Work order distribution and path planning method based on multi-factor intelligent matching

Abstract

The invention discloses a work order distribution and path planning method based on multi-factor intelligent matching, particularly relates to the field of work order distribution and path planning, and comprises the steps of grid division, priority ranking, data acquisition, index calculation and path planning. According to the method, elastic expansion and contraction of geographic units are realized through dynamic grid reconstruction, work order space-time distribution characteristics are accurately matched, resource scheduling rigid constraints are broken through, a three-dimensional evaluation system is innovated to fuse spatial topology, dynamic efficiency and geometric complexity, and a time period weight dynamic allocation mechanism gives consideration to global optimization and local adaptability. A geometric feature quantification technology is deeply fused, and a path risk is pre-judged through fractal dimension and curvature energy analysis, so that the operation safety and the equipment cruising ability are remarkably improved, and a multi-target collaborative intelligent decision closed loop is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of work order dispatching and path planning. More specifically, the present invention relates to a work order dispatching and path planning method based on multi-factor intelligent matching. Background Art

[0002] In the field of intelligent work order scheduling and path planning, traditional methods mainly rely on basic geographic information systems and static road network data for decision-making. Its core goal is to reduce operation and maintenance costs by optimizing the moving path. The current industry generally adopts a fixed grid division mechanism, divides the service area into equal-sized geographical units, manually marks the work order location, and then calls the Dijkstra algorithm to calculate the shortest path. The operation process of the existing technology includes four stages: manually binding geographic tags after receiving the work order, the static path planning engine generating candidate routes, screening solutions based on historical experience values, and finally dispatching execution instructions to the device.

[0003] There are three key defects in the existing technology: First, the static grid division cannot perceive the spatio-temporal distribution characteristics of work order density, resulting in overloading of grid resources in high-density areas and idle resources in low-density areas; second, the evaluation model only uses the straight-line distance or estimated time as a single indicator, ignoring the coupling relationship between traffic situation dynamic fluctuations, equipment collaboration efficiency, and path geometric characteristics; third, there is a lack of a quantitative standard for path complexity, and traditional methods are difficult to identify invisible risk factors such as high-frequency turning and large-angle bends that affect operation stability. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the existing technology, the present invention provides a work order dispatching and path planning method based on multi-factor intelligent matching. Through the following solutions, it solves the three major defects of rigid static grid division, single-dimensional evaluation ignoring multi-factor interaction, and lack of quantitative standard for path complexity in the above-mentioned background art, resulting in resource misallocation and execution deviation problems.

[0005] To achieve the above object, the present invention provides the following technical solution: A work order dispatching and path planning method based on multi-factor intelligent matching, including: S1: Grid Division: Based on GIS urban construction coordinates and device positioning data, automatically associate the work order with a preset geographical grid, including geographical level division, dynamic density adjustment, boundary anchor point definition, and device binding; S2: Priority Sorting: Establish a three-level sorting rule to assign weights to the work orders, and perform dynamic weight superposition in combination with dependency relationship markings. Select the top 8 for path planning according to the total weight calculation formula; S3: Data Collection: Construct a multi-source heterogeneous data fusion system, covering spatial topology data, dynamic efficiency data, and road network characteristic data, and realize three-dimensional modeling of traffic dynamics, network structure, and device status through a multi-dimensional spatio-temporal data synchronization mechanism; S4: Index Calculation: Based on the data collected in S3, establish a mathematical model to calculate the spatial topology comprehensive index, dynamic efficiency index, and path complexity index; S5: Path Planning: Establish a comprehensive scoring formula based on the three indexes calculated in S4, adjust the comprehensive scoring formula using dynamic weights, and finally set the path planning logic according to the comprehensive score.

[0006] Preferably, the geographical level division is based on administrative divisions as the basic unit, and the GIS urban construction coordinate grid is superimposed within the administrative grid, specifically a 500m×500m square grid. Each grid corresponds to a unique ID in the database, and records the longitude and latitude of the center point and the set of boundary coordinates.

[0007] Preferably, the dynamic density adjustment dynamically adjusts the grid according to the work order density threshold. The area with >50 work orders per month is determined as the high-density area, and the grid is automatically reduced to 200m×200m. The area with <10 work orders per month is determined as the low-density area, and the grids are merged into a 1km×1km large grid. The grid size is recalculated monthly based on the work order heat map, and the boundary is automatically updated through the GIS system.

[0008] Preferably, the boundary anchor points are defined with road boundaries and natural boundaries as the physical boundaries of the grid. The inflection points are marked with longitude and latitude coordinates and stored in the database for real-time matching. Among them, the road boundary is bounded by the center line of the main road. In the natural boundary, the river is divided by the center line of the river channel, and the mountain is referenced by the contour line. When the location of the work order is less than 5 meters away from the boundary, the overflow mechanism is triggered, and it is preferentially assigned to the adjacent grid with 50% fewer current work orders.

[0009] Preferably, the device binding adopts the one-item-one-code pre-association rule. Each device is bound with coordinates during installation, and the system pre-calculates the grid ID to which it belongs. When a work order is generated, the grid is automatically matched according to the device coordinates. For work orders without devices, they are assigned through LBS positioning.

