Heavy truck charging gap quantification and site recommendation method and device

Abstract

The application discloses a heavy truck charging gap quantification and site selection recommendation method and device. The server method comprises the following steps: performing grid division according to candidate site coordinates of a target candidate point to obtain a grid analysis area of the target candidate point; performing spatial grid-by-grid cross matching on heavy truck historical trajectory data and the grid analysis area to obtain basic demand indexes of each grid unit; calculating demand density of each grid unit based on the basic demand indexes; calculating total supply coverage values of each grid unit according to a historical built charging station set within the grid analysis area and a preset expansion boundary thereof; calculating a charging gap score of the target candidate point based on the demand density and the total supply coverage values; and generating a candidate point recommendation priority list in descending order of the charging gap score. Therefore, the application provides scientific, objective and verifiable data-driven site selection decision support for charging operators.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method and apparatus for quantifying and recommending charging gaps for heavy-duty trucks. Background Technology

[0002] With the accelerated promotion of electric heavy-duty trucks, the site selection decision for charging infrastructure has become a crucial step. A typical application scenario is that charging operators need to select the optimal site for charging stations from multiple candidate locations within a certain area, prioritizing areas with high demand for heavy-duty truck parking but insufficient coverage of existing charging stations within a limited budget.

[0003] The current site selection evaluation method is based on multi-criteria decision analysis. This method first constructs an indicator system that includes dimensions such as traffic flow, land cost, and power grid access. Then, experts score each candidate site on each dimension, and the scores are weighted and summed using methods such as the analytic hierarchy process or TOPSIS. The sites are then recommended based on their comprehensive scores.

[0004] However, this technology has several shortcomings: First, it relies on subjective weighting, leading to significant differences in scores among different experts and a lack of objective quantitative basis. Second, it cannot answer the core question of "how much actual demand is covered by existing charging facilities, and how many vehicles are parked but have no available charging stations," because it lacks a means to accurately match heavy truck trajectory behavior data with the spatial distribution of charging facilities. Third, the evaluation results are decoupled from the spatial location of candidate points, ignoring the supply and demand spillover effect between adjacent candidate points, resulting in inflated scores. Fourth, it lacks a feedback calibration mechanism based on actual operational data of existing charging stations, making closed-loop optimization impossible. Summary of the Invention

[0005] This application provides a method and apparatus for quantifying and recommending charging gaps for heavy-duty trucks. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] In a first aspect, embodiments of this application provide a method for quantifying and recommending charging gaps for heavy-duty trucks, applied to a server, the method comprising: Receive a site selection recommendation request sent by the client. The site selection recommendation request carries the coordinates of at least one candidate site. The grid is divided according to the candidate site coordinates to obtain the gridded analysis area of ​​the target candidate point. Each grid cell in the gridded analysis area has a unique index identifier and latitude and longitude coordinates. Acquire historical trajectory data of heavy trucks within a preset time period within the gridded analysis area, and perform spatial grid-by-grid cross-matching between the historical trajectory data of heavy trucks and the gridded analysis area to obtain the basic requirement indicators for each grid unit; Calculate the demand density for each grid cell based on the basic demand indicators; Extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and calculate the total supply coverage value for each grid cell based on the set of historically built charging stations; Based on demand density and total supply coverage, calculate the charging gap score of the target candidate sites; generate a priority list of candidate sites by sorting them in descending order of charging gap score.

[0007] Secondly, embodiments of this application provide a device for quantifying and recommending charging gaps for heavy-duty trucks, the device comprising: The request receiving module is used to receive the site selection recommendation request sent by the client. The site selection recommendation request carries the candidate site selection coordinates of at least one target candidate point. The grid division module is used to divide the grid according to the candidate site coordinates to obtain the gridded analysis area of ​​the target candidate point. Each grid cell in the gridded analysis area has a unique index identifier and latitude and longitude coordinates. The grid cross-matching module is used to acquire the historical trajectory data of heavy trucks within a preset time period in the gridded analysis area, and to perform spatial grid-by-grid cross-matching of the historical trajectory data of heavy trucks with the gridded analysis area to obtain the basic requirement indicators of each grid unit. The demand density calculation module is used to calculate the demand density of each grid cell based on the basic demand index. The total supply coverage value calculation module is used to extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and to calculate the total supply coverage value of each grid cell based on the set of historically built charging stations. The ranking and recommendation module is used to calculate the charging gap score of the target candidate points based on the demand density and total supply coverage; and generate a priority list of candidate point recommendations by sorting them in descending order of the charging gap score.

[0008] The technical solutions provided in this application embodiment may include the following beneficial effects: In this embodiment, on the one hand, by receiving candidate site coordinates sent by the client and performing standardized grid division, a unified spatial computing primitive is established. This allows for the acquisition of historical heavy-duty truck trajectory data and spatial grid-by-grid cross-matching with the gridded analysis area, achieving refined statistics and quantification of heavy-duty truck parking demand within each grid unit. Based on this, the historical set of existing charging stations is further extracted, and the total supply coverage value for each grid unit is calculated. The demand density and supply coverage value are cross-compared to calculate the charging gap score for the target candidate point. Since this process is entirely driven by objective trajectory data and charging station spatial data, it does not rely on subjective expert scoring, completely eliminating scoring differences caused by human factors and ensuring the repeatability and objectivity of the evaluation results. Simultaneously, by comparing the demand density and supply coverage value grid-by-grid, it is possible to determine how much actual demand the existing charging facilities cover and how many vehicles are parked without available stations.

[0009] On the other hand, a gridded analysis area is constructed with the candidate site coordinates as the center. When calculating the charging gap score, the charging gap index of all grid units within the preset range of the candidate point is summarized, so that the evaluation results are coupled with the spatial location of the candidate points. This can effectively identify the supply and demand spillover effect between adjacent candidate points and avoid the inflated score caused by spatial decoupling. By generating a recommendation priority list in descending order of charging gap score and combining it with the subsequent adaptive calibration mechanism, the site recommendation results can be continuously optimized with the accumulation of operational data, forming a complete closed loop from data input, quantitative analysis to result calibration.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0011] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0012] Figure 1 This is a flowchart illustrating a method for quantifying charging gaps and recommending locations for heavy-duty trucks, provided in an embodiment of this application. Figure 2 This is a flowchart of a method for quantifying the charging gap of heavy-duty trucks and recommending locations, provided in this application. Figure 3 This is a schematic diagram of the structure of a heavy-duty truck charging gap quantification and site selection recommendation device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] The following description and accompanying drawings fully illustrate specific embodiments of this application to enable those skilled in the art to practice them.

