value based on the feature importance data; extract the target feature importance index in the feature importance index matrix that is greater than the feature importance index average value; construct a fitness function based on the sum of the target feature importance indicators; and calculate the fitness data of each candidate scheduling scheme based on the fitness function.

[0076] In one embodiment, the optimization module 40 is also used to calculate the corresponding selection probability based on the fitness data of each candidate scheduling scheme; determine the target candidate scheduling scheme with the highest selection probability among the candidate scheduling schemes; and use the target candidate scheduling scheme and the target candidate scheduling scheme with a selection probability greater than a preset probability threshold as the scheduling scheme to be optimized.

[0077] In one embodiment, the optimization module 40 is further used to mutate the at least one new scheduling plan to obtain multiple mutated scheduling plans; use the multiple mutated scheduling plans as candidate scheduling plans, and return to the step of iteratively optimizing the multiple candidate scheduling plans through the multi-objective optimization model of the genetic algorithm to obtain the target scheduling plan for the next scheduling cycle.

[0078] In one embodiment, the flood prevention scheduling system based on a multi-objective optimization genetic algorithm combined with rainfall conditions also includes the update module, which is used to broadcast the target scheduling plan for the next scheduling cycle and receive feedback information from each on-duty personnel on the target scheduling plan; and verify or update the target scheduling plan based on the feedback information.

[0079] In one embodiment, the generation module 30 is further used to obtain the maximum number of working days in the next scheduling cycle, the number of on-duty employees in each shift, and the rated number of on-duty employees for each on-duty employee in one duty day; construct a first scheduling constraint condition based on the maximum number of working days so that the scheduling plan is less than or equal to the constraint condition of the maximum number of working days; construct a second scheduling constraint condition based on the number of on-duty employees so that the scheduling plan meets the constraints of the maximum number of on-duty employees and the minimum number of on-duty employees; construct a third scheduling constraint condition based on the rated number of on-duty employees so that the scheduling plan meets the constraint condition of the rated number of on-duty employees for each employee on each duty day.

[0080] The present application provides a flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions. The flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the flood prevention scheduling method based on the multi-objective optimization genetic algorithm combined with rainfall conditions in the above-mentioned embodiment one.

[0081] Reference below Figure 4 , which shows a schematic diagram of the structure of a flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions suitable for implementing the embodiment of the present application. The flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The flood prevention scheduling equipment based on the multi-objective optimization genetic algorithm combined with rainfall conditions is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0082] like Figure 4As shown, the flood control scheduling device based on the multi-objective optimization genetic algorithm combined with the rainfall condition may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the flood control scheduling device based on the multi-objective optimization genetic algorithm combined with the rainfall condition are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the flood control scheduling device based on the multi-objective optimization genetic algorithm combined with the rainfall conditions to communicate wirelessly or wired with other devices to exchange data. Although the flood control scheduling device based on the multi-objective optimization genetic algorithm combined with the rainfall conditions with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0083] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0084] The flood control scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions provided by the present application adopts the flood control scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions in the above-mentioned embodiment, which can solve the technical problem of flood control scheduling based on a multi-objective optimization genetic algorithm combined with rainfall conditions. Compared with the prior art, the beneficial effects of the flood control scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions provided by the present application are the same as the beneficial effects of the flood control scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions provided by the above-mentioned embodiment, and the other technical features of the flood control scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0085] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0086] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0087] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions in the above-mentioned embodiment.

[0088] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0089] The computer-readable storage medium may be included in the flood control scheduling device based on the multi-objective optimization genetic algorithm combined with rainfall conditions; or it may exist independently without being assembled into the flood control scheduling device based on the multi-objective optimization genetic algorithm combined with rainfall conditions.

[0090] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the flood prevention scheduling device based on the multi-objective optimization genetic algorithm combined with rainfall conditions, the flood prevention scheduling device based on the multi-objective optimization genetic algorithm combined with rainfall conditions is enabled: flood prevention scheduling based on the multi-objective optimization genetic algorithm combined with rainfall conditions.

[0091] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0092] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0094] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned flood control scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions, and can solve the technical problem of flood control scheduling based on a multi-objective optimization genetic algorithm combined with rainfall conditions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the flood control scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions provided by the above-mentioned embodiment, and will not be repeated here.

[0095] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions as described above.

[0096] The computer program product provided by the present application can solve the technical problem of flood control scheduling based on a multi-objective optimization genetic algorithm combined with rainfall conditions. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the flood control scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions provided by the above embodiment, and will not be repeated here.

