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CN-122022322-A - Tourist attraction resource scheduling method and system based on dynamic supply and demand model

CN122022322ACN 122022322 ACN122022322 ACN 122022322ACN-122022322-A

Abstract

The invention relates to the technical field of tourist attraction management, in particular to a tourist attraction resource scheduling method and system based on a dynamic supply and demand model, comprising the steps of obtaining passenger flow data, resource state data and environment data of a attraction; the method comprises the steps of constructing a resource region adaptation network, extracting static properties and dynamic states of a resource region by utilizing a standardized data set, obtaining adaptation characteristics by improving a stacked self-encoder, adjusting the weight of the resource region adaptation network in real time based on the static properties and the dynamic states, constructing a prediction model, fusing the adaptation characteristics, obtaining region predicted passenger flow by dynamic fusion weight adjustment, calculating region dynamic bearing capacity by combining the standardized data set, determining dynamic supply and demand gaps based on the difference value between the region predicted passenger flow and the region dynamic bearing capacity, and determining resource adaptation priority according to the dynamic supply and demand gaps, thereby solving the problems that the intellectualization of resource scheduling is difficult to realize in the prior art and the efficient utilization requirement of scenic spots on resources cannot be met.

Inventors

  • SUN TAO

Assignees

  • 青岛酒店管理职业技术学院

Dates

Publication Date
20260512
Application Date
20260128

Claims (10)