[0014] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0015] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0016] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0017] This application provides a method and apparatus for quantifying and recommending charging gaps for heavy-duty trucks, addressing the problems existing in the aforementioned related technologies. In this application's embodiments, on one hand, by receiving candidate site coordinates sent by a client and performing standardized grid division, a unified spatial computing primitive is established. This allows for the acquisition of historical heavy-duty truck trajectory data and spatial grid-by-grid cross-matching with the gridded analysis area, achieving refined statistics and quantification of heavy-duty truck parking demand within each grid unit. Based on this, a set of historically built charging stations is further extracted, and the total supply coverage value for each grid unit is calculated. The demand density and supply coverage value are cross-compared to calculate the charging gap score for the target candidate point. Since this process is entirely driven by objective trajectory data and charging station spatial data, it does not rely on subjective expert scoring, completely eliminating scoring differences caused by human factors and ensuring the repeatability and objectivity of the evaluation results. Simultaneously, by comparing the demand density and supply coverage value grid-by-grid, it is possible to determine how much actual demand is covered by existing charging facilities and how many vehicles are parked without available stations.

[0018] On the other hand, a gridded analysis area is constructed with the candidate site coordinates as the center. When calculating the charging gap score, the charging gap index of all grid units within the preset range of the candidate point is summarized, so that the evaluation result is coupled with the spatial location of the candidate point. This can effectively identify the supply and demand spillover effect between adjacent candidate points and avoid the inflated score caused by spatial decoupling. By generating a recommendation priority list in descending order of charging gap score and combining it with the subsequent adaptive calibration mechanism, the site recommendation result can be continuously optimized with the accumulation of operational data, forming a complete closed loop from data input, quantitative analysis to result calibration. The following is a detailed description using an exemplary embodiment.

[0019] The following will be combined with the appendix Figure 1 - Appendix Figure 2 This application provides a detailed description of the method for quantifying and recommending charging gaps for heavy-duty trucks based on embodiments of the present application. This method can be implemented using a computer program and can run on a heavy-duty truck charging gap quantification and site selection recommendation device based on the von Neumann architecture. This computer program can be integrated into the application or run as a standalone utility application.

[0020] Please see Figure 1 This document presents a flowchart illustrating a method for quantifying charging gaps and recommending locations for heavy-duty trucks, applicable to the server side. For example... Figure 1 As shown, the method in this application embodiment may include the following steps: S101, Receive a site selection recommendation request sent by the client, the site selection recommendation request carrying the candidate site selection coordinates of at least one target candidate point; The site selection recommendation request is a business call instruction initiated by the client to the server to request analysis and ranking of proposed locations for electric heavy-duty truck charging stations. Target candidate points are one or more geographical entities that the user intends to evaluate as suitable for constructing a new electric heavy-duty truck charging station. Candidate site coordinates are the spatial representation of the target candidate points on the Earth's ellipsoid, supporting latitude and longitude coordinate formats (such as WGS). (latitude and longitude values ​​in the 84 coordinate system) or address text (standardized latitude and longitude obtained after address parsing).

[0021] In one possible implementation, the user inputs the address text of three proposed sites via a web or mobile interface (e.g., "vacant land north of the east gate of XX City Logistics Park A", "plot of land next to the exit ramp of XX Expressway", "northwest corner of the intersection of XX Provincial Highway and G104 National Highway"). The client encapsulates these three addresses into a JSON-formatted site selection recommendation request message and sends it to the server. The message carries three candidate site coordinate fields, `candidate_points`, which contain three address strings. Upon receiving the site selection recommendation request, the server parses the message, extracts the `candidate_points` array, and calls a geocoding service to convert each address string in the array to WGS. The latitude and longitude coordinates in the 84 coordinate system (e.g., [117.1832, 39.0456]) are used as the standardized spatial input for the target candidate points in subsequent steps.

[0022] S102, grid division is performed based on the candidate site selection coordinates to obtain the gridded analysis area of ​​the target candidate point. Each grid cell in the gridded analysis area has a unique index identifier and latitude and longitude coordinates. In some embodiments of this application, the specific process of dividing the candidate site coordinates into grids to obtain the gridded analysis region of the target candidate point includes: performing geocoding on the candidate site coordinates to obtain the standardized geographic coordinates of the target candidate point; constructing a square analysis region with a preset side length using the standardized geographic coordinates of the target candidate point as the geometric center to obtain the square analysis region of the target candidate point; dividing the square analysis region into uniform two-dimensional grids according to the preset grid side length to obtain a two-dimensional regular grid set of the target candidate point; assigning a unique index identifier to each grid cell in the two-dimensional regular grid set of the target candidate point, and recording the latitude and longitude coordinates of the southwest corner vertex of each grid cell to obtain the gridded analysis region of the target candidate point.

[0023] In one possible implementation, geocoding is performed on the candidate location coordinates of each target candidate point to convert them into standardized geographic coordinates, and the administrative division information to which the candidate point belongs is obtained through reverse parsing. Using the standardized geographic coordinates as the geometric center, a square analysis region with a side length of L is constructed by extending outwards by a preset distance along both the longitude and latitude directions; where L is a preset parameter, with a default value of 20 kilometers. The square analysis region is then uniformly divided according to a preset grid side length g, resulting in a two-dimensional regular grid set G={g1, g2, …, g} of M rows × N columns. n}, which is the gridded analysis area; where g is a preset parameter with a default value of 500 meters, and M and N are determined by rounding up the ratio of L to g. Each grid cell is assigned a unique index identifier, and the latitude and longitude coordinates of its southwest corner vertex are recorded to form a structured spatial computing primitive, which is used in subsequent steps to perform trajectory point statistics and quantitative calculation of charging station service capacity at the grid scale.

[0024] S103, acquire the historical trajectory data of heavy trucks within a preset time period in the gridded analysis area, and perform spatial grid-by-grid cross-matching between the historical trajectory data of heavy trucks and the gridded analysis area to obtain the basic requirement indicators of each grid unit; In some embodiments of this application, the specific process of spatially cross-matching the historical trajectory data of heavy trucks with the gridded analysis area to obtain the basic demand indicators of each grid cell includes: identifying the historical trajectory data of heavy trucks according to preset stopping judgment conditions to obtain a set of stopping events, and marking each stopping event in the set with stopping representative coordinates, energy type and stopping duration to obtain stopping event information for each stopping event; allocating the stopping event information to the grid cells of the gridded analysis area of ​​the target candidate point according to the stopping representative coordinates of each stopping event and the latitude and longitude coordinates of each grid cell; and calculating the basic demand indicators of each grid cell based on the stopping event information allocated to each grid cell.