[0010] Preferably, in the three - level sorting rule, the first level is the urgency level. Specifically, 1 - hour single > same - day single > 24 - hour single, which means that it needs to be processed within 1 hour > needs to be processed on the same day > needs to be processed within 24 hours. When the urgency level is a 1 - hour single, the weight is 10; when the urgency level is a same - day single, the weight is 5; when the urgency level is a 24 - hour single, the weight is 1. The second level is the work order type, including fault repair and preventive maintenance. When the work order type is fault repair, the weight is 3; when the work order type is preventive maintenance, the weight is 1. The third level is the dynamic time bonus. For 1 - hour singles, when the remaining time is less than 30 minutes, the priority is increased. When the remaining time ≤ 30 minutes, the weight + 2; when the remaining time ≤ 15 minutes, the weight + 5; when the superposition time ≤ 30 minutes, the bonus is added and the weight + 7. The dependency relationship mark is specifically expressed as: when the target work order not being completed will block other work orders, the target work order is marked as the critical path, and the weight of the critical path work order is multiplied by 1.5.

[0011] Preferably, the total weight calculation formula is specifically expressed as: total score=(urgency level weight×10)+(work order type weight×5)+(overtime bonus×3). The screening logic is: first, sort in descending order of the total score. When the scores are the same, sort in ascending order of the receiving time, and finally select the top 8 to enter the path planning.

[0012] Preferably, the spatial topology data includes grid transition cost, dynamic cross - grid distance, non - Euclidean geometry correction coefficient, and equipment cooperation distance; the dynamic efficiency data includes time - window conflict coefficient, processing duration elasticity, traffic flow pulse coefficient, and dependency relationship waiting duration; the road network characteristic data includes topological migration index, path fractal dimension, curvature energy integral, and self - similarity fluctuation coefficient.

[0013] Preferably, the grid transition cost reads the grid density label through the spatial database and calls the map API to obtain the straight - line distance d between the grid center points base , and then calculate the penalty value according to C hc =1.2d base or 0.8d base . C hc represents the grid transition cost, and C hc =1.2d base is the high - density grid transition cost, and C hc =0.8d base is the low - density grid transition cost; the dynamic cross - grid distance obtains the actual path distance d between the starting point (x s , y s ) and the ending point (x e , y e ) in real - time through the map API, and combines with the traffic coefficient f api to calculate according to D t =d dyn (1 + f api ) t) Calculate, D dyn represents the dynamic cross-grid distance, f t ∈ [0, 1], x s represents the longitude coordinate of the starting point, y s represents the latitude coordinate of the starting point, x e represents the longitude coordinate of the ending point, y e represents the latitude coordinate of the ending point, x s 、y s 、x e and y e Specifically, the decimal degrees in the WGS84 coordinate system are used; the non-Euclidean geometry correction coefficient is obtained by calculating the Euclidean distance , and comparing it with the road network distance dr of the map API to obtain λ ng = d e / d r , λ ng represents the non-Euclidean geometry correction coefficient; the device collaboration distance is calculated by using the Prim algorithm on the device coordinate set P = {(x 1 , y 1 ),..., (x n , y n )} to construct a minimum spanning tree and then calculating the total length D co = ∑∥e k ∥, x n represents the abscissa of the device in the two-dimensional plane coordinate system, y n represents the ordinate of the device in the two-dimensional plane coordinate system, D co represents the device collaboration distance, e k is the edge in the minimum spanning tree.

[0014] Preferably, the time window conflict coefficient α t is obtained by calculating the overlapping duration ratio α c of the current work order window [s c and the historical window set W, α t = ∑max(0, min(e c , e i )) - max(s c , s i )) / (e c - s c ), e c represents the end time of the current work order, e i represents the end time of the scheduled work order, s c represents the start time of the current work order, s i represents the start time of the scheduled work order; the processing duration elasticity σ d is obtained by extracting the historical durations T = {t 1 ,..., t n} Calculate the standard deviation later , t k represents the processing duration of the k-th historical work order, and μ t represents the sample mean of the historical work order processing duration; the traffic flow pulse coefficient β f is obtained by comparing the real-time traffic flow q now with the reference traffic flow q base to get β f = ∣q now − q base ∣ / q base ; the dependency waiting duration T wait is calculated by calculating T wait = max(c j ) − t current , c j represents the estimated completion time of task j, and t current represents the system clock, j ∈ CP.

[0015] Preferably, the topological migration index I td uses the Douglas-Peucker algorithm to compress the GPS trajectory and then statistically calculates the ratio of the number of direction mutations > 45° to the total mileage , Δθ represents the change in the direction angle of adjacent line segments, δ is the indicator function, and L total represents the total path length, and N seg represents the total number of trajectory line segments; the path fractal dimension D f is calculated by calculating the slope of logN(r) using the improved box counting method , r 1 , r 2 represents different grid side lengths, and N(r) represents the number of grids required to cover the path; the curvature energy integral E c is calculated by using Simpson's integral after generating the curvature function κ(s) by cubic spline interpolation , κ(s) represents the curvature value of the path at the arc length position s, L is the total path length, Δs is the integration step size, κ 2i−2 is the curvature at the starting point of the i-th segment, κ 2i−1 is the midpoint curvature, and κ 2i is the ending point curvature; the self-similar fluctuation coefficient γ s is calculated by decomposing the path coordinate sequence into 3 layers by Haar wavelet and then calculating the adjacent scale energy ratio , E l represents the energy of the detail coefficient of the l-th layer.

