Government affair service process optimization analysis method based on big data

Abstract

The invention provides a government affair service process optimization analysis method based on big data, and the method comprises the steps: building a global government affair service Petri network based on the support of a government affair service system for handling all types of government affair services and all processing nodes contained in the government affair service system; obtaining all to-be-processed government affair service requirements and state data of each processing node, wherein each government affair service requirement has a processing sequence number; updating the place of the global government affair service Petri network based on the state data of each processing node; and mapping all government affair service requirements into a global government affair service Petri network according to the processing sequence numbers to obtain a corresponding processing network chain, thereby determining a government affair service processing strategy and outputting the government affair service processing strategy. Therefore, the processing network chain of all government affair service requirements mapped in the global government affair service Petri network can be optimized, the government affair service process is optimized, the processing efficiency is improved, and the processing time and the queuing time are optimized.

Description

Technical Field

[0001] This application relates to the field of government service process optimization, and more specifically, to a big data-based method for government service process optimization analysis. Background Technology

[0002] Government services are vital to people's livelihoods, but due to the complexity and cumbersome nature of government processes, they often suffer from long processing times, low efficiency, and long queues. Optimizing government processes, improving efficiency, and reducing processing and queuing times is an urgent problem to solve. Traditional methods for optimizing government processes are often based on rules and experience, but these methods have limitations when dealing with complex government processes.

[0003] Government processes are often complex and dynamic, making them difficult to fully describe and resolve through rules and experience. For example, a government process may involve multiple departments and stages, each of which includes multiple sub-tasks and operations. The processing time and sequence of these tasks and operations may be affected by various factors. Some processing nodes can only handle one business process at a time, but the same node can usually handle more than one government process. Therefore, the changes and uncertainties of these factors make the optimization of government processes more difficult. Summary of the Invention

[0004] The purpose of this application is to provide a big data-based method for optimizing and analyzing government service processes, so as to effectively optimize government service processes, improve processing efficiency, and optimize processing time and queuing time through intelligent means.

[0005] To achieve the above objectives, the embodiments of this application are implemented in the following manner:

[0006] In a first aspect, embodiments of this application provide a big data-based method for optimizing and analyzing government service processes. This method is applied to a government service system that supports multiple government services and includes a server and several processing nodes. Each government service processing involves multiple process steps, and each process step can be processed by any processing node that supports that step. Each processing node supports processing at least one process step, and can only process one process step at a time. The method, applied to the server, includes: constructing a global government service Petri net based on the government service system's support for all types of government services and all processing nodes included in the system. The global government service Petri net includes multiple locations and multiple transitions, with each location associated with at least one transition. Transitions are connected via input arcs, and each transition is connected to at least one place via an output arc. Each process step of each government service is mapped to a transition in the global government service Petri net, and each processing node is mapped to a place in the global government service Petri net, with one processing node corresponding to one place. The system acquires all government service requests to be processed and the status data of each processing node, where each government service request has a processing sequence number. Based on the status data of each processing node, the system updates the places in the global government service Petri net. All government service requests are mapped to the global government service Petri net according to their processing sequence numbers. Based on the processing network chains of all government service requests mapped to the global government service Petri net, the government service processing strategy is determined and output.

[0007] In conjunction with the first aspect, in the first possible implementation of the first aspect, based on the fact that the government service system supports handling all types of government services and all processing nodes contained in the government service system, a global government service Petri net is constructed, including: abstracting all processing nodes contained in the government service system as places in the Petri net; based on the process steps supported by the processing nodes, type restrictions are applied to each place to obtain multiple types of places, and the process steps supported by different types of places are not completely the same; for each type of government service supported by the government service system: the process steps of the current type of government service are abstracted as transitions in the Petri net; for each transition corresponding to the process steps of the current type of government service, each place in the Petri net is traversed, and all places supporting the process steps corresponding to the transition are established with the transition as input arcs, and all places supporting the process steps corresponding to the next transition are established with the transition as output arcs, until all process steps of the current type of government service form a complete processing network chain in the Petri net; until each type of government service forms a complete processing network chain in the Petri net, the global government service Petri net is obtained.