[0025] In one possible implementation, historical trajectory data of heavy trucks with a duration of nearly M days (default M=30 days) are extracted from the gridded analysis area; the trajectory data is then identified based on a preset stopping determination rule (i.e., speed is zero and duration is greater than or equal to 15 minutes) to obtain a set of stopping events. , and for Each dwell event is recorded, including its dwelling coordinates (lat, lng), energy type, and dwell duration. Secondly, based on the gridded analysis area... The spatial boundary will map each identified dwelling event to the corresponding grid cell into which it falls. In the middle; for each grid cell The three basic demand indicators are calculated separately: : The total number of electric heavy truck parking incidents that fall into this grid; The number of deduplicated electric heavy-duty trucks that fall into this grid; : The average dwell time of all dwelling events within this grid.

[0026] Finally, the above three basic requirement indicators will be used as the basic requirement indicators for each grid cell.

[0027] S104, based on the basic demand index, calculates the demand density of each grid cell; The basic demand indicators include the total number of electric heavy-duty truck parking events falling into the grid, the number of de-loaded electric heavy-duty trucks falling into the grid, and the average parking duration of all parking events within the grid.

[0028] In some embodiments of this application, the specific process of calculating the demand density of each grid cell based on the basic demand indicators includes: selecting the maximum value of different indicators from the basic demand indicators of each grid cell; and calculating the demand density of each grid cell based on the maximum value of different indicators and each demand indicator in the basic demand indicators of each grid cell.

[0029] Among them, demand density The calculation formula is:

[0030] in, , , This represents the maximum value of different indices across all grid cells. , , The default weights for the demand components. To fall into the grid cell The total number of electric heavy truck parking incidents To fall into the grid cell The number of electric heavy-duty trucks to be de-duplicated. It is a grid cell The average dwell time of an internal dwell event.

[0031] Specifically, =0.4、 =0.35、 =0.25, .

[0032] S105, extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and calculate the total supply coverage value for each grid cell based on the set of historically built charging stations. In some embodiments of this application, the specific process of calculating the total supply coverage value of each grid unit based on the historical set of built charging stations includes: reading the attribute data of each historically built charging station in the historical set of built charging stations, the attribute data including the centroid coordinates of the fence, charging power, and effective service radius; using a preset two-dimensional field effect attenuation model and attribute data, calculating the service coverage value of each historically built charging station to each grid unit; and selecting the service coverage values ​​of all charging stations received by each grid unit from the service coverage values ​​of each historically built charging station to each grid unit and superimposing them to obtain the total supply coverage value of each grid unit.

[0033] In one possible implementation, a set of all existing charging stations located within the analysis area and its surrounding extended area (with the extended boundary being a 20km buffer zone extending beyond the analysis area boundary) is extracted from the charging station database. Read each charging station The attribute data includes: fence centroid coordinates Charging power (kW), and effective service radius (Set according to the charging station's power level, such as 20km for DC fast charging stations and 10km for regular charging stations). For each charging station... A two-dimensional field-effect attenuation model is used to calculate its effect on each grid cell. Service coverage .

[0034] Specifically, the model expression for the preset two-dimensional field effect decay model is as follows:

[0035] in, It is a historical charging station For grid cells The service coverage value, , For each historically built charging station Normalized power, It is a historical charging station The actual charging power, This is the highest charging power value among all existing charging stations. For grid cells From the center of gravity to the historical charging stations The spherical distance from the center of mass, It is a historical charging station The effective service radius It is a negative exponential decay function, simulating the natural decay of charging service with distance; Specifically, total supply coverage The calculation expression is:

[0036] S106. Based on demand density and total supply coverage, calculate the charging gap score of the target candidate points; generate a priority list of candidate points by sorting them in descending order of charging gap score.

[0037] In some embodiments of this application, the specific process of calculating the charging gap score of the target candidate point based on demand density and total supply coverage value includes: calculating and preset a supply coverage threshold based on the total supply coverage value of each grid cell; calculating the charging gap index of each grid cell based on demand density, total supply coverage value and supply coverage threshold; summing the charging gap indices of each grid cell within a preset range of the target candidate point and performing a weighted summation with a preset distance weighting function to obtain the charging gap score of the target candidate point.

[0038] In one possible implementation, each grid cell is calculated. Demand density Each grid cell Total supply coverage Then; simultaneously, based on all grid cells Data, calculation, and preset supply coverage thresholds. (Defaults to all grids) The top 25th percentile of the value is used to define the critical state of whether the supply is sufficient or not. For each grid cell... The supply-demand matching degree is calculated, which is characterized by the ratio of total supply coverage to a threshold. Specifically, it employs... The calculation is performed in the form of a formula, and the result characterizes the saturation of the current supply state of the grid relative to the sufficient critical state. Each grid cell can be calculated based on demand density and supply-demand matching degree. Final charging gap index .

[0039] Specifically, the charging gap index of each grid cell The calculation formula is:

[0040] in, For each grid cell Demand density, Each grid cell Total supply coverage value, This is the supply coverage threshold; Among them, when When the value is high, it indicates that there is a strong demand for heavy truck parking within that grid cell. (High) and the existing charging station coverage is insufficient. (Low), meaning a severe charging gap; when When the value is low, it indicates that either the demand within the grid cell is low or it is already fully covered by existing charging stations, meaning that the charging gap is slight or non-existent.

[0041] In one possible implementation, for each target candidate point For the evaluation center, a local influence area is defined around it (default radius is 5km); the charging gap index of all grid cells within this local area is extracted. And use distance weighting function The charging gap score of the target candidate point is calculated by weighted summation. .

[0042] Specifically, target candidate points Charging gap score The calculation formula is:

[0043]

[0044] in, It is the first One target candidate point, Distance weighting function, used to measure grid cells The gap for the target candidate point The degree of contribution of the rating, It is a limiting condition, indicating that it applies only to the target candidate point. The summation calculation is performed on grid cells with a distance of no more than 5 kilometers. It is the target candidate point With grid cells The spherical distance between the centers of mass, It is the distance attenuation half-range. It is the first Each grid cell.