[0016] Preferably, when implementing S3, the PostGIS spatial engine is used to label the grid density, the traffic data is updated every 3 minutes through the map API, the GPS Beidou terminal collects coordinates at a frequency of 1 Hz, the OR-Tools library is used for multi-objective path optimization, Apache Flink processes real-time data streams, the CGAL library calculates geometric features, the Hungarian algorithm allocates task resources, the anomaly monitoring uses a 5-minute sliding window, and the curvature calculation error is <0.1 rad / km.

[0017] Preferably, the spatial topology comprehensive index is specifically expressed as: , where S represents the spatial topology comprehensive index and η represents the path efficiency weight coefficient.

[0018] Preferably, the dynamic efficiency index is specifically expressed as: , where T represents the dynamic efficiency index and ϕ represents the smoothing constant.

[0019] Preferably, the path complexity index is specifically expressed as: , where P represents the path complexity index, k 1 , k 2 is the power-law adjustment coefficient, and ψ represents the curvature energy smoothing term.

[0020] Preferably, the comprehensive scoring formula is specifically expressed as: , where Z(t) represents the comprehensive score, w s (t) represents the spatial topology weight, w t (t) represents the dynamic efficiency weight, w p (t) represents the path complexity weight, γ represents the emergency work order gain coefficient, τ represents the non-linear attenuation factor, t 0 represents the work order generation time, e is a constant, t is the time, and the path planning logic is: sorted in descending order of Z(t), when Z(t) is the same, sorted in descending order of S, and when S is the same, sorted in ascending order of the work order reception time.

[0021] Preferably, the w s (t), w t (t) and w p (t) are dynamic weights, and the weight distribution of [ws, wt, wp] is [0.3, 0.6, 0.1] during the morning peak period from 7:00 to 9:00, [0.4, 0.3, 0.3] during the flat peak period from 10:00 to 16:00, [0.2, 0.5, 0.3] during the evening peak period from 17:00 to 19:00, and [0.5, 0.2, 0.3] during the night period from 20:00 to 6:00.

[0022] The technical effects and advantages of the present invention: The present invention realizes the elastic expansion and contraction of geographical units through dynamic grid reconstruction technology, automatically adjusts the grid granularity and boundary anchor points based on the spatio-temporal distribution characteristics of work orders, enabling the precise matching of resource scheduling with business requirements. This technology breaks through the rigid constraints of fixed grids, maintains service continuity through an intelligent overflow mechanism during sudden surges in work orders, and reduces computational overhead through grid merging during low-load periods, achieving the collaborative optimization of infrastructure utilization rate and response speed; The present invention innovatively constructs a three-dimensional evaluation system for spatio-temporal paths, incorporates spatial topological efficiency, dynamic traffic risk, and path geometric complexity into a unified calculation framework, and through a dynamic weight allocation mechanism with time period perception, automatically strengthens the time efficiency weight during traffic peak periods to avoid congestion, enhances the spatial coordination weight in areas with intensive equipment operations to optimize resource allocation, and enhances the path stability weight in complex road network sections to ensure execution reliability, forming a multi-objective adaptive intelligent decision-making ability; The present invention deeply integrates geometric feature analysis technology, quantifies the path tortuosity through fractal dimension calculation, and evaluates the driving smoothness in combination with curvature energy integration. This technology first transforms the geometric characteristics of the road network into computable engineering parameters, enabling the planning engine to actively avoid high-energy consumption sections such as high-frequency turns and steep slopes and curves, significantly improving the equipment's endurance and operation safety, and breaking through the empirical dependence of traditional path planning on geometric risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic diagram of the overall structure of the present invention.

[0024] Figure 2 is a schematic diagram of the complete embodiment structure of the present invention.

[0025] Figure 3 is a schematic diagram of the medium topological connection of the present invention.

[0026] Figure 4 is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Refer to Figures 1 - 3 The work order dispatching and path planning method based on multi-factor intelligent matching shown, includes: S1: Grid Division: Based on the urban construction coordinates of GIS and the equipment positioning data, the work orders are automatically associated with the preset geographical grids, including geographical level division, dynamic density adjustment, boundary anchor point definition, and equipment binding.

[0029] The geographical level division is based on administrative divisions, and the GIS urban construction coordinate grid is superimposed within the administrative grid. Specifically, it is a 500m×500m square grid, and each grid corresponds to a unique ID in the database, recording the longitude and latitude of the center point and the set of boundary coordinates. The dynamic density adjustment dynamically adjusts the grids according to the work order density threshold. The area with >50 work orders per month is determined as the high-density area, and the grid is automatically reduced to 200m×200m. The area with <10 work orders per month is determined as the low-density area, and the grids are merged into a 1km×1km large grid. The grid size is recalculated monthly based on the work order heat map, and the boundary is automatically updated through the GIS system. The boundary anchor point definition uses road boundaries and natural boundaries as the physical boundaries of the grids. The inflection points are marked with longitude and latitude coordinates and stored in the database for real-time matching. Among them, the road boundary is bounded by the center line of the main road. In the natural boundary, the river is divided by the center line of the river channel, and the mountain is referenced by the contour line. When the location of the work order is less than 5 meters away from the boundary, the overflow mechanism is triggered, and it is preferentially assigned to the adjacent grid with 50% fewer current work orders. The equipment binding adopts the one-to-one code pre-association rule. Each equipment is bound with coordinates during installation, and the system pre-calculates the grid ID it belongs to. When a work order is generated, it automatically matches the grid according to the equipment coordinates. For work orders without equipment, they are assigned through LBS positioning.