[0008] In conjunction with the first possible implementation of the first aspect, in the second possible implementation of the first aspect, the state data includes the activity status and processing efficiency of the processing nodes. Based on the state data of each processing node, the location of the global government service Petri net is updated, including: for each processing node: based on the activity status and processing efficiency of the processing node, the parameter information of the processing node is updated to obtain the updated location.

[0009]

[0010] in, Let i be the location of the i-th processing node at time step t. This represents the active state of the i-th processing node at time step t, used to reveal the process steps that the i-th processing node supports processing at time step t. The processing efficiency of the i-th processing node at time step t is used to reveal the unit time required for the i-th processing node to process each process step at time step t.

[0011] In conjunction with the second possible implementation of the first aspect, in the third possible implementation of the first aspect, each government service request has a type identifier. All government service requests are mapped to the global government service Petri net according to their processing sequence number. This includes mapping each government service request to the global government service Petri net according to its processing sequence number in the following way: For each process step required for the current government service request, the current process step is abstracted into a transition. The corresponding processing sequence number will be used as a priority parameter to distinguish the processing priority of this process step. Among them, the establishment of change With the warehouse The relationship between the input arcs is expressed as follows: Establishing changes With the warehouse The output arc relationship between them is expressed as The processing network chain formed by the current government service demands in the global government service Petri net is obtained as follows:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in, This refers to the processing network formed by the s-th government service request within the global Petri net of government services. This represents the set of libraries corresponding to the s-th government service request in the global Petri net of government services. This represents the set of active states associated with the corresponding library set for the s-th government service request in the global Petri net of government services. This represents the set of processing efficiencies associated with the corresponding library set for the s-th government service request in the global Petri net of government services. Let represent the transition set corresponding to the s-th government service request in the global Petri net of government services. This represents the priority parameter set associated with the transition set corresponding to the s-th government service request in the global government service Petri net.

[0019] In conjunction with the third possible implementation of the first aspect, in the fourth possible implementation of the first aspect, based on the processing network chain mapped to the global government service Petri net of all government service demands, a government service processing strategy is determined and output, including: the processing network chain formed by n government service demands in the global government service Petri net. Constructing the time cost function:

[0020]

[0021]

[0022] Among these, CT represents the time cost. Let s be the start time of the s-th government service request. Let be the end time of the s-th government service request, n be the total number of government service requests, and Ms be the total number of process steps included in the s-th government service request. The processing time for the l-th step of the process for the s-th government service request. The waiting time for the l-th process step of the s-th government service request;

[0023] Constructing timing constraints:

[0024] T(p) = p,

[0025] Where T(p) = p indicates that government service requests are processed in the order of their processing sequence numbers;

[0026] Construct priority constraints:

[0027] R(p) = p,

[0028] Where R(p) = p means that any processing node processes the process steps assigned to it according to the priority of the sequence number, and the smaller the sequence number, the higher the priority; use a greedy algorithm or genetic algorithm to solve, determine the government service processing strategy and output it.

[0029] Combining the fourth possible implementation of the first aspect, in the fifth possible implementation of the first aspect, a genetic algorithm is used to solve and determine the government service processing strategy and output it, including: Step 1: Based on the processing network chain formed by n government service requirements in the global government service Petri net. Step 1: Initialize the population; Step 2: Calculate the fitness function value and select the best individual as the parent to be retained based on the fitness function value; Step 3: Perform crossover and mutation operations; Step 4: Add the newly generated individuals to the population to form a new population; Step 5: Repeat steps 2-4 until the termination condition is met; Step 6: After the termination condition is met, output the optimal result as the government service processing strategy.

[0030] In conjunction with the fifth possible implementation of the first aspect, in the sixth possible implementation of the first aspect, the method for calculating the fitness function value in step 2 is as follows: The fitness function value is calculated using the following formula:

[0031] Fitness = ω1 × CT + ω2 × t total +ω3×Δt total ,

[0032] Where Fitness is the fitness function value, ω1, ω2, and ω3 are the weights, CT is the time cost, and t is the time cost. total Δt represents the total processing time for all process steps of all government service requests, and Δt represents the total waiting time for all process steps of all government service requests.