[0045] In this embodiment of the application, the area proportion statistics of different gap levels within a preset range around each target candidate point are output simultaneously. The statistical information includes the area proportion of each of the severe gap area, the mild gap area, the basically balanced area and the oversupply area.

[0046] In one possible implementation, based on the calculated candidate points The numerical values ​​are used to sort all candidate points in descending order, generating a final priority list of candidate points. At the same time, the percentage of area covered by different gap levels (severe gap, mild gap, basic balance, and oversupply) within a 5km radius of each candidate point is calculated and output as a multi-dimensional auxiliary evaluation basis for site selection decision.

[0047] Furthermore, it is necessary to classify the charging gap level based on the charging gap index. This process specifically includes: classifying the gap level of each grid unit according to the charging gap index of each grid unit and a preset gap level judgment threshold; the gap level is one of the following: severe gap area, mild gap area, basically balanced area, and oversupply area; the severe gap area is used to indicate that the demand for heavy truck parking in the grid unit is strong and the existing charging station coverage is seriously insufficient; the mild gap area is used to indicate that there is demand for heavy truck parking in the grid unit but the existing coverage is still insufficient; the basically balanced area is used to indicate that the supply and demand relationship in the grid unit is basically balanced. The "supply surplus area" is used to indicate that the demand for heavy truck parking within the grid unit is weak or is already fully covered by existing facilities. If the gap level of each grid unit is a severe gap area, it is determined to be an area where new charging facilities are recommended as a priority. Alternatively, if the gap level of each grid unit is a slight gap area, it is determined to be a candidate area for investigation and evaluation. Alternatively, if the gap level of each grid unit is a basically balanced area, it is determined to be an area where new charging facilities are not currently needed. Alternatively, if the gap level of each grid unit is a supply surplus area, it is determined to be an area where new charging facilities are not recommended.

[0048] In one possible implementation, the numerical distribution characteristics of the charge gap index (CGI) of each grid cell are used to set four progressive preset gap level determination thresholds, dividing all grid cells in the analysis area into four level intervals. The specific threshold settings are as follows: Severe Gap Area: The criteria for determination are as follows ; Mild gap area: The criteria for determination are as follows ; Basic equilibrium zone: The determination criteria are as follows ; Areas with oversupply: The criteria for determination are... .

[0049] For example, grids falling into severe shortage areas physically mean extremely high demand with almost no effective coverage, and are therefore considered priority locations for new charging facility construction. Grids falling into mild shortage areas physically mean some demand exists but existing coverage is insufficient, and are considered potential areas for assessment. Grids falling into basic balance areas physically mean a basic match between supply and demand, and are therefore areas where new charging facilities are not currently necessary. Grids falling into oversupply areas physically mean weak demand or sufficient coverage by existing facilities, and are therefore areas where new charging facilities are not recommended.

[0050] Specifically, the gap level of each grid unit is adaptively adjusted according to the following steps: obtain historical operation data of the built charging stations; extract the charging gap index value and the corresponding monthly average number of inbound passengers of each grid unit where the built charging station is located from the operation data; statistically analyze the median of the monthly average number of inbound passengers by grouping according to the charging gap index value; and dynamically adjust the gap level of each grid unit based on the comparison results of the median with the preset operation line and the excellent line.

[0051] In one possible implementation, if historical operational data for existing charging stations (at least 6 months of average monthly visitor volume data) exists, then: extract the CGI value and corresponding average monthly visitor volume for each grid cell containing the existing charging station; group by CGI value (step size 0.1), and calculate the median average monthly visitor volume for each group; if the median average monthly visitor volume for a CGI group is lower than a preset operational threshold (indicating poor operation of stations within that CGI range), then the gap level corresponding to that group is upgraded by one level (e.g., the "slight gap" area is adjusted to "basic balance"); if the median average monthly visitor volume for a CGI group is higher than a preset excellent threshold (indicating excellent operation of stations within that CGI range), then the gap level for that group is maintained or appropriately relaxed. This adaptive mechanism ensures that as actual operational data accumulates, the judgment standard for the CGI threshold continuously approaches the true market trend.

[0052] Furthermore, target candidate points can be recommended to the client according to a dynamically adjusted recommendation priority list.

[0053] Furthermore, when the site recommendation request carries multiple target candidate points, the distance between any two target candidate points in space is determined by a preset deduplication distance threshold, and all candidate point pairs with a distance less than the threshold are filtered out. For each candidate point pair determined to be too close in space, the charging gap scores of the candidate point pairs are compared, the first candidate point with the higher score is retained, and the target candidate point with the lower score is marked as the second candidate point to be eliminated. The grid cells shared by the first candidate point in the second candidate point are removed to obtain the updated grid cells of the second candidate point. Based on the updated grid cells, the charging gap score of the second candidate point is recalculated.

[0054] For example, when multiple candidate points are input simultaneously, those points that are less than the deduplication distance threshold... Candidate points (default 3km) are compared with their score values. The deduplication operation retains the candidate points with high scores, and removes the grid cells shared by the high-scoring candidate points from the former's score and recalculates the score to avoid inflated scores due to excessive candidate point density.

[0055] It should be noted that the system will use gap scores when generating the final recommendation ranking. As the primary ranking criterion, the following supplementary information is provided to decision-makers as footnotes: the growth rate of the number of electric heavy-duty trucks in the region (from data type ④, average monthly growth rate over the past 6 months); the policy support for station construction in the target area (from AI search, data type ⑤, summary of provincial / municipal subsidy standards); and the industrial agglomeration around the candidate sites (from GIS POI, data type ①, trend of newly built logistics parks and industrial parks over the past five years). This supplementary information is not directly involved in the score calculation (to avoid regression-weighted scoring), but only serves as a supplementary reference for manual decision-making.

[0056] For example Figure 2 As shown, Figure 2 This application presents a flowchart for quantifying the charging gap for heavy-duty trucks and recommending suitable locations. First, a standardized grid is constructed for the target area using a two-dimensional spatial grid, laying the data foundation for spatial analysis. Based on this, a demand density layer and a supply coverage layer are constructed sequentially to quantify the intensity of heavy-duty truck parking demand and the coverage capacity of existing charging facilities within the area. Then, the charging gap index is calculated to spatially couple demand and supply, revealing the degree of supply-demand imbalance in each grid unit. Next, candidate locations are ranked and recommended based on the gap index, forming an intuitive list of location priorities. Finally, an adaptive calibration and multi-source auxiliary information fusion mechanism is introduced, dynamically optimizing thresholds based on operational data and integrating multi-dimensional external information to achieve closed-loop iteration and accuracy improvement of the location model, ensuring that the recommendation results more closely reflect real market trends and operational needs.