[0030] S2: Priority Sorting: Establish a three-level sorting rule to assign weights to the work orders, and combine the dependency relationship marks for dynamic weight superposition. The top 8 are selected according to the weight total score calculation formula for path planning.

[0031] In the three-level sorting rule, the first level is the urgency level, specifically 1h order > same-day order > 24h order, indicating that it needs to be processed within 1 hour > needs to be processed on the same day > needs to be processed within 24 hours. When the urgency level is 1h order, the weight is 10; when the urgency level is the same-day order, the weight is 5; when the urgency level is 24h order, the weight is 1. The second level is the work order type, including fault repair and preventive maintenance. When the work order type is fault repair, the weight is 3; when the work order type is preventive maintenance, the weight is 1. The third level is the dynamic time addition. For 1h orders, when the remaining time is less than 30 minutes, the priority is increased. When the remaining time ≤ 30 minutes, the weight +2; when the remaining time ≤ 15 minutes, the weight +5; when the superposition time ≤ 30 minutes, the addition is made, and the weight +7. The dependency relationship mark specifically means that when the target work order is not completed and will block other work orders, the target work order is marked as the critical path, and the weight of the critical path work order ×1.5.

[0032] The total weight score calculation formula is specifically expressed as: Total score = (Emergency level weight × 10) + (Work order type weight × 5) + (Overtime bonus × 3). The screening logic is: First, sort in descending order of the total score. When the scores are the same, sort in ascending order of the reception time. Finally, select the top 8 to enter the path planning.

[0033] Example: For work order A, the emergency level is a 1-hour order, the type is fault repair, the remaining time is 20 minutes, and the reception time is 09:00:01. The calculation process is (10 × 10) + (3 × 5) + (2 × 3) = 100 + 15 + 6, and the total score is 121; For work order B, the emergency level is a 1-hour order, the type is preventive maintenance, the remaining time is 50 minutes, and the reception time is 08:59:59. The calculation process is (10 × 10) + (1 × 5) + (0 × 3) = 100 + 5 + 0, and the total score is 105; For work order C, the emergency level is a same-day order, the type is fault repair, and the reception time is 09:00:00. The calculation process is (5 × 10) + (3 × 5) + (0 × 3) = 50 + 15 + 0, and the total score is 65; Result: Work order A (121 points) > Work order B (105 points) > Work order C (65 points).

[0034] S3: Data collection: Build a multi-source heterogeneous data fusion system, covering spatial topology data, dynamic efficiency data, and road network characteristic data, and realize the three-dimensional modeling of traffic dynamics, network structure, and equipment status through a multi-dimensional spatio-temporal data synchronization mechanism.

[0035] The spatial topology data includes grid transition cost, dynamic cross-grid distance, non-Euclidean geometry correction coefficient, and equipment cooperation distance; the dynamic efficiency data includes time window conflict coefficient, processing duration elasticity, traffic flow pulse coefficient, and dependency waiting duration; the road network characteristic data includes topological migration index, path fractal dimension, curvature energy integral, and self-similar fluctuation coefficient.

[0036] In the collection of the spatial topology data, the grid transition cost calls the map API according to the grid density label to obtain the straight-line distance between the grid center points. The transition cost of high-density grids is 1.2 times the straight-line distance, and the transition cost of low-density grids is 0.8 times the straight-line distance; the dynamic cross-grid distance obtains the actual path distance between the starting point and the ending point through the map API in real time, and calculates it by combining the actual path distance with the traffic coefficient; the non-Euclidean geometry correction coefficient is obtained by calculating the Euclidean distance between the starting point and the ending point and comparing it with the actual road network distance provided by the map API, and the non-Euclidean geometry correction coefficient is equal to the Euclidean distance divided by the actual road network distance; the equipment cooperation distance constructs a minimum spanning tree for the equipment coordinate set through the Prim algorithm and calculates the sum of the lengths of all the edges of the tree.