[0033] Secondly, embodiments of this application provide a government service system, including a server and several processing nodes. The government service system supports the processing of multiple government services. The processing of each government service includes multiple process steps. Each process step can be processed by any processing node that supports processing that process step. A processing node supports processing at least one process step, and a processing node can only process one process step at a time. The server is used to run the big data-based government service process optimization analysis method according to any one of the first aspect or any possible implementation of the first aspect, and to arrange processing nodes to process government service needs through the determined government service processing strategy.

[0034] Thirdly, embodiments of this application provide a storage medium, characterized in that the storage medium is disposed within a server and includes a stored program, wherein, when the program is running, it controls the server where the storage medium is located to execute the big data-based government service process optimization and analysis method described in the first aspect or any possible implementation of the first aspect.

[0035] Fourthly, embodiments of this application provide a server, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement the steps of the big data-based government service process optimization and analysis method described in any one of the first aspects or possible implementations of the first aspect.

[0036] Beneficial effects:

[0037] 1. The government service system supports the processing of multiple government services. Utilizing the system's support for all types of government services and all processing nodes, a global government service Petri net is constructed (containing multiple locations and transitions; each location is connected to at least one transition via an input arc, and each transition is connected to at least one location via an output arc. Each process step of each government service is mapped to a transition in the global government service Petri net, and each processing node is mapped to a location in the global government service Petri net, with one processing node corresponding to one location). Then, all pending government service requests (with processing sequence numbers) and the status data of each processing node are obtained. Based on the status data of each processing node, the locations in the global government service Petri net are updated. All government service requests are mapped to the global government service Petri net according to their processing sequence numbers. Based on the processing network chains of all government service requests mapped in the global government service Petri net, the government service processing strategy is determined and output. This approach utilizes a globally constructed Petri net for government services (which can be understood as a large framework that allows each subsequent government service to be mapped to the globally constructed Petri net without needing to build a separate Petri net for each government service). This enables the government service system to support the processing of each type of government service, which can be mapped to the globally constructed Petri net to obtain the corresponding processing network chain. By optimizing the processing network chains mapped to all government service needs in the globally constructed Petri net, the government service processing strategy is determined, thereby optimizing the government service process, improving processing efficiency, and reducing processing time and queuing time.

[0038] 2. Abstract all processing nodes as locations in a Petri net, and abstract the process steps of each type of government service as transitions in a Petri net. Establish possible input arc relationships or output arc relationships between each location and each transition to construct a global government service Petri net. Map government service demands onto the global government service Petri net to form a processing network chain. Consider the activity status of processing nodes (revealing the process steps they support) and processing efficiency (revealing the unit time required to process each process step), consider the priority of processing steps, construct corresponding time cost functions and constraints, and use greedy algorithms or genetic algorithms to find the optimal government service processing strategy, effectively optimizing the government service process.

[0039] 3. When using genetic algorithms for optimization, in addition to considering time cost, the total processing time and total waiting time of all process steps for all government service requests are also considered, with corresponding weights assigned to account for multiple factors: time cost considers the fastest completion time for all government service requests; total processing time considers the processing time for all government service requests (which can be understood as a consideration of processing nodes); and total waiting time considers the waiting time for all government service requests (which can be understood as a consideration of user queuing). By comprehensively considering all factors, a better government service processing strategy is sought. When subsequent service criteria change (e.g., with a greater emphasis on user experience), the corresponding weights can be adjusted to find a more suitable solution, demonstrating good applicability.

[0040] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of a government service system provided in an embodiment of this application.

[0043] Figure 2 A flowchart illustrating a big data-based government service process optimization and analysis method provided in this application embodiment.

[0044] Figure 3 A flowchart for using genetic algorithms to find optimal government service processing strategies.

[0045] Icons: 10 - Government Service System; 11 - Server; 12 - Processing Node. Detailed Implementation

[0046] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0047] Please see Figure 1 , Figure 1 This is a schematic diagram of a government service system 10 provided in an embodiment of this application.

[0048] In this embodiment, the government service system 10 may include a server 11 and several processing nodes 12. The government service system 10 supports the processing of multiple government services. The processing of each government service includes multiple process steps. Each process step can be processed by any processing node 12 that supports processing that process step. A processing node 12 supports processing at least one process step, and a processing node 12 can only process one process step at a time.