[0057] In one possible implementation, a comparative evaluation of the charging gap at three candidate sites in Tangshan City, Hebei Province, is taken as an example: (a) Input data: Coordinates of three candidate points: A (118.×°E, 39.×°N, near the highway exit), B (118.×°E, 39.×°N, inside the logistics park), and C (118.×°E, 39.×°N, open space beside the provincial highway); historical trajectory data of heavy trucks in Tangshan City for the past 30 days (approximately 5 million trajectory points, including 950,000 parking event records); and a database of existing charging stations in Tangshan City and surrounding areas (15 heavy truck charging stations, including fences and entry data for each station).

[0058] (II) Execution process: Gridding and Demand Density Calculation: A 20km × 20km analysis area was generated centered on each candidate point, and divided into 1600 500m × 500m grid cells. Trajectory stopping events were assigned to the grid cells. 142 high-demand grid cells (D>0.7) were identified around point A, 108 around point B, and 47 around point C.

[0059] Field effect coverage calculation: The fences of 15 charging stations were incorporated into the field effect model (R=20km for DC fast charging stations and R=10km for ordinary stations), and the total value of each grid was calculated using negative exponential decay. The high-coverage grids (Stotal>threshold) around point A accounted for only 18% (low coverage), point B 52% (medium coverage), and point C 76% (high coverage).

[0060] Charging Gap Index (CGI) Calculation: Around point A: 98 severely gapped grids (CGI≥0.6) were identified, concentrated in the corridor area 3km east of the highway exit; the average CGI of this area is 0.78, and the maximum single grid CGI is 0.93. The area of ​​the gapped area is 1.2km².

[0061] Around point B: 41 severely gapped grids were identified, distributed on the south side of the logistics park, with a mean CGI of 0.52; 57 grids with slight gaps were identified.

[0062] Around point C: 12 severely gapped grids were identified, with an average CGI of 0.31. Most grids were in a basically balanced area.

[0063] Candidate point scoring: Score(A) = 76.3; Score(B) = 51.8; Score(C) = 24.6; Space deduplication: Point A and a certain alternative candidate point A' (approximately 2.5km from A, Score(A')=48.2) trigger deduplication, retaining point A with the higher Score.

[0064] Adaptive calibration verification: By querying historical operational data of 15 existing charging stations in Tangshan City, the correlation between the CGI value distribution and their average monthly charging volume was extracted. Charging stations in areas with CGI≥0.7 (3 stations): The average monthly number of visits is at an excellent level (2100-4500 times), and no threshold adjustment is required; CGI 0.4-0.7 range (5 stations): Average monthly station visits are moderate (800-1800 times), threshold maintained; Areas with CGI < 0.3 (7 stations): Among them, 2 stations have an average monthly visit rate of less than 300 times (poor operation). The system will automatically upgrade the gap level of their grid to avoid being overly optimistic when scoring subsequent candidate points.

[0065] After calibration, the score at point A was fine-tuned to 78.1.

[0066] Recommendation Conclusion: Site A is the preferred choice for construction, as it has the largest surrounding area with the highest charging gap, the highest CGI (Charging Performance Index), and existing operational data in the area validates the economic feasibility of building a site in a high CGI region. Site B can be considered as an alternative. Site C has a relatively balanced supply and demand and is not recommended at this time.

[0067] In this embodiment, on the one hand, by receiving candidate site coordinates sent by the client and performing standardized grid division, a unified spatial computing primitive is established. This allows for the acquisition of historical heavy-duty truck trajectory data and spatial grid-by-grid cross-matching with the gridded analysis area, achieving refined statistics and quantification of heavy-duty truck parking demand within each grid unit. Based on this, the historical set of existing charging stations is further extracted, and the total supply coverage value for each grid unit is calculated. The demand density and supply coverage value are cross-compared to calculate the charging gap score for the target candidate point. Since this process is entirely driven by objective trajectory data and charging station spatial data, it does not rely on subjective expert scoring, completely eliminating scoring differences caused by human factors and ensuring the repeatability and objectivity of the evaluation results. Simultaneously, by comparing the demand density and supply coverage value grid-by-grid, it is possible to determine how much actual demand the existing charging facilities cover and how many vehicles are parked without available stations.

[0068] On the other hand, a gridded analysis area is constructed with the candidate site coordinates as the center. When calculating the charging gap score, the charging gap index of all grid units within the preset range of the candidate point is summarized, so that the evaluation results are coupled with the spatial location of the candidate points. This can effectively identify the supply and demand spillover effect between adjacent candidate points and avoid the inflated score caused by spatial decoupling. By generating a recommendation priority list in descending order of charging gap score and combining it with the subsequent adaptive calibration mechanism, the site recommendation results can be continuously optimized with the accumulation of operational data, forming a complete closed loop from data input, quantitative analysis to result calibration.

[0069] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0070] Please see Figure 3This illustration shows a schematic diagram of a heavy-duty truck charging gap quantification and site selection recommendation device provided in an exemplary embodiment of this application. This heavy-duty truck charging gap quantification and site selection recommendation device can be implemented as all or part of a device through software, hardware, or a combination of both. The device 1 includes a request receiving module 10, a grid partitioning module 20, a grid cross-matching module 30, a demand density calculation module 40, a total supply coverage value calculation module 50, and a ranking recommendation module 60.

[0071] The request receiving module 10 is used to receive a site selection recommendation request sent by the client. The site selection recommendation request carries the candidate site selection coordinates of at least one target candidate point. The grid division module 20 is used to divide the grid according to the candidate site selection coordinates to obtain the gridded analysis area of ​​the target candidate point. Each grid cell in the gridded analysis area has a unique index identifier and latitude and longitude coordinates. The grid cross-matching module 30 is used to acquire the historical trajectory data of heavy trucks within a preset time period in the gridded analysis area, and to perform spatial grid-by-grid cross-matching of the historical trajectory data of heavy trucks with the gridded analysis area to obtain the basic requirement indicators of each grid unit. The demand density calculation module 40 is used to calculate the demand density of each grid cell based on the basic demand index. The total supply coverage value calculation module 50 is used to extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and to calculate the total supply coverage value of each grid cell based on the set of historically built charging stations. The sorting and recommendation module 60 is used to calculate the charging gap score of the target candidate points based on the demand density and total supply coverage value; and to generate a priority list of candidate point recommendations by sorting them in descending order of the charging gap score.