[0037] The grid transition cost reads the grid density label through the spatial database and calls the map API to obtain the straight-line distance d between the grid center points base , and then calculates the penalty value according to C hc = 1.2d base or 0.8d base . C hc represents the grid transition cost, and C hc = 1.2d base is the high-density grid transition cost, and C hc = 0.8d base is the low-density grid transition cost; the dynamic cross-grid distance is obtained in real time through the map API for the actual path distance d between the starting point (x s , y s ) and the ending point (x e , y e ). Combining with the traffic coefficient f api , it is calculated according to D t = d dyn (1 + f api ). D t represents the dynamic cross-grid distance, f dyn ∈ [0, 1], x t represents the longitude coordinate of the starting point, y s represents the latitude coordinate of the starting point, x s represents the longitude coordinate of the ending point, y e represents the latitude coordinate of the ending point, x e represents the longitude coordinate of the ending point, x s , y s , x e and y e specifically use decimal degrees in the WGS84 coordinate system; the non-Euclidean geometry correction coefficient is obtained by calculating the Euclidean distance , and comparing it with the road network distance dr of the map API to get λ ng = d e / d r . λ ng represents the non-Euclidean geometry correction coefficient; the device collaboration distance is calculated by constructing a minimum spanning tree for the device coordinate set P = {(x 1 , y 1 ),..., (x n , y n )} using Prim's algorithm, and the total length D co = ∑∥e k ∥, x n represents the abscissa of the device in the two-dimensional plane coordinate system, y n represents the ordinate of the device in the two-dimensional plane coordinate system, D co represents the device collaboration distance, and e kIs an edge in the minimum spanning tree.

[0038] In the collection of the dynamic performance data, the time window conflict coefficient is calculated by computing the overlapping duration ratio between the current work order time window and the set of historical time windows, specifically, the sum of the maximum end time minus the minimum start time of all overlapping periods, divided by the total duration of the current work order window; the processing duration elasticity is obtained by extracting the historical processing duration data of similar work orders and calculating the standard deviation of these durations; the traffic flow pulse coefficient is the absolute difference between the real-time flow and the reference flow divided by the reference flow; the dependency waiting duration is calculated by modeling the critical path with Petri nets and computing the difference between the maximum expected completion time of all tasks on the critical path and the current system time.

[0039] The time window conflict coefficient α t By calculating the overlapping duration ratio α between the current work order window [s c , e c and the set of historical windows W t =∑max(0, min(e c , e i ))−max(s c , s i )) / (e c −s c ), where e c represents the end time of the current work order, e i represents the end time of the scheduled work order, s c represents the start time of the current work order, and s i represents the start time of the scheduled work order; the processing duration elasticity σ d is calculated by extracting the historical durations T={t 1 ,..., t n} of similar work orders through SQL and then computing the standard deviation , where t k represents the processing duration of the k-th historical work order, and μ t represents the sample mean of the historical work order processing durations; the traffic flow pulse coefficient β f is obtained by comparing the real-time flow q now with the reference flow q base to get β f =∣q now −q base ∣ / q base ; the dependency waiting duration T wait is calculated by modeling the critical path CP with Petri nets and then computing T wait =max(c j )−t current , where c j represents the expected completion time of task j, and t current represents the system clock, and j∈CP.

[0040] In the collection of the road network characteristic data, the topological migration index compresses the GPS trajectory through the Douglas-Peucker algorithm, counts the number of times the direction angle changes by more than 45 degrees, and divides it by the total path length; the path fractal dimension is determined by covering the path with grids of different side lengths and calculating the slope of the logarithm of the required number of grids and the logarithm of the grid side length; the curvature energy integral generates the curvature function of the path through third-order spline interpolation and integrates the square of the curvature along the path length using the Simpson integral method; the self-similar fluctuation coefficient decomposes the path coordinate sequence by three-layer Haar wavelet and calculates the energy ratio of adjacent scale detail coefficients.

[0041] The topological migration index I td Ratio of the number of times of direction mutation > 45° to the total mileage after compressing the GPS trajectory using the Douglas-Peucker algorithm , Δθ represents the change in the direction angle of adjacent line segments, δ is the indicator function, L total represents the total path length, N seg represents the total number of trajectory line segments; the path fractal dimension D f Calculates the slope of logN(r) through the improved box-counting method , r 1 , r 2 represents different grid side lengths, N(r) represents the number of grids required to cover the path; the curvature energy integral E c Generates the curvature function κ(s) through third-order spline interpolation and calculates through the Simpson integral , κ(s) represents the curvature value of the path at the arc length position s, L is the total path length, Δs is the integration step size, κ 2i−2 is the curvature at the starting point of the i-th segment, κ 2i−1 is the midpoint curvature, κ 2i is the ending point curvature; the self-similar fluctuation coefficient γ s Decomposes the path coordinate sequence into 3 layers by Haar wavelet and calculates the adjacent scale energy ratio , E l represents the energy of the detail coefficients of the l-th layer.

[0042] When implementing S3, the PostGIS spatial engine is used to annotate the grid density, the traffic data is updated every 3 minutes through the map API, the GPS Beidou terminal collects coordinates at a frequency of 1Hz, the OR-Tools library is used for multi-objective path optimization, Apache Flink processes real-time data streams, the CGAL library calculates geometric features, the Hungarian algorithm allocates task resources, the anomaly monitoring uses a 5-minute sliding window, and the curvature calculation error < 0.1 rad / km.

[0043] S4: Index Calculation: Based on the data collected in S3, a mathematical model is established to calculate the indices of the spatial topology comprehensive index, dynamic efficiency index, and path complexity index.

[0044] The spatial topology comprehensive index is obtained by dividing the product of the grid transition cost and the dynamic cross-grid distance by the non-Euclidean geometry correction coefficient, and then adding the square root of the product of the device collaboration distance and the path efficiency weight coefficient.