[0049] Server 11 can run a big data-based government service process optimization analysis method and arrange processing node 12 to process government service needs through the determined government service processing strategy.

[0050] Please see Figure 2 , Figure 2 This is a flowchart illustrating a big data-based government service process optimization and analysis method provided in an embodiment of this application. In this embodiment, the big data-based government service process optimization and analysis method may include steps S10, S20, S30, S40, and S50.

[0051] First, server 11 can run step S10.

[0052] Step S10: Based on the fact that the government service system supports the handling of all types of government services and all processing nodes included in the government service system, construct a global government service Petri net. The global government service Petri net contains multiple locations and multiple transitions. Each location is connected to at least one transition through an input arc, and each transition is connected to at least one location through an output arc. Each process step of each government service is mapped to a transition in the global government service Petri net, and each processing node is mapped to a location in the global government service Petri net. One processing node corresponds to one location.

[0053] In this embodiment, in order to improve the efficiency of the subsequent government service system 10 during operation, the server 11 can first construct a global government service Petri net.

[0054] For example, server 11 can abstract all processing nodes 12 contained in government service system 10 into Petri nets, and then restrict the type of each net based on the processing steps supported by processing node 12, resulting in multiple types of nets. Different types of nets support different processing steps.

[0055] For the government service system 10, which supports the handling of each type of government service, the server 11 can abstract the process steps of the current type of government service into the transition of a Petri net.

[0056] For each transition corresponding to the process steps of the current type of government service, server 11 can traverse every location in the Petri net, establishing input arc relationships between all locations supporting the process steps corresponding to that transition and that transition, and establishing output arc relationships between all locations supporting the process steps corresponding to the next transition and that transition, until all process steps of the current type of government service form a complete processing network chain in the Petri net. Here, the global government service Petri net can be regarded as a large framework. In subsequent actual operation, various government service requirements can be mapped to the global government service Petri net to quickly obtain the corresponding processing network chain (which can be understood as an operating logic; a process step can be processed by any processing node 12 that supports processing that process step. Therefore, the processing network chain corresponding to the government service requirement actually has multiple specific processing schemes).

[0057] Once each type of government service forms a complete processing network in the Petri net, the global government service Petri net is obtained. After the global government service Petri net is constructed, server 11 can run step S20.

[0058] Step S20: Obtain all pending government service requests and the status data of each processing node, wherein each government service request has a processing sequence number.

[0059] In this embodiment, server 11 can obtain all pending government service requests (each government service request has a processing sequence number) and status data of each processing node 12. For example, the data can be obtained in stages (e.g., once per hour, or once every half hour, or once every ten minutes, or even once every 3 minutes).

[0060] After obtaining all government service requests and the status data of each processing node 12, server 11 can run step S30.

[0061] Step S30: Update the database of the global Petri net for government services based on the status data of each processing node.

[0062] In this embodiment, the status data includes the activity status of processing node 12 (revealing the process steps that processing node 12 currently supports processing) and processing efficiency (revealing the unit time required for processing node 12 to process each process step). Therefore, for each processing node 12:

[0063] Server 11 can update the parameter information of processing node 12 based on the activity status and processing efficiency of processing node 12, and obtain the updated library:

[0064]

[0065] in, For the i-th processing node 12 at time step t, This represents the active state of the i-th processing node 12 at time step t, used to reveal the process steps supported by the i-th processing node 12 at time step t. The processing efficiency of the i-th processing node 12 at time step t is used to reveal the unit time required by the i-th processing node 12 to process each process step at time step t.

[0066] After the Petri Net database for government services is fully updated, server 11 can run step S40.

[0067] Step S40: Map all government service requests to the global government service Petri net according to their processing sequence number.