[0072] It should be noted that the heavy-duty truck charging gap quantification and site selection recommendation device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the heavy-duty truck charging gap quantification and site selection recommendation method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the heavy-duty truck charging gap quantification and site selection recommendation device and the heavy-duty truck charging gap quantification and site selection recommendation method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0073] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0074] In this embodiment, on the one hand, by receiving candidate site coordinates sent by the client and performing standardized grid division, a unified spatial computing primitive is established. This allows for the acquisition of historical heavy-duty truck trajectory data and spatial grid-by-grid cross-matching with the gridded analysis area, achieving refined statistics and quantification of heavy-duty truck parking demand within each grid unit. Based on this, the historical set of existing charging stations is further extracted, and the total supply coverage value for each grid unit is calculated. The demand density and supply coverage value are cross-compared to calculate the charging gap score for the target candidate point. Since this process is entirely driven by objective trajectory data and charging station spatial data, it does not rely on subjective expert scoring, completely eliminating scoring differences caused by human factors and ensuring the repeatability and objectivity of the evaluation results. Simultaneously, by comparing the demand density and supply coverage value grid-by-grid, it is possible to determine how much actual demand the existing charging facilities cover and how many vehicles are parked without available stations.

[0075] On the other hand, a gridded analysis area is constructed with the candidate site coordinates as the center. When calculating the charging gap score, the charging gap index of all grid units within the preset range of the candidate point is summarized, so that the evaluation results are coupled with the spatial location of the candidate points. This can effectively identify the supply and demand spillover effect between adjacent candidate points and avoid the inflated score caused by spatial decoupling. By generating a recommendation priority list in descending order of charging gap score and combining it with the subsequent adaptive calibration mechanism, the site recommendation results can be continuously optimized with the accumulation of operational data, forming a complete closed loop from data input, quantitative analysis to result calibration.

[0076] This application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the heavy-duty truck charging gap quantification and site selection recommendation method provided in the above-described method embodiments.

[0077] This application also provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the heavy-duty truck charging gap quantification and site selection recommendation method of the above-described method embodiments.

[0078] Please see Figure 4 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Figure 4 As shown, device 1000 may include: at least one processor 1001, at least one network interface 1004, user interface 1003, memory 1005, and at least one communication bus 1002.

[0079] The communication bus 1002 is used to realize the connection and communication between these components.

[0080] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0081] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0082] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the device 1000 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles operating devices, user interfaces, and applications; the GPU is responsible for rendering and drawing the content required for display on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0083] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing the operating device, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating device, a network communication module, a user interface module, and a heavy-duty truck charging gap quantification and location recommendation application.

[0084] exist Figure 4 In the device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call the heavy truck charging gap quantization and address recommendation application stored in the memory 1005, and specifically perform the following operations: Receive a site selection recommendation request sent by the client. The site selection recommendation request carries the coordinates of at least one candidate site. The grid is divided according to the candidate site coordinates to obtain the gridded analysis area of ​​the target candidate point. Each grid cell in the gridded analysis area has a unique index identifier and latitude and longitude coordinates. Acquire historical trajectory data of heavy trucks within a preset time period within the gridded analysis area, and perform spatial grid-by-grid cross-matching between the historical trajectory data of heavy trucks and the gridded analysis area to obtain the basic requirement indicators for each grid unit; Calculate the demand density for each grid cell based on the basic demand indicators; Extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and calculate the total supply coverage value for each grid cell based on the set of historically built charging stations; Based on demand density and total supply coverage, calculate the charging gap score of the target candidate sites; generate a priority list of candidate sites by sorting them in descending order of charging gap score.

[0085] In one embodiment, when the processor 1001 performs mesh generation based on the candidate address coordinates to obtain the meshed analysis region of the target candidate point, it specifically performs the following operations: Geocoding is performed on the candidate site coordinates to obtain the standardized geographic coordinates of the target candidate points; Using the standardized geographic coordinates of the target candidate points as the geometric center, a square analysis region with a preset side length is constructed to obtain the square analysis region of the target candidate points. The square analysis region is divided into a uniform two-dimensional grid according to the preset grid side length to obtain a two-dimensional regular grid set of target candidate points; Each grid cell in the two-dimensional regular grid set of the target candidate points is assigned a unique index identifier, and the latitude and longitude coordinates of the southwest corner vertex of each grid cell are recorded to obtain the gridded analysis area of ​​the target candidate points.

[0086] In one embodiment, when processor 1001 performs spatial grid-by-grid cross-matching of historical trajectory data of heavy trucks with the gridded analysis area to obtain the basic requirement indicators of each grid cell, it specifically performs the following operations: Based on preset stopping conditions, the historical trajectory data of heavy trucks is identified to obtain a set of stopping events. Each stopping event in the set is marked with the stopping representative coordinates, energy type and stopping duration to obtain the stopping event information for each stopping event. Based on the representative coordinates of each stationary event and the latitude and longitude coordinates of each grid cell, the stationary event information is assigned to the grid cells of the gridded analysis area of ​​the target candidate point; By analyzing the dwell event information assigned to each grid cell, the basic requirement indicators for each grid cell are statistically analyzed.

[0087] In one embodiment, when processor 1001 calculates the demand density of each grid cell based on basic demand metrics, it specifically performs the following operations: From the basic requirement indicators of each grid cell, select the maximum values ​​of different indicators; The demand density of each grid cell is calculated based on the maximum value of different indicators and the demand indicators in the basic demand indicators of each grid cell.

[0088] In one embodiment, when processor 1001 calculates the total supply coverage value for each grid cell based on a historical set of built charging stations, it specifically performs the following operations: Read the attribute data of each historically built charging station in the historical built charging station set. The attribute data includes the centroid coordinates of the fence, the charging power, and the effective service radius. Using a pre-defined two-dimensional field effect attenuation model and attribute data, the service coverage value of each historically built charging station for each grid cell is calculated. From the service coverage values ​​of each historically built charging station to each grid unit, the service coverage values ​​of all charging stations received by each grid unit are selected and superimposed to obtain the total supply coverage value of each grid unit.