[0097] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions, characterized in that: The flood control scheduling method based on multi-objective optimization genetic algorithm combined with rainfall conditions includes: Obtain the list of on-duty personnel and current water situation information of each department; Constructing an initial scheduling model according to the duty personnel list and the current water situation information; generating a plurality of candidate scheduling schemes based on the initial scheduling model and a plurality of scheduling constraints; The multiple candidate shift scheduling schemes are iteratively optimized through a multi-objective optimization model of a genetic algorithm to obtain a target shift scheduling scheme for the next shift scheduling cycle.

2. The method according to claim 1, characterized in that The iterative optimization of the plurality of candidate shift scheduling schemes by using a preset multi-objective optimization model to obtain a target shift scheduling scheme for the next shift scheduling cycle includes: Calculate the fitness data corresponding to each candidate scheduling scheme; Determine, according to the fitness data, a plurality of shift scheduling schemes to be optimized whose selection probabilities meet preset shift scheduling requirements; The multiple shift scheduling schemes to be optimized are cross-processed to obtain at least one target shift scheduling scheme.

3. The method according to claim 2, characterized in that The step of calculating the fitness data corresponding to each candidate shift scheduling scheme includes: Extracting data feature values ​​and feature importance data corresponding to each candidate scheduling plan; Constructing a feature importance index matrix and a feature importance index average value according to the feature importance data; Extracting a target feature importance index that is greater than an average value of the feature importance index in the feature importance index matrix; Constructing a fitness function according to the sum of the target feature importance indicators; The fitness data of each candidate scheduling scheme is calculated based on the fitness function.

4. The method according to claim 3, characterized in that The step of determining, according to the fitness data, a plurality of shift scheduling schemes to be optimized whose selection probabilities meet preset shift scheduling requirements comprises: Calculate the corresponding selection probability according to the fitness data of each candidate scheduling plan; Determine the target candidate shift scheduling scheme with the highest probability among the candidate shift scheduling schemes; The target candidate shift scheduling scheme and the target candidate shift scheduling scheme whose selection probability is greater than a preset probability threshold are used as the shift scheduling scheme to be optimized.

5. The method according to claim 2, characterized in that The cross-processing of the plurality of shift scheduling schemes to be optimized to obtain at least one target shift scheduling scheme includes: Performing a variation process on the at least one new shift scheduling scheme to obtain a plurality of variation shift scheduling schemes; The multiple variant shift scheduling schemes are used as candidate shift scheduling schemes, and the multiple candidate shift scheduling schemes are iteratively optimized through the multi-objective optimization model of the genetic algorithm to obtain the target shift scheduling scheme for the next shift scheduling cycle.

6. The method according to any one of claims 1 to 5, characterized in that The flood prevention scheduling method based on the multi-objective optimization genetic algorithm combined with rainfall conditions also includes: Broadcasting the target scheduling plan for the next scheduling cycle and receiving feedback from each on-duty personnel on the target scheduling plan; The target shift scheduling plan is verified or updated based on the feedback information.

7. The method according to any one of claims 1 to 5, characterized in that Before generating at least one candidate scheduling scheme based on the initial scheduling model and a plurality of preset scheduling model scheduling constraints, the method further includes: Get the maximum number of working days in the next shift cycle, the number of on-duty employees in each shift, and the rated number of on-duty employees in one shift day; Constructing a first scheduling constraint condition according to the maximum number of working days, so that the scheduling scheme is less than or equal to the constraint condition of the maximum number of working days; Constructing a second shift scheduling constraint condition according to the number of employees on duty, so that the shift scheduling plan meets the constraints of the maximum number of employees on duty and the minimum number of employees on duty; A third shift scheduling constraint condition is constructed according to the rated duty number, so that the shift scheduling plan meets the constraint condition of the rated duty number of each employee on each duty day.

8. A flood prevention scheduling system based on multi-objective optimization genetic algorithm combined with rainfall conditions, characterized in that: The flood prevention scheduling system based on multi-objective optimization genetic algorithm combined with rainfall conditions includes: The acquisition module is used to obtain the list of on-duty personnel in each department and the current water situation information; A construction module, used to construct an initial scheduling model according to the duty personnel list and the current water situation information; A generation module, used to generate multiple candidate scheduling solutions based on the initial scheduling model and multiple scheduling constraints; The optimization module is used to iteratively optimize the multiple candidate shift scheduling schemes through a multi-objective optimization model of a genetic algorithm to obtain a target shift scheduling scheme for the next shift scheduling cycle.

9. A flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions, characterized in that: The flood prevention scheduling device based on a multi-objective optimization genetic algorithm combined with rainfall conditions comprises: a memory, a processor, and a flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions stored in the memory and executable on the processor. The flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions is configured to implement the steps of the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions. When the flood prevention scheduling program based on a multi-objective optimization genetic algorithm combined with rainfall conditions is executed by a processor, the steps of the flood prevention scheduling method based on a multi-objective optimization genetic algorithm combined with rainfall conditions as described in any one of claims 1 to 7 are implemented.

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