[0068] In this embodiment, each government service request has a type identifier. Server 11 can map each government service request to the global government service Petri net according to the processing sequence number in the following manner:

[0069] For each process step required by current government service needs: abstract the current process step into a change. The corresponding processing sequence number will be used as a priority parameter to distinguish the processing priority of this process step. So, establish change With the warehouse The relationship between the input arcs is expressed as:

[0070]

[0071] Establishing changes With the warehouse The output arc relationship between them is expressed as:

[0072]

[0073] Therefore, we can obtain the processing network chain formed by the current government service demands in the global government service Petri net:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] in, This refers to the processing network formed by the s-th government service request within the global Petri net of government services. This represents the set of libraries corresponding to the s-th government service request in the global Petri net of government services. This represents the set of active states associated with the corresponding library set for the s-th government service request in the global Petri net of government services. This represents the set of processing efficiencies associated with the corresponding library set for the s-th government service request in the global Petri net of government services. Let represent the transition set corresponding to the s-th government service request in the global Petri net of government services. This represents the priority parameter set associated with the transition set corresponding to the s-th government service request in the global government service Petri net.

[0081] After mapping all government service requests to the global government service Petri network according to their processing sequence number and obtaining the corresponding processing network chain, server 11 can run step S50.

[0082] Step S50: Based on the processing network chain of all government service requirements mapped in the global government service Petri net, determine the government service processing strategy and output it.

[0083] In this embodiment, server 11 can form a processing network chain based on n government service requests in the global government service Petri net. Constructing the time cost function:

[0084]

[0085]

[0086] Among these, CT represents the time cost. Let s be the start time of the s-th government service request. Let be the end time of the s-th government service request, n be the total number of government service requests, and Ms be the total number of process steps included in the s-th government service request. The processing time for the l-th step of the process for the s-th government service request. The waiting time for the l-th process step of the s-th government service request. This represents the largest (i.e., latest) end time among n government service requests. This represents the smallest (i.e. earliest) start time among n government service requests.

[0087] Constructing timing constraints:

[0088] T(p)=p, (12)

[0089] Where T(p) = p indicates that government service requests are processed in the order of their processing numbers.

[0090] Construct priority constraints:

[0091] R(p)=p, (13)

[0092] Where R(p) = p means that any processing node 12 processes the process steps assigned to it according to the priority of the sequence number, and the smaller the sequence number, the higher the priority.

[0093] In this embodiment, no upper limit is set on the processing capacity of processing node 12, because such a situation generally does not exist in practice (because time cost and waiting time will constrain this issue). If necessary, corresponding constraints can be constructed for use (constraining the number of processing steps of each processing node 12, i.e., the number of warehouse identifiers does not exceed the set value), which is not limited here.

[0094] After constructing the time cost function and constraints, server 11 can use a greedy algorithm or a genetic algorithm to solve the problem, determine the government service processing strategy, and output the result.

[0095] In this embodiment, server 11 uses a genetic algorithm to determine and output the government service processing strategy. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a flowchart illustrating how to use a genetic algorithm to optimize government service processing strategies. Optimizing government service processing strategies using a genetic algorithm may include steps 1, 2, 3, 4, 5, and 6.

[0096] Step 1: The processing network formed in the global Petri net of government services based on n government service requests. Initialize the population. For example, the pseudocode is as follows:

[0097] definitialize_population(population_size):

[0098] population=[generate_random_chromosome()for_inrange(population_size)]

[0099] returnpopulation

[0100] The `initialize_population` function is used to generate the initial population, where `population_size` is the specified population size. It initializes the entire population by calling the `generate_random_chromosome` function to generate a random chromosome for each individual. Afterwards, random chromosomes can be generated, as shown in the pseudocode below:

[0101] defgenerate_random_chromosome():

[0102] chromosome=[generate_random_node()for_inrange(num_nodes)]

[0103] return chromosome

[0104] The `generate_random_chromosome` function is used to generate random chromosomes, while processing the network... Each node in the population corresponds to a gene locus on the chromosome. num_nodes represents the number of nodes. By calling the generate_random_node function, a random node is generated for each gene in the chromosome, thus completing the population initialization.

[0105] Step 2: Calculate the fitness function value and select the best individual as the parent to be retained based on the fitness function value.

