New energy vehicle high-magnification charging pile task allocation method based on ant colony algorithm

Abstract

The invention belongs to the technical field of intelligent optimization algorithm and new energy high-rate charging cross fusion, and provides a new energy vehicle high-rate charging pile task allocation method based on an ant colony algorithm. The method comprises the following steps: S1, pre-searching and filtering high-magnification charging piles of which the danger probability is greater than a threshold value to form a candidate set; s2, defining a high-magnification charging task set and a high-magnification charging pile set by adopting a graph model; s3, constructing a comprehensive cost function, and determining a weight coefficient of the comprehensive cost function; s4, the high-magnification charging pile carries out multiple rounds of searching; s5, performing local search optimization on the optimal solution obtained by each ant; and S6, matching optimization is carried out, and distribution is completed. According to the method, the problems of safety and efficiency in a traditional method are effectively solved by preprocessing and filtering the charging piles with too high danger probability, applying a multi-round search strategy, simulating pruning operation and applying a local optimization algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of the cross - integration of intelligent optimization algorithms and high - rate charging of new energy sources, and particularly relates to a method for task allocation of high - rate charging piles for new - energy vehicles based on the ant colony algorithm. Background Art

[0002] High - rate charging refers to the charging current exceeding 1C of the battery's rated capacity, mainly applied to the new - energy fast - charging scenario. With the popularization of electric vehicles and the rapid development of renewable energy, high - rate charging piles are the key facilities to alleviate charging anxiety and improve charging efficiency. However, in the high - rate charging scenario, the safety problems of charging piles are becoming increasingly prominent. The main safety risks include battery thermal runaway, uneven power distribution, overheating of cables, and insufficient environmental heat dissipation. These problems may not only lead to low charging efficiency but also trigger serious safety accidents, such as charging - pile fires, threatening the safety of users' lives and property.

[0003] Traditional charging - pile management methods often focus on improving charging efficiency. These methods usually directly include all available charging piles in the candidate set and perform task allocation based on simple factors such as distance and price, while ignoring the key factor of the danger probability of charging piles. When using the traditional ant colony algorithm to handle the task allocation of high - rate charging piles, there is a lack of effective pre - processing means to filter out charging piles with too high a danger probability, resulting in the reliability and safety of the task - allocation results being difficult to guarantee. Therefore, how to optimize the high - rate charging - pile matching algorithm and reduce the failure rate while ensuring safety has become an urgent problem to be solved in the current high - rate charging - pile management field. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for task allocation of high - rate charging piles for new - energy vehicles based on the ant colony algorithm, aiming to solve the problems mentioned in the background art. This method not only inherits the positive - feedback and concurrency characteristics of the ant colony algorithm and can quickly find the optimal task - allocation scheme but also realizes safe, flexible, and efficient task allocation.

[0005] The present invention provides a method for task allocation of high - rate charging piles for new - energy vehicles based on the ant colony algorithm, including the following steps: Step S1: Perform pre - search according to the prediction results of the deep - learning model of high - rate charging piles, filter out high - rate charging piles with a danger probability greater than the threshold, and form a candidate set; Step S2: Based on the candidate set, use a graph model to define the high - rate charging task set and high - rate charging - pile set of new - energy vehicles; the high - rate charging task set includes several high - rate charging task nodes, and the high - rate charging - pile set includes several high - rate charging - pile nodes; Step S3: Construct the comprehensive cost function of the high-rate charging task nodes and high-rate charging pile nodes, analyze and streamline the high-rate charging pile parameters in the candidate set through simulation pruning, and determine the weight coefficients of the comprehensive cost function based on the high-rate charging pile parameters after simulation pruning; Step S4: Conduct multiple rounds of search on the high-rate charging piles in the candidate set through the ant colony algorithm. The process of the ant colony algorithm includes initializing pheromones, simulating the construction of matching solutions, updating pheromones according to the comprehensive cost, iterating, and obtaining the optimal solution; Step S5: Optimize the optimal solution obtained by each ant through local search; Step S6: Combine real-time data for matching optimization to complete the allocation.