[0045] The spatial topology comprehensive index is specifically expressed as: , where S represents the spatial topology comprehensive index and η represents the path efficiency weight coefficient.

[0046] The spatial topology comprehensive index realizes progressive analysis through the balance of spatial efficiency and resource collaboration. First, multiply the grid transition cost Ch and the dynamic distance Dd to reflect the double influence of the actual movement cost by density penalty and road conditions, and then divide by the non-Euclidean correction coefficient λ n to eliminate the straight-line distance error. The device collaboration distance Dc is used as an independent term to reflect the cluster operation efficiency, and the contribution ratio of the two is adjusted by the weight coefficient η. The determination of the weight coefficient η adopts regression analysis of historical work order data: select 1000 completed work orders, use the actual fuel consumption / time as the target variable, and obtain η = 0.68 by least squares fitting.

[0047] The dynamic efficiency index is the product of the time window conflict coefficient and the processing duration elasticity, plus the sum of the traffic flow pulse coefficient and the logarithm of the dependency waiting duration, and then multiplied by the smoothing constant.

[0048] The dynamic efficiency index is specifically expressed as: , where T represents the dynamic efficiency index and ϕ represents the smoothing constant.

[0049] The dynamic efficiency index is used to characterize the composite risk in the time dimension. The first term α t σ d represents the coupling effect of time window conflict and processing duration uncertainty, reflecting the product relationship of the planned failure rate. The second term introduces the combination of the traffic pulse coefficient β f and the logarithm of the waiting time term, where ln(T w +ϕ) non-linearly maps the waiting time to a risk value, and ϕ = 0.01 is used to prevent mathematical anomalies when the waiting time is zero. The formula structure is verified by Monte Carlo simulation: generate 100,000 groups of parameter combinations, compare with the actual delay data, and determine that the logarithmic transformation reduces the error by more than 30% compared with the linear form. The dimensional consistency between coefficients is achieved through Z-score standardization to eliminate the variable scale difference.

[0050] The path complexity index is the cube root of the geometric mean of the topological migration index and the path fractal dimension, plus the product of the curvature energy integral and the self-similarity fluctuation coefficient, multiplied by the power-law adjustment coefficient, and added with the curvature energy smoothing term.

[0051] The path complexity index is specifically expressed as: , where P represents the path complexity index, and k 1 , k 2 is the power-law adjustment coefficient, and ψ represents the curvature energy smoothing term.

[0052] The path complexity index quantifies the path complexity through multi-scale fusion of geometric features, uses geometric mean to eliminate dimensional differences, compresses the value range to a reasonable range through cube root, and introduces the power-law coefficient k t for the topological migration index I f and the fractal dimension D 1 , k 2 , and determines the weights through factor analysis: six types of typical paths such as urban roads and highways are selected, and the principal component analysis method is used to obtain k 2 = 1.3k 2 = 1.3, reflecting that the contribution degree of the fractal dimension is 1.86 times that of the migration index. For the curvature energy integral E c , ψ = 0.1 is added to prevent calculation failure in zero-curvature road sections, and its value is set according to twice the sensor noise level.

[0053] S5: Path planning: Establish a comprehensive scoring formula based on the three indicators calculated in S4, adjust the comprehensive scoring formula using dynamic weights, and finally set the path planning logic according to the comprehensive score.

[0054] The comprehensive scoring formula is obtained by multiplying the spatial topology weight, dynamic efficiency weight, and path complexity weight by their corresponding indices respectively, then multiplying by the combination of the emergency work order gain coefficient and the exponential decay factor, and finally through normalization processing.

[0055] The comprehensive scoring formula is specifically expressed as: , where Z(t) represents the comprehensive score, w s (t) represents the spatial topology weight, w t (t) represents the dynamic efficiency weight, w p (t) represents the path complexity weight, γ represents the emergency work order gain coefficient, τ represents the non-linear decay factor, t 0 represents the work order generation time, e is a constant, t is the time, and the path planning logic is: arrange in descending order of Z(t), when Z(t) is the same, arrange in descending order of S, and when S is the same, arrange in ascending order of the work order reception time.

[0056] The w s (t), wt (t) and w p (t) is the dynamic weight. The weight distribution of [ws, wt, wp] is [0.3, 0.6, 0.1] during the morning peak period from 7:00 to 9:00, [0.4, 0.3, 0.3] during the flat peak period from 10:00 to 16:00, [0.2, 0.5, 0.3] during the evening peak period from 17:00 to 19:00, and [0.5, 0.2, 0.3] during the night period from 20:00 to 6:00.

[0057] The comprehensive scoring formula assigns basic weights according to the characteristics of different time periods. It emphasizes time efficiency during the morning peak and space efficiency at night. The weight values are determined through regression analysis of the historical work order completion rate. The denominator is normalized to eliminate the influence of the absolute value of the weight, ensuring that the sum of weights in different time periods is consistent with the scoring scale. The exponential term 1 / (1 + γe −τ(t−t0) ) realizes the penalty for work order retention, automatically improves the priority over time, and is calibrated according to the measured data. When the coefficient γ = 0.2, the score of a work order retained for 2 hours increases by 19%. When Z(t) is the same, it is sorted according to the original spatial index S to ensure repeatability.