[0106] In this embodiment, server 11 can calculate the fitness function value using the following formula:

[0107] Fitness = ω1 × CT + w2 × t total +w3×Δt total (14)

[0108] Where Fitness is the fitness function value, ω1, w2, and w3 are the weights, CT is the time cost, and t is the time cost. totalLet Δt be the total processing time for all steps in all processes of all government service requests, and Δt be the total waiting time for all steps in all processes of all government service requests. The pseudocode is as follows:

[0109] deffitness(chromosome,w1,w2,w3):

[0110] CT = calculate_CT(chromosome)

[0111] t_total=calculate_t_total(chromosome)

[0112] Δt_total=calculate_Δt_total(chromosome)

[0113] fitness_value=w1*CT+w2*t_total+w3*Δt_total

[0114] return fitness_value

[0115] The fitness function is used to calculate the fitness value of a chromosome. In the context of government processes, the fitness function considers time cost, total processing time, and total queuing time, and designs corresponding weight parameters ω1, ω2, and ω3. These parameters can be used to adjust the impact of different parts on the overall fitness.

[0116] After calculating the fitness function value of each individual, a subset of the best individuals can be selected as parents to be retained, and then step 3 can be performed.

[0117] Step 3: Perform crossover and mutation operations.

[0118] In this embodiment, the crossover function can be used to perform the crossover operation, and the mutate function can be used to perform the mutation operation. The pseudocode is as follows:

[0119] cross:

[0120] def crossover(parent1,parent2):

[0121] crossover_point=random.randint(1,len(parent1)-1)

[0122] child1=parent1[:crossover_point]+parent2[crossover_point:]

[0123] child2=parent2[:crossover_point]+parent1[crossover_point:]

[0124] return child1,child2

[0125] Mutations:

[0126] defmutate(chromosome):

[0127] mutation_point=random.randint(0,len(chromosome)-1)

[0128] mutated_chromosome=chromosome.copy()

[0129] mutated_chromosome[mutation_point]=generate_random_node()

[0130] return mutated_chromosome

[0131] The `crossover` function performs a crossover operation, generating two offspring chromosomes by crossing the genes of two parent chromosomes. In the context of government processes, this operation preserves information about each node, ensuring that the offspring contain legitimate node information from both parents. The `mutate` function performs a mutation operation, generating a mutated chromosome by randomly replacing or adding / deleting nodes. In the context of government processes, this ensures that the mutated individual maintains the legitimacy of the process.

[0132] After performing the crossover and mutation operations, you can proceed to step 4.

[0133] Step 4: Add the newly generated individuals to the population to form a new population.

[0134] Next, you can proceed to step 5.

[0135] Step 5: Repeat steps 2-4 until the termination condition is met.

[0136] Termination conditions include: reaching the maximum number of iterations or finding a satisfactory solution.

[0137] Step 6: After the termination condition is met, output the optimal result as the government service processing strategy.

[0138] Therefore, server 11 can determine the government service processing strategy, and thus control the task allocation of each processing node 12 according to the government service processing strategy, thereby optimizing the government service process.

[0139] This application provides a storage medium located within a server 11, which includes a stored program. When the program runs, it controls the server 11 containing the storage medium to execute the big data-based government service process optimization and analysis method of this embodiment.

[0140] Furthermore, this application embodiment also provides a server 11, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, the steps of the big data-based government service process optimization and analysis method of this embodiment are implemented.

[0141] In summary, this application provides a big data-based method for optimizing and analyzing government service processes. It utilizes the government service system 10, which supports all types of government services, and all processing nodes 12 within the system. A global government service Petri net is constructed (containing multiple locations and transitions; each location is connected to at least one transition via an input arc, and each transition is connected to at least one location via an output arc. Each process step of each government service is mapped to a transition in the global government service Petri net, and each processing node 12 is mapped to a location in the global government service Petri net, with one processing node 12 corresponding to one location). Then, all pending government service requests (with processing sequence numbers) and the status data of each processing node 12 are obtained. Based on the status data of each processing node 12, the locations in the global government service Petri net are updated. All government service requests are mapped to the global government service Petri net according to their processing sequence numbers. Based on the processing network chains of all government service requests mapped in the global government service Petri net, a government service processing strategy is determined and output. This approach utilizes a globally constructed Petri net for government services (which can be understood as a large framework that allows each subsequent government service to be mapped to the globally constructed Petri net without needing to build a separate Petri net for each government service). This enables the government service system 10 to support the mapping of each type of government service to the corresponding processing network chain within the globally constructed Petri net. By optimizing the processing network chains mapped to all government service needs within the globally constructed Petri net, a government service processing strategy is determined, thereby optimizing the government service process, improving processing efficiency, and reducing processing and queuing times.