[0006] Further, Step S1 specifically includes the following steps: Step S11: Collect the status data, environmental data, and battery data of the high-rate charging piles, and then perform preprocessing, which includes data cleaning and feature engineering; among them, the high-rate charging pile status data includes fault records, real-time temperature, and power distribution; the environmental data includes temperature, humidity, and fire protection facility status; the battery data includes health status, state of charge, and charge and discharge cycle times; Step S12: Input the preprocessed high-rate charging pile data into the deep learning model for training, and predict the risk probability of the high-rate charging piles according to historical fault cases and environmental data during the training process ; Step S13: Draw the ROC curve, calculate the difference between the recall rate and the false alarm rate, and calculate the optimized threshold of the risk probability. The formula is expressed as: ; In the formula: is the vertical axis of the curve, representing the proportion of fault samples correctly predicted as high-risk by the deep learning model; is the horizontal axis of the curve, representing the proportion of normal samples mispredicted as high-risk by the deep learning model; is the optimized threshold; is the value of the threshold Threshold corresponding to the maximum value within the value range of the threshold Threshold; Step S14: Screen according to the optimized threshold, filter the high-rate charging piles with a risk probability greater than the optimized threshold. The filtering formula is expressed as: ; When the risk probability of the high-rate charging pile ≤ the optimized threshold , the high-rate charging pile is considered safe and included in the candidate set; otherwise, the high-rate charging pile is considered dangerous and not included in the candidate set.

[0007] Further, in step S2, the high-rate charging task set and high-rate charging pile graph model of the new energy vehicle are represented as: G = (C, T, W, E); In the formula: T is the set of high-rate charging task nodes of the new energy vehicle to be allocated; C is the set of high-rate charging pile nodes of the new energy vehicle; E is the distance from the new energy vehicle to the high-rate charging pile or the edge connecting two high-rate charging pile nodes; W is the comprehensive cost for the new energy vehicle to reach the high-rate charging pile.

[0008] Further, in step S2, the high-rate charging task set and high-rate charging pile set of the new energy vehicle are defined using a graph model, which specifically includes the following steps: Step S21: High-rate charging task set: T = (t1, t2,..., t n ), where t1, t2,..., t n are the first high-rate charging task node, the second high-rate charging task node, and the nth high-rate charging task node respectively; the information included in the high-rate charging task node: high-rate charging task number, high-rate charging task location, and charging amount required for the high-rate charging task; Step S22: High-rate charging pile set: C = (c1, c2,..., c m ), where c1, c2,..., c m are the first high-rate charging pile node, the second high-rate charging pile node, and the mth high-rate charging pile node respectively; the information included in the high-rate charging pile node: high-rate charging pile number, high-rate charging pile location, current load of the high-rate charging pile, and maximum load capacity of the high-rate charging pile.

[0009] Further, step S3 specifically includes the following steps: Step S31: Analyze and streamline the high-rate charging pile parameters in the high-rate charging pile candidate set through simulation pruning to remove unnecessary high-rate charging pile parameters in the candidate set. The simulation pruning operation can be expressed as: ; where P is the parameter set, w i is the weight of the parameter , Q is the pruning threshold, and P pruned is the parameter set after pruning, which only contains parameters with weights not less than the pruning threshold Q; Step S32: Use statistical methods to evaluate the correlation or influence degree between each high-rate charging pile parameter after simulation pruning and the comprehensive cost function to determine the weight coefficient of each high-rate charging pile parameter after simulation pruning on the comprehensive cost function. The statistical methods include the correlation coefficient method or the regression analysis method; Step S33: Construct the comprehensive cost function of the high-rate charging task nodes and high-rate charging pile nodes, which is expressed by the formula: ; In the formula: is the comprehensive cost of using the j-th high-rate charging pile node c at the i-th high-rate charging task node t i ; j is the comprehensive cost; are all weight coefficients, ; is the distance from the current location of the i-th high-rate charging task node t i to the j-th high-rate charging pile node c j ; is the estimated high-rate charging time of using the j-th high-rate charging pile node c at the i-th high-rate charging task node t i ; j is the estimated high-rate charging time; is the current load of the j-th high-rate charging pile node c j .