[0089] In one embodiment, when the processor 1001 calculates the charging gap score of the target candidate point based on demand density and total supply coverage, it specifically performs the following operations: The supply coverage threshold is calculated and preset based on the total supply coverage value of each grid cell; The charging gap index for each grid cell is calculated based on demand density, total supply coverage, and supply coverage threshold. The charging gap index of each grid cell within the preset range of the target candidate point is summarized and weighted by a preset distance weighting function to obtain the charging gap score of the target candidate point.

[0090] In one embodiment, the processor 1001 also performs the following operations: Based on the charging gap index of each grid unit, and according to the preset gap level judgment threshold, the gap level of each grid unit is divided into one of the following: severe gap area, mild gap area, basic balance area, and oversupply area. The severe gap area is used to indicate that the demand for heavy truck parking in the grid unit is strong and the existing charging station coverage is seriously insufficient. The mild gap area is used to indicate that there is demand for heavy truck parking in the grid unit but the existing coverage is still insufficient. The basic balance area is used to indicate that the supply and demand relationship in the grid unit is basically matched. The oversupply area is used to indicate that the demand for heavy truck parking in the grid unit is weak or has been fully covered by existing facilities. If the gap level of each grid cell is a severe gap area, it is determined to be an area where new charging facilities are recommended. Alternatively, if the gap level of each grid cell is a slight gap area, it is determined to be a candidate area that can be investigated and evaluated. Alternatively, if the gap level of each grid cell is a basically balanced area, it is determined to be an area where new charging facilities are not currently needed. Alternatively, if the gap level of each grid cell is a supply surplus area, it is determined to be an area where new charging facilities are not recommended. The system synchronously outputs statistical information on the area proportion of different gap levels within a preset range around each target candidate point. The statistical information includes the area proportion of each of the severe gap area, mild gap area, basically balanced area, and oversupply area.

[0091] In one embodiment, the processor 1001 also performs the following operations: Obtain historical operational data of existing charging stations; From the operational data, extract the charging gap index value of each grid unit where the existing charging station is located and its corresponding average monthly number of inbound passengers; The median of monthly average station entry volume is statistically analyzed by grouping the charging gap index value. Based on the comparison results of the median with the preset operating lines and excellent lines, the preset gap level judgment threshold is dynamically adjusted.

[0092] In one embodiment, the processor 1001 also performs the following operations: When a location recommendation request carries multiple target candidate points, the distance between any two target candidate points in the space is determined by a preset deduplication distance threshold, and all candidate point pairs with a distance less than the threshold are selected. For each pair of candidate points that are judged to be too close in space, compare the charging gap scores of the candidate point pairs, retain the first candidate point with the higher score, and mark the target candidate point with the lower score as the second candidate point to be eliminated. Remove the grid cells shared by the first candidate point in the second candidate point to obtain the updated grid cells of the second candidate point; Based on the updated grid cells, the charging gap score of the second candidate point is recalculated.

[0093] In this embodiment, on the one hand, by receiving candidate site coordinates sent by the client and performing standardized grid division, a unified spatial computing primitive is established. This allows for the acquisition of historical heavy-duty truck trajectory data and spatial grid-by-grid cross-matching with the gridded analysis area, achieving refined statistics and quantification of heavy-duty truck parking demand within each grid unit. Based on this, the historical set of existing charging stations is further extracted, and the total supply coverage value for each grid unit is calculated. The demand density and supply coverage value are cross-compared to calculate the charging gap score for the target candidate point. Since this process is entirely driven by objective trajectory data and charging station spatial data, it does not rely on subjective expert scoring, completely eliminating scoring differences caused by human factors and ensuring the repeatability and objectivity of the evaluation results. Simultaneously, by comparing the demand density and supply coverage value grid-by-grid, it is possible to determine how much actual demand the existing charging facilities cover and how many vehicles are parked without available stations.

[0094] On the other hand, a gridded analysis area is constructed with the candidate site coordinates as the center. When calculating the charging gap score, the charging gap index of all grid units within the preset range of the candidate point is summarized, so that the evaluation results are coupled with the spatial location of the candidate points. This can effectively identify the supply and demand spillover effect between adjacent candidate points and avoid the inflated score caused by spatial decoupling. By generating a recommendation priority list in descending order of charging gap score and combining it with the subsequent adaptive calibration mechanism, the site recommendation results can be continuously optimized with the accumulation of operational data, forming a complete closed loop from data input, quantitative analysis to result calibration.

[0095] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for quantifying the charging gap of heavy-duty trucks and recommending locations can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the program for quantifying the charging gap of heavy-duty trucks and recommending locations can be a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0096] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for quantifying and recommending charging gaps for heavy-duty trucks, characterized in that, Applied to the server side, the method includes: Receive a site selection recommendation request sent by a client, the site selection recommendation request carrying the candidate site selection coordinates of at least one target candidate point; The candidate site coordinates are used to divide the area into grids to obtain the gridded analysis region of the target candidate point. Each grid cell in the gridded analysis region has a unique index identifier and latitude and longitude coordinates. Obtain historical trajectory data of heavy trucks within a preset time period within the gridded analysis area, and perform spatial grid-by-grid cross-matching between the historical trajectory data of heavy trucks and the gridded analysis area to obtain the basic demand indicators of each grid unit; Based on the aforementioned basic demand indicators, the demand density of each grid cell is calculated; Extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and calculate the total supply coverage value of each grid cell based on the set of historically built charging stations; Based on the demand density and total supply coverage, calculate the charging gap score of the target candidate points; generate a priority list of candidate points by sorting them in descending order of the charging gap scores.

2. The method according to claim 1, characterized in that, The step of dividing the target candidate point into a grid based on the candidate site coordinates to obtain the gridded analysis region includes: Geocoding is performed on the candidate site coordinates to obtain the standardized geographic coordinates of the target candidate point; Using the standardized geographic coordinates of the target candidate point as the geometric center, a square analysis region with a preset side length is constructed to obtain the square analysis region of the target candidate point; The square analysis region is divided into a uniform two-dimensional grid according to a preset grid side length to obtain a two-dimensional regular grid set of the target candidate points; Each grid cell in the two-dimensional regular grid set of the target candidate point is assigned a unique index identifier, and the latitude and longitude coordinates of the southwest corner vertex of each grid cell are recorded to obtain the gridded analysis area of ​​the target candidate point.