[0142] All processing nodes 12 are abstracted as places in a Petri net, and the process steps of each type of government service are abstracted as transitions in a Petri net. The possible input arc relationships or output arc relationships between each place and each transition are established to construct a global government service Petri net. Government service demands are mapped onto the global government service Petri net to form a processing network chain. Considering the activity state of processing nodes 12 (revealing the process steps they support) and processing efficiency (revealing the unit time required to process each process step), and considering the priority of the process steps, corresponding time cost functions and constraints are constructed. Greedy algorithms or genetic algorithms are used for optimization to determine the government service processing strategy, effectively optimizing the government service process.

[0143] When using a genetic algorithm for optimization, in addition to considering time cost, the total processing time and total waiting time of all process steps for all government service requests are also considered, with corresponding weights assigned to account for multiple factors: time cost is the fastest completion time for all government service requests, total processing time is the processing time for all government service requests (which can be understood as a consideration of processing node 12), and total waiting time is the waiting time for all government service requests (which can be understood as a consideration of user queuing). By comprehensively considering all factors, a better government service processing strategy is sought. When subsequent service criteria change (e.g., with a greater emphasis on user experience), the corresponding weights can be adjusted to find a more suitable solution, demonstrating good applicability.

[0144] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0145] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing and analyzing government service processes based on big data, characterized in that, This method is applied to a government service system that supports multiple government services. The system has a server and several processing nodes. Each government service involves multiple process steps, and each step can be processed by any processing node that supports that step. Each processing node can handle at least one process step, and can only handle one process step at a time. The method is applied to the server and includes: Based on the fact that the government service system supports the handling of all types of government services and all processing nodes contained in the government service system, a global government service Petri net is constructed. The global government service Petri net contains multiple locations and multiple transitions. Each location is connected to at least one transition through an input arc, and each transition is connected to at least one location through an output arc. Each process step of each government service is mapped to the transition of the global government service Petri net, and each processing node is mapped to a location of the global government service Petri net, where one processing node corresponds to one location. Obtain all pending government service requests and the status data of each processing node, where each government service request has a processing sequence number; Update the global Petri net of government services based on the status data of each processing node. Map all government service requests to the global government service Petri network according to their processing sequence number; Based on the processing network chain of all government service demands mapped onto the global government service Petri net, the government service processing strategy is determined and output.

2. The method for optimizing and analyzing government service processes based on big data according to claim 1, characterized in that, Based on the government service system's support for handling all types of government services and all processing nodes within the system, a global Petri net for government services is constructed, including: Abstract all processing nodes in the government service system into places in a Petri net; Based on the processing node's supported processing steps, each storage location is restricted by type, resulting in multiple types of storage locations. The processing steps supported by different types of storage locations are not exactly the same. Regarding the government service system's support for processing each type of government service: The process steps of current types of government services are abstracted into the evolution of Petri nets; For each transition corresponding to the process steps of the current type of government service, traverse each location in the Petri net, establish input arc relationships between all locations that support the process steps corresponding to the transition and the transition, and establish output arc relationships between all locations that support the process steps corresponding to the next transition and the transition, until all process steps of the current type of government service form a complete processing network chain in the Petri net; This continues until each type of government service forms a complete processing network in the Petri net, resulting in a global government service Petri net.

3. The method for optimizing and analyzing government service processes based on big data according to claim 2, characterized in that, Status data includes the activity status and processing efficiency of processing nodes. Based on the status data of each processing node, the database of the global Petri net for government services is updated, including: For each processing node: Based on the activity status and processing efficiency of the processing nodes, update the parameter information of the processing nodes to obtain the updated locations: in, Let i be the location of the i-th processing node at time step t. This represents the active state of the i-th processing node at time step t, used to reveal the process steps that the i-th processing node supports processing at time step t. The processing efficiency of the i-th processing node at time step t is used to reveal the unit time required for the i-th processing node to process each process step at time step t.