[0058] Example: Time period: 8:30 in the morning peak (applicable weight [0.3, 0.6, 0.1]) Work order A: S = 82, T = 75, P = 68, generation time: 8:00; Work order B: S = 78, T = 88, P = 72, generation time: 8:15; Work order C: S = 95, T = 65, P = 81, generation time: 8:20; Calculate the comprehensive score: ; ; ; Result: B(91.0) → A(87.3) → C(80.7).

[0059] Reference Figure 4 , the present invention operates through the collaborative operation of a geographic grid management module, a work order scheduling core module, a data acquisition and adaptation module, a real-time computing engine module, and a resource collaboration module. Specifically: Geographic grid management module: Realizes dynamic grid division based on the GIS urban construction coordinate system, maintains grid IDs, boundary coordinates, and density labels through the PostGIS spatial engine, and triggers dynamic reconstruction monthly according to the work order heat map; Work order scheduling core module: integrates three-level sorting rules and multi-objective path planning algorithms, receives real-time coordinate input from the LBS positioning system, and generates task sequences with time window constraints; Data collection adaptation module: connects to multi-source data such as GPS / Beidou terminals and map APIs to complete the standardization of spatial topological relationships; Real-time computing engine module: uses Apache Flink to process data streams, and combines OR-Tools and CGAL libraries to implement dynamic distance calculation and geometric feature analysis; Resource collaboration module: Tasks are assigned through the Hungarian algorithm, and a collaborative network is established based on the equipment coordinate set generated by the Prim algorithm to achieve optimal matching between work orders and equipment.

[0060] The geographic grid management module provides a basic spatial reference system to each module, the data acquisition adaptation module pushes real-time data to the real-time computing engine for indicator processing, and the work order scheduling core module comprehensively receives geographic grid data and real-time computing indicators, generates a work order allocation plan, and then triggers the resource collaboration module to execute.

[0061] The present invention first performs dynamic geographic grid division, adjusts the grid granularity and sets boundary anchor points according to the real-time work order density, and realizes intelligent matching of equipment and grid through pre-binding rules; then starts a three-level priority sorting mechanism, performs weighted scoring based on the urgency, work order type and remaining time, and screens high-priority work orders into the path queue; then synchronously collects spatial topological data, real-time traffic efficiency indicators and path geometric characteristics, and integrates multi-source data to build a calculation model; based on a preset algorithm, a spatial efficiency index, a time risk index and a path complexity index are generated respectively; finally, a weight coefficient is dynamically allocated in combination with the time period characteristics, the work order execution priority is calculated through a nonlinear scoring model, and the path planning engine is driven to generate the optimal route, so as to realize grid resource scheduling and intelligent dispatching that is adaptive to traffic conditions. The whole process adopts a streaming computing framework to ensure data real-time, and a dynamic weight mechanism is used to balance the multi-dimensional goals of efficiency, cost and path reliability.

Claims

1. A work order dispatching and path planning method based on multi-factor intelligent matching, characterized in that: include: S1: Grid division: Based on GIS urban construction coordinates and equipment positioning data, the work order is automatically associated with the preset geographic grid, including geographic hierarchy division, dynamic density adjustment, boundary anchor point definition and equipment binding; S2: Priority sorting: Establish a three-level sorting rule to assign weights to work orders, and dynamically superimpose weights based on dependency tags. Select the top 8 for path planning based on the weighted total score calculation formula; S3: Data collection: Build a multi-source heterogeneous data fusion system, covering spatial topology data, dynamic efficiency data and road network characteristic data, and realize three-dimensional modeling of traffic dynamics, network structure and equipment status through a multi-dimensional spatiotemporal data synchronization mechanism; S4: Index calculation: Based on the data collected in S3, a mathematical model is established to calculate the spatial topology comprehensive index, dynamic efficiency index and path complexity index; S5: Path planning: Establish a comprehensive scoring formula based on the three indicators calculated in S4, use dynamic weights to adjust the comprehensive scoring formula, and finally set the path planning logic based on the comprehensive score.

2. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 1 is characterized in that: The geographical level division is based on administrative divisions as the basic unit, and the GIS urban construction coordinate grid is superimposed on the administrative grid, specifically a 500m×500m square grid. Each grid corresponds to a unique ID in the database, recording the latitude and longitude of the center point and the boundary coordinate set; The dynamic density adjustment dynamically adjusts the grid according to the work order density threshold, determines the area with >50 work orders / month as a high-density area, automatically reduces the grid to 200m×200m, determines the area with <10 work orders / month as a low-density area, merges the grid into a large grid of 1km×1km, recalculates the grid size based on the work order heat map every month, and automatically updates the boundary through the GIS system; The boundary anchor point definition uses road boundaries and natural boundaries as the physical boundaries of the grid, marks the turning points by longitude and latitude coordinates, and stores them in the database for real-time matching. The road boundary is bounded by the centerline of the main road, the river in the natural boundary is divided by the centerline of the river channel, and the mountain is referenced by the contour line. When the work order location is less than 5 meters from the boundary, the overflow mechanism is triggered and the work order is preferentially distributed to the adjacent grid with 50% fewer current work orders. The device binding adopts the one-object-one-code pre-association rule. The coordinates of each device are bound when it is installed. The system pre-calculates the grid ID to which it belongs. When a work order is generated, the grid is automatically matched according to the device coordinates. If there is no device work order, it is allocated through LBS positioning.

3. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 1 is characterized in that: In the three-level sorting rule, the first level is the urgency, specifically 1h order > same-day order > 24h order, indicating that it needs to be processed within 1 hour > needs to be processed on the same day > needs to be processed within 24 hours. When the urgency is 1h order, the weight is 10, when the urgency is same-day order, the weight is 5, and when the urgency is 24h order, the weight is 1; the second level is the work order type, including fault repair and preventive maintenance. When the work order type is fault repair, the weight is 3, and when the work order type is preventive maintenance, the weight is 1; the third level is dynamic time bonus. For 1h orders, the priority is increased when the remaining time is less than 30 minutes, the weight is +2 when the remaining time is ≤30 minutes, the weight is +5 when the remaining time is ≤15 minutes, and the weight is +7 when the superposition time is ≤30 minutes; the dependency marking is specifically expressed as: when the target work order is not completed and will block other work orders, the target work order is marked as a critical path, and the weight of the critical path work order is ×1.

5.

4. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 1 is characterized in that: The weighted total score calculation formula is specifically expressed as: total score = (urgency weight × 10) + (work order type weight × 5) + (overtime bonus × 3), and the screening logic is: first sort in descending order by total score, and for the same time score, sort in ascending order by receiving time, and finally take the top 8 to enter the path planning.

5. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 1 is characterized in that: The spatial topological data include grid transition cost, dynamic cross-grid distance, non-Euclidean geometry correction coefficient and equipment coordination distance; the dynamic performance data include time window conflict coefficient, processing time elasticity, traffic flow pulse coefficient and dependency waiting time; the road network characteristic data include topological detour index, path fractal dimension, curvature energy integral and self-similar fluctuation coefficient.

6. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 5 is characterized in that: In the collection of the spatial topological data, the grid transition cost calls the map API according to the grid density label to obtain the straight-line distance between the grid center points. The transition cost of the high-density grid is 1.2 times the straight-line distance, and the transition cost of the low-density grid is 0.8 times the straight-line distance. The dynamic cross-grid distance obtains the actual path distance between the starting point and the end point in real time through the map API, and calculates the actual path distance in combination with the traffic coefficient. The non-Euclidean geometry correction coefficient is obtained by calculating the Euclidean distance between the starting point and the end point, and comparing it with the actual road network distance provided by the map API. The non-Euclidean geometry correction coefficient is equal to the Euclidean distance divided by the actual road network distance. The device collaborative distance constructs a minimum spanning tree for the device coordinate set through the Prim algorithm, and calculates the sum of the lengths of all edges of the tree.

7. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 5 is characterized in that: In the collection of dynamic performance data, the time window conflict coefficient is calculated by calculating the overlapping time ratio of the current work order time window and the historical time window set, specifically the sum of the maximum end time of all overlapping time periods minus the minimum start time, divided by the total time of the current work order window; the processing time elasticity is calculated by extracting the historical processing time data of similar work orders and calculating the standard deviation of these time lengths; The traffic flow pulse coefficient is the absolute difference between the real-time flow and the benchmark flow divided by the benchmark flow; The dependency waiting time is calculated by modeling the critical path through Petri nets and calculating the difference between the maximum estimated completion time of all tasks on the critical path and the current system time.

8. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 5 is characterized in that: In the collection of the road network characteristic data, the topological tortuosity index is obtained by compressing the GPS trajectory through the Douglas-Peucker algorithm, counting the number of times the azimuth angle changes more than 45 degrees, and dividing it by the total path length; the path fractal dimension is determined by covering the path with grids of different side lengths, calculating the slope of the logarithm of the required number of grids and the logarithm of the grid side length; the curvature energy integral is obtained by generating the curvature function of the path through third-order spline interpolation, and integrating the square of the curvature along the path length using the Simpson integral method; the self-similar fluctuation coefficient is obtained by performing a three-layer Haar wavelet decomposition on the path coordinate sequence and calculating the energy ratio of the detail coefficients of adjacent scales.

9. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 1 is characterized in that: The spatial topology comprehensive index is the product of the grid transition cost and the dynamic cross-grid distance divided by the non-Euclidean geometry correction coefficient, plus the device coordination distance multiplied by the square root of the path efficiency weight coefficient; The dynamic performance index is the product of the time window conflict coefficient and the processing time elasticity, plus the sum of the traffic flow pulse coefficient and the logarithm of the dependency waiting time, multiplied by a smoothing constant; The path complexity index is obtained by adding the cube root of the geometric mean of the topological tortuosity index and the path fractal dimension, multiplying the product of the curvature energy integral and the self-similar fluctuation coefficient, multiplying it by the power law adjustment coefficient, and adding the curvature energy smoothing term.

10. The work order dispatching and path planning method based on multi-factor intelligent matching according to claim 1 is characterized in that: The comprehensive scoring formula is obtained by multiplying the spatial topology weight, the dynamic efficiency weight and the path complexity weight by the corresponding index respectively, then multiplying by a combination of the emergency work order gain coefficient and the exponential decay factor, and finally by normalization.

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