3. The method according to claim 1, characterized in that, The step of performing spatial grid-by-grid cross-matching between the historical trajectory data of the heavy trucks and the gridded analysis area to obtain the basic requirement indicators for each grid cell includes: Based on preset stopping conditions, the historical trajectory data of the heavy truck is identified to obtain a set of stopping events, and each stopping event in the set of stopping events is marked with the stopping representative coordinates, energy type and stopping duration to obtain the stopping event information of each stopping event; Based on the representative coordinates of each dwelling event and the latitude and longitude coordinates of each grid cell, the dwelling event information is allocated to the grid cells of the gridded analysis region of the target candidate point; Based on the dwell event information allocated to each grid cell, the basic requirement indicators of each grid cell are statistically analyzed.

4. The method according to claim 1, characterized in that, The basic demand indicators include the total number of electric heavy truck parking events falling into the grid, the number of de-loaded electric heavy trucks falling into the grid, and the average parking time of all parking events within the grid. The calculation of the demand density for each grid cell based on the aforementioned basic demand index includes: From the basic requirement indicators of each grid cell, the maximum values ​​of different indicators are selected; The demand density of each grid cell is calculated based on the maximum value of the different indicators and the demand indicators in the basic demand indicators of each grid cell.

5. The method according to claim 1, characterized in that, The calculation of the total supply coverage value for each grid cell based on the historical set of existing charging stations includes: Read the attribute data of each historically built charging station in the historically built charging station set. The attribute data includes the centroid coordinates of the fence, the charging power, and the effective service radius. Using a preset two-dimensional field effect attenuation model and the attribute data, the service coverage value of each historically built charging station to each grid unit is calculated. From the service coverage values ​​of each historically built charging station to each grid unit, the service coverage values ​​of all charging stations received by each grid unit are selected and superimposed to obtain the total supply coverage value of each grid unit.

6. The method according to claim 1, characterized in that, The calculation of the charging gap score for the target candidate point based on the demand density and total supply coverage includes: Based on the total supply coverage value of each grid cell, the supply coverage threshold is calculated and preset; The charging gap index for each grid cell is calculated based on the demand density, total supply coverage value, and supply coverage threshold. The charging gap index of each grid cell within a preset range of the target candidate point is summarized and weighted by a preset distance weighting function to obtain the charging gap score of the target candidate point. The charging gap index of each grid cell The calculation formula is: in, For each grid cell Demand density, Each grid cell is Total supply coverage value, This is the supply coverage threshold; The target candidate point Charging gap score The calculation formula is: in, It is the first One target candidate point, Distance weighting function, used to measure grid cells The gap for the target candidate point The degree of contribution of the rating, It is a limiting condition, indicating that it applies only to the target candidate point. The summation calculation is performed on grid cells with a distance of no more than 5 kilometers. It is the target candidate point With grid cells The spherical distance between the centers of mass, It is the distance attenuation half-range. It is the first Each grid cell.

7. The method according to claim 6, characterized in that, The method further includes: Based on the charging gap index of each grid unit, and according to a preset gap level determination threshold, the gap level of each grid unit is divided into four categories: severe gap area, mild gap area, basic balance area, and oversupply area. The severe gap area indicates that the demand for heavy truck parking in the grid unit is strong and the existing charging station coverage is seriously insufficient. The mild gap area indicates that there is demand for heavy truck parking in the grid unit but the existing coverage is still insufficient. The basic balance area indicates that the supply and demand relationship in the grid unit is basically matched. The oversupply area indicates that the demand for heavy truck parking in the grid unit is weak or has been fully covered by existing facilities. If the shortage level of each grid cell is a severe shortage area, it is determined to be an area where new charging facilities are recommended. Alternatively, if the shortage level of each grid cell is a mild shortage area, it is determined to be a candidate area for investigation and evaluation. Alternatively, if the shortage level of each grid cell is a basically balanced area, it is determined to be an area where new charging facilities are not currently needed. Alternatively, if the shortage level of each grid cell is a supply surplus area, it is determined to be an area where new charging facilities are not recommended. The system synchronously outputs statistical information on the area proportion of different gap levels within a preset range around each target candidate point. The statistical information includes the area proportion of each of the severe gap area, mild gap area, basically balanced area, and oversupply area.

8. The method according to claim 7, characterized in that, The notch level of each grid cell is adaptively adjusted according to the following steps: Obtain historical operational data of existing charging stations; From the operational data, extract the charging gap index value and the corresponding monthly average number of charging stations in the grid unit where each existing charging station is located; The median of the monthly average number of vehicles entering the station is statistically analyzed based on the charging gap index value. The gap level of each grid unit is dynamically adjusted based on the comparison results between the median and the preset operating lines and excellent lines.

9. The method according to claim 1, characterized in that, After calculating the charging gap score of the target candidate point based on the demand density and total supply coverage, the method further includes: When the location recommendation request carries multiple target candidate points, the distance between any two target candidate points in the space is determined by a preset deduplication distance threshold, and all candidate point pairs with a distance less than the threshold are filtered out. For each pair of candidate points that are judged to be too close in space, compare the charging gap scores of the candidate point pairs, retain the first candidate point with the higher score, and mark the target candidate point with the lower score as the second candidate point to be eliminated. Remove the grid cells shared by the first candidate point from the second candidate point to obtain the updated grid cells of the second candidate point; Based on the updated grid cells, the charging gap score of the second candidate point is recalculated.

10. A device for quantifying and recommending charging gaps for heavy-duty trucks, characterized in that, The device includes: The request receiving module is used to receive a site selection recommendation request sent by the client, wherein the site selection recommendation request carries the candidate site selection coordinates of at least one target candidate point; The grid division module is used to divide the grid according to the candidate site selection coordinates to obtain the gridded analysis area of ​​the target candidate point. Each grid cell in the gridded analysis area has a unique index identifier and latitude and longitude coordinates. The grid cross-matching module is used to acquire the historical trajectory data of heavy trucks within a preset time period in the gridded analysis area, and to perform spatial grid-by-grid cross-matching of the historical trajectory data of heavy trucks with the gridded analysis area to obtain the basic requirement indicators of each grid unit. The demand density calculation module is used to calculate the demand density of each grid cell based on the basic demand index. The total supply coverage value calculation module is used to extract the set of historically built charging stations within the gridded analysis area and its preset extended boundary, and to calculate the total supply coverage value of each grid unit based on the set of historically built charging stations. The ranking and recommendation module is used to calculate the charging gap score of the target candidate points based on the demand density and total supply coverage value; and generate a priority list of candidate point recommendations by arranging the candidate points in descending order according to the charging gap score.

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