  1. 1. A tourist attraction resource scheduling method based on a dynamic supply and demand model is characterized by comprising the following steps: obtaining passenger flow data, resource state data and environment data of a scenic spot, and obtaining a standardized data set through processing; Constructing a resource region adaptation network, extracting static properties and dynamic states of a resource region by using the standardized data set, and obtaining adaptation characteristics by improving a stacked self-encoder, wherein the weight of the resource region adaptation network is adjusted in real time based on the static properties and the dynamic states; constructing a prediction model, fusing the adaptive characteristics, and obtaining regional predicted passenger flow through dynamic fusion weight adjustment; calculating regional dynamic bearing capacity by combining the standardized data set, determining a dynamic supply and demand gap based on the difference value between the regional predicted passenger flow volume and the regional dynamic bearing capacity, and determining resource adaptation priority according to the dynamic supply and demand gap; And constructing a multi-constraint multi-target scheduling model, generating a scheduling scheme based on the dynamic supply and demand gap and the resource adaptation priority, and executing the scheduling scheme.
  2. 2. The method for scheduling tourist attraction resources based on dynamic supply and demand model according to claim 1, wherein the weight calculation of the resource region adapting network comprises: the node weight of the sightseeing vehicle is calculated, and the calculation formula is as follows: wherein 、 The remaining duration of the sightseeing vehicle and the rated duration of the sightseeing vehicle are respectively, For the historical dispatch efficiency of sightseeing vehicles, The state coefficients are maintained for the sightseeing vehicle, For the current passenger capacity of the sightseeing vehicle, Rated passenger capacity for sightseeing vehicles; The node weight of the service personnel is calculated, and the calculation formula is as follows: wherein As a skilled level of skill of the attendant, For the on-duty status factor of the attendant, For the service personnel to have a historical service efficiency, Calculating the distance from the area for the service personnel based on the real-time position; calculating the weight of a pleasure boat node, and comprehensively judging based on the proportion of the remaining range of the pleasure boat to the rated range, the state coefficient of the pleasure boat and the historical scheduling efficiency, wherein the calculation formula is as follows: wherein For the remaining voyage of the pleasure boat, For the rated voyage of the pleasure boat, Is a coefficient of state of the pleasure boat, Efficiency is historically scheduled for pleasure boats.
  3. 3. The method for scheduling tourist attraction resources based on dynamic supply and demand model according to claim 2, wherein the weights of the resource region adaptive network further comprise region node weights and edge weights, and the calculation of the region node weights and edge weights comprises: The regional node weight is calculated, and the calculation formula is as follows: wherein For the maximum load-bearing capacity of the area, For real-time passenger flow volume, For the region function type coefficient, For the open state code to be encoded, Is a regional congestion factor; edge weights are calculated according to the resource types, wherein the sightseeing bus area edge weights, the service personnel area edge weights and the pleasure boat area edge weights are all determined by combining the adaptation similarity between corresponding resources and areas and the historical association frequency, and the calculation formula is as follows: X=1, 2,3 correspond to sightseeing bus, attendant and cruise resources, respectively, where α (t) +β (t) =1, For an adapted similarity of corresponding resources to regions, Is the historical association frequency.
  4. 4. The tourist attraction resource scheduling method based on the dynamic supply and demand model according to claim 1, wherein the steps of constructing the prediction model and fusing the adaptive features, and obtaining the regional prediction passenger flow through dynamic fusion weight adjustment include: The prediction model is a prediction model formed by mixing an autoregressive integral moving average model and an extreme learning machine; The extreme learning machine takes the adaptive characteristics, weather factors and temporary activity information as input to predict the sudden passenger flow deviation of sightseeing vehicles, service personnel and pleasure boat resources; And (3) calculating dynamic fusion weights, wherein the calculation formula is as follows: where k=1, 2,3 represents sightseeing vehicles, attendant and cruise resources, The average absolute error of the length sliding window is preset for the autoregressive integral moving average model, The average absolute error of the length sliding window is preset for the extreme learning machine, Is a region function type coefficient; And obtaining regional predicted passenger flow according to the autoregressive integral moving average model predicted value, the sudden passenger flow deviation predicted by the extreme learning machine model and the dynamic fusion weight.
  5. 5. The method for scenic spot resource scheduling based on a dynamic supply and demand model according to claim 1, wherein the determining a dynamic supply and demand gap based on a difference between the regional predicted passenger flow volume and the regional dynamic bearing capacity comprises: Calculating effective capacity according to the resource type, determining the effective capacity of the sightseeing vehicle based on the rated passenger capacity, the remaining endurance ratio and the maintenance state of the sightseeing vehicle, wherein the calculation formula is as follows: , wherein, Rated passenger capacity for sightseeing vehicles; the effective capacity of service personnel is calculated by combining the skill proficiency and on-duty state coefficient, and the calculation formula is as follows: , wherein, Is a basic bearing coefficient; The effective capacity of the pleasure boat is determined according to the rated passenger capacity of the pleasure boat, the remaining voyage occupation ratio and the state coefficient of the pleasure boat, and the calculation formula is as follows: wherein Rated passenger capacity for pleasure boats; The regional dynamic bearing capacity is a weighted sum of the effective capacity of sightseeing vehicles, service personnel and pleasure boat resources and the corresponding side weight, the unit area bearing capacity based on the regional area and the congestion coefficient is overlapped, and the calculation formula is as follows: wherein Is the bearing coefficient per unit area of the steel plate, In the form of a region area, As a factor of the area crowding, For the edge weights of the corresponding resources, For the total number of sightseeing vehicles, For the total number of service personnel, Is the total number of pleasure boats; and the dynamic supply and demand gap is determined by the difference value between the regional predicted passenger flow volume and the regional dynamic bearing capacity, the difference value is taken when the difference value is positive, and the dynamic supply and demand gap is 0 when the difference value is negative.
  6. 6. The method for scheduling tourist attraction resources based on dynamic supply and demand model according to claim 7, wherein the determining the resource adaptation priority according to the dynamic supply and demand gap comprises: And determining the dynamic supply and demand gap, the regional node weight, the average distance from the resource to the region, the regional congestion coefficient and the demand urgency coefficient, wherein the calculation formula is as follows: , wherein, For the dynamic supply and demand gap, the dynamic supply and demand gap is provided with a plurality of dynamic supply and demand gaps, As the weight of the nodes of the region, For the average distance calculated based on the real-time location of the resource and the region coordinates, As a factor of the area crowding, And the demand emergency coefficient is divided into high, medium and low stages according to the proportion of the supply and demand gap to the maximum bearing capacity of the area.
  7. 7. The method for scheduling tourist attraction resources based on a dynamic supply and demand model according to claim 1, wherein the constructing a multi-constraint multi-objective scheduling model, generating a scheduling scheme based on the dynamic supply and demand gap and the resource adaptation priority, comprises: Constructing a multi-constraint multi-target scheduling model, balancing scheduling cost, tourist waiting time, three types of resource utilization rates and tourist satisfaction by using a target function, and adjusting the importance degree of each target through a preset weight coefficient; the constraint conditions comprise resource allocation uniqueness constraint, gap satisfaction constraint, sightseeing vehicle cruising constraint, pleasure boat course constraint, attendant skill matching constraint and regional bearing upper limit constraint; generating a scheduling scheme by adopting an improved multi-factor evolution algorithm, wherein the population scale, the cross probability and the variation probability of the multi-factor evolution algorithm are dynamically adjusted based on the number of resources and areas, supply and demand gaps and iteration times; After the scheduling scheme is executed, the actual passenger flow volume of the area and the in-place state of three types of resources are monitored in real time, and when the actual supply and demand gap exceeds the preset proportion of the predicted gap or the in-place rate of the resources is lower than a preset threshold value, secondary scheduling is triggered, the resource adaptation priority is determined again, and a scheduling scheme is generated.
  8. 8. A tourist attraction resource scheduling method based on a dynamic supply and demand model, characterized in that the tourist attraction resource scheduling method based on the dynamic supply and demand model according to any one of claims 1-7 is used, comprising: the data acquisition module is configured to acquire passenger flow data, resource state data and environment data of a scenic spot, and a standardized data set is obtained through processing; The adaptive network construction module is configured to construct a resource region adaptive network, extract static properties and dynamic states of a resource region by utilizing the standardized data set, obtain adaptive characteristics by improving a stacked self-encoder, and adjust the weight of the resource region adaptive network in real time based on the static properties and the dynamic states; The passenger flow volume prediction module is configured to construct a prediction model and fuse the adaptive characteristics, and the regional predicted passenger flow volume is obtained through dynamic fusion weight adjustment; the priority determining module is configured to calculate the regional dynamic bearing capacity in combination with the standardized data set, determine a dynamic supply and demand gap based on the difference value between the regional predicted passenger flow volume and the regional dynamic bearing capacity, and determine the resource adaptation priority according to the dynamic supply and demand gap; And the scheduling scheme generating module is configured to construct a multi-constraint multi-target scheduling model, generate a scheduling scheme based on the dynamic supply and demand gap and the resource adaptation priority and execute the scheduling scheme.
  9. 9. A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded and executed by a processor of a terminal device for a method of scenic spot resource scheduling based on a dynamic supply and demand model as claimed in claim 1.
  10. 10. A terminal device comprising a processor for implementing instructions and a computer-readable storage medium for storing instructions, wherein the instructions are adapted to be loaded by the processor and to perform a tourist attraction resource scheduling method based on a dynamic supply and demand model according to claim 1.