4. The method for optimizing and analyzing government service processes based on big data according to claim 3, characterized in that, Each government service request has a type identifier. All government service requests are mapped to the global government service Petri net according to their processing sequence number, including: Based on the processing sequence number, each government service request is mapped to the global government service Petri network in the following manner: For each process step required to meet current government service needs: Abstract the current process steps into transitions. The corresponding processing sequence number will be used as a priority parameter to distinguish the processing priority of this process step. Among them, establishing changes With the warehouse The input arc relationship between them is expressed as Establishing changes With the warehouse The output arc relationship between them is expressed as The processing network chain formed by the current government service demands in the global government service Petri net is obtained as follows: in, This refers to the processing network formed by the s-th government service request within the global Petri net of government services. This represents the set of libraries corresponding to the s-th government service request in the global Petri net of government services. This represents the set of active states associated with the corresponding library set for the s-th government service request in the global Petri net of government services. This represents the set of processing efficiencies associated with the corresponding library set for the s-th government service request in the global Petri net of government services. Let represent the transition set corresponding to the s-th government service request in the global Petri net of government services. This represents the priority parameter set associated with the transition set corresponding to the s-th government service request in the global government service Petri net.

5. The method for optimizing and analyzing government service processes based on big data according to claim 4, characterized in that, Based on the processing network chains of all government service demands mapped onto the global Petri net of government services, the government service processing strategies are determined and output, including: A processing network is formed based on n government service requests within a global government service Petri net. Constructing the time cost function: Among these, CT represents the time cost. Let s be the start time of the s-th government service request. Let be the end time of the s-th government service request, n be the total number of government service requests, and Ms be the total number of process steps included in the s-th government service request. The processing time for the l-th step of the process for the s-th government service request. The waiting time for the l-th process step of the s-th government service request; Constructing timing constraints: T(p) = p, Where T(p) = p indicates that government service requests are processed in the order of their processing sequence numbers; Construct priority constraints: R(p) = p, Where R(p) = p means that any processing node processes the process steps assigned to it according to the priority of the sequence number, and the smaller the sequence number, the higher the priority. Solve using a greedy algorithm or a genetic algorithm, determine the government service processing strategy, and output the solution.

6. The method for optimizing and analyzing government service processes based on big data according to claim 5, characterized in that, The genetic algorithm is used to solve and determine the government service processing strategy, which is then output, including: Step 1: The processing network formed in the global Petri net of government services based on n government service requests. Initialize the population; Step 2: Calculate the fitness function value and select the best individual as the parent to be retained based on the fitness function value; Step 3: Perform crossover and mutation operations; Step 4: Add the newly generated individuals to the population to form a new population; Step 5: Repeat steps 2-4 until the termination condition is met; Step 6: After the termination condition is met, output the optimal result as the government service processing strategy.

7. The method for optimizing and analyzing government service processes based on big data according to claim 6, characterized in that, The method for calculating the fitness function value in step 2 is as follows: The fitness function value is calculated using the following formula: Fitness=ω1×CT+ω2×t total +ω3×Δt total , Where Fitness is the fitness function value, ω1, ω2, and ω3 are the weights, CT is the time cost, and t is the time cost. total Δt represents the total processing time for all process steps of all government service requests, and Δt represents the total waiting time for all process steps of all government service requests.

8. A government service system, characterized in that, The system includes a server and several processing nodes. The government service system supports the processing of multiple government services. The processing of each government service includes multiple process steps. Each process step can be processed by any processing node that supports processing that process step. A processing node supports processing at least one process step, and a processing node can only process one process step at a time. The server is used to run the big data-based government service process optimization analysis method according to any one of claims 1-7, and to arrange processing nodes to process government service needs through the determined government service processing strategy.

9. A storage medium, characterized in that, The storage medium is located within the server and includes a stored program, wherein, when the program is running, it controls the server where the storage medium is located to execute the big data-based government service process optimization and analysis method as described in any one of claims 1 to 7.

10. A server comprising a memory and a processor, the memory for storing information including program instructions, and the processor for controlling the execution of the program instructions, characterized in that: When the program instructions are loaded and executed by the processor, they implement the steps of the big data-based government service process optimization and analysis method as described in any one of claims 1 to 7.

Images