Description

Tourist attraction resource scheduling method and system based on dynamic supply and demand model Technical Field The invention relates to the technical field of tourist attraction management, in particular to a tourist attraction resource scheduling method and system based on a dynamic supply and demand model. Background With the digitalized and intelligent transformation of the travel industry, the operation management of scenic spots is developing toward the refinement, and the dynamic scheduling of service resources becomes a core technical branch of the intelligent management of scenic spots. The sightseeing vehicle, service personnel and pleasure boats serve as core carriers for scenic spot operation, and a dynamic matching mechanism of resources and passenger flows suitable for a entourage complex scene is constructed, so that the sightseeing vehicle, service personnel and pleasure boat is one of the intelligent research directions of the current scenic spot. At present, the main stream scenic spot resource scheduling technical schemes in the field mainly comprise two types, namely a traditional scheduling scheme based on manual experience, a semi-automatic scheduling scheme based on a static model, wherein the traditional scheduling scheme is used for presetting the departure frequency of sightseeing vehicles, the attended areas of service personnel and the sailing shift of pleasure boats according to the historical passenger flow distribution of holiday peak time periods, and the semi-automatic scheduling scheme based on a static model is used for presetting a scheduling threshold value by building a statistical model and combining historical passenger flow data, for example, the passenger flow peak time interval of each area is determined through the historical data, and a certain number of sightseeing vehicles and service personnel are fixedly launched in the corresponding time periods. However, in the process of realizing the technical scheme in the embodiment of the application, the inventor discovers that the technology at least has the following technical problems that the existing scenic spot resource scheduling technology lacks a perfect dynamic matching mechanism of resources and passenger flow, and cannot realize the self-adaptive adjustment of a scheduling strategy, so that the resource scheduling is disjointed with the actual supply and demand. The prior art is difficult to realize the intellectualization of resource scheduling, and can not meet the requirement of scenic spots on efficient utilization of resources. Therefore, there is a need for a mechanism that can build dynamic matching of resources to passenger flows to provide a strategy for scheduling resources within a tourist attraction. Disclosure of Invention The embodiment of the application provides a tourist attraction resource scheduling method and system based on a dynamic supply and demand model, which solve the problems that the intellectualization of resource scheduling is difficult to realize and the high-efficiency utilization requirement of scenic attraction on resources cannot be met in the prior art. In a first aspect, an embodiment of the present application provides a tourist attraction resource scheduling method based on a dynamic supply and demand model, including: s1, passenger flow data, resource state data and environment data of a scenic spot are obtained, and a standardized data set is obtained through processing; s2, constructing a resource region adaptation network, extracting static properties and dynamic states of a resource region by using the standardized data set, and obtaining adaptation characteristics by improving a stacked self-encoder, wherein the weight of the resource region adaptation network is adjusted in real time based on the static properties and the dynamic states; s3, constructing a prediction model, fusing the adaptive features, and obtaining regional prediction passenger flow through dynamic fusion weight adjustment; s4, calculating the regional dynamic bearing capacity by combining the standardized data set, determining a dynamic supply and demand gap based on the difference value between the regional predicted passenger flow volume and the regional dynamic bearing capacity, and determining the resource adaptation priority according to the dynamic supply and demand gap; S5, constructing a multi-constraint multi-target scheduling model, generating a scheduling scheme based on the dynamic supply and demand gap and the resource adaptation priority, and executing the scheduling scheme. Further, the obtaining the standardized data set through processing includes: S1.1, the passenger flow data comprise real-time passenger flow of each region, the resource state data comprise rated passenger flow of sightseeing vehicles, rated cruising of sightseeing vehicles, remaining cruising of sightseeing vehicles, historical scheduling efficiency of sightseeing vehicles, maintenance state coefficien