[0010] Furthermore, step S4 specifically includes the following steps: Step S41: Initialize the pheromone concentration of each pair of high-rate charging task node - high-rate charging pile node connections, which is expressed by the formula: ; In the formula: is the pheromone concentration from the i-th high-rate charging task node t i to the j-th high-rate charging pile node c j , that is, an indicator of the attractiveness or selection probability of this path; Step S42: Conduct multiple rounds of simulated search according to the weight coefficients of the comprehensive cost function. The specific steps of each round of simulated search are as follows: Preset the maximum number of iterations and the pheromone threshold; Each ant randomly selects an unassigned high-rate charging task node and high-rate charging pile node, which is expressed by the formula: ; In the formula: is the probability that the ant chooses to match the i-th high-rate charging task node t i to the j-th high-rate charging pile node c j ; represents the information for evaluating the matching of the to-be-assigned i-th high-rate charging task node t i and the j-th high-rate charging pile node c j based on other influencing factors or rules; $N$ is the set of the $i$-th high-rate charging task node $t$ i and the $k$-th high-rate charging pile node $c$ k ; Update the correspondence between the selected high-rate charging task nodes and high-rate charging pile nodes, and mark the high-rate charging task nodes as assigned; Step S43: Calculate the comprehensive cost of the current matching solution through the comprehensive cost function, and update the pheromone concentration according to the comprehensive cost. The formula is expressed as: ; In the formula, is the pheromone concentration on the path from the $i$-th high-rate charging task node $t$ i to the $j$-th high-rate charging pile node $c$ j ; is the pheromone evaporation coefficient; represents the remaining part of the original pheromone concentration; is the pheromone increment on the path from the $i$-th high-rate charging task node $t$ i to the $j$-th high-rate charging pile node $c$ j ; Step S44: When the preset maximum number of iterations is reached or the change rate of pheromone concentration is less than the preset pheromone threshold, stop updating the pheromone and the simulated matching pheromone iteration, record the ant matching of this round of simulated search as the optimal solution, and mark the weight coefficient of the comprehensive cost function of this round of simulated search; Step S45: Repeat Step S43 - Step S44 until the weight coefficients of the comprehensive cost function are all marked, obtain the optimal solution of each ant, that is, finally obtain the optimal solution set of multiple rounds of simulated search.

[0011] Furthermore, Step S5 is specifically as follows. Use the simulated annealing algorithm to perform local search optimization on the optimal solution obtained by each ant, including the following steps: Step S51: Take the optimal solution of each ant as the initial solution, and set the initial temperature, termination temperature, and cooling rate; Step S52: According to the high-rate charging pile $j$ of the current solution, calculate the comprehensive score $S(j)$ of the high-rate charging pile $j$; then randomly select a high-rate charging pile within the neighborhood of the current solution , and calculate the comprehensive score of the high-rate charging pile ; The comprehensive score $S$ formula is expressed as: In the formula: are the weight coefficients of Safety, Speed, Price, and Distance respectively; Step S53: According to the Metropolis criterion, if , then accept the high-rate charging pile ; if , then calculate the acceptance probability , which is expressed by the formula: ; In the formula: T is the temperature; P M is the acceptance probability; Step S54: Update the temperature according to the cooling strategy; Step S55: Repeat Step S52 - Step S54 until the termination temperature is reached, and obtain the optimal solution after local search optimization for each ant as the result of local search optimization.

[0012] The present invention has the following beneficial effects: (1) By virtue of the positive feedback and concurrency characteristics of the ant colony algorithm, the optimal task allocation scheme can be quickly found. The positive feedback mechanism of the ant colony algorithm can achieve load balancing of high-rate charging tasks, avoid the problem of overload during peak hours at high-rate charging stations, and improve resource utilization efficiency. Moreover, the ant colony algorithm has strong robustness and self-adaptability, and can adapt to task allocation problems of different scales and complexities. And through the continuously iterative pheromone update mechanism, the system can provide more intelligent decision-making support to help managers make more scientific resource allocation and scheduling decisions. In addition, by simulating the behavior of ant colonies, a comprehensive cost function is constructed by combining factors such as the geographical location, charging speed, and current power of high-rate charging piles, and dynamic information such as user behavior prediction and environmental factor changes is incorporated, making the task allocation more accurate and real-time.

[0013] (2) A deep learning model is introduced to accurately predict the risk probability of high-rate charging piles, and the threshold of the risk probability is optimized using the ROC curve analysis to effectively filter out potential safety hazards and improve the safety and reliability of task allocation. At the same time, a multi-round search strategy is adopted to optimize for different weight coefficients, improving the flexibility of task allocation to ensure that the final allocation scheme can more comprehensively meet user needs and system performance requirements. In addition, through simulated pruning operations, the parameters of high-rate charging piles are deeply analyzed and streamlined, simplifying the problem complexity, improving the calculation efficiency, and optimizing the determination of weight coefficients and the construction of the comprehensive cost function. After obtaining the initial optimal solution, a local optimization algorithm is further used to finely adjust the solution, improving the accuracy and efficiency of task allocation and enhancing the adaptability to complex environments and dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The exemplary embodiments of the present invention can be more fully understood by referring to the following drawings: Figure 1 This is a flowchart of the high-rate charging pile task allocation method for new energy vehicles based on the ant colony algorithm provided by the embodiments of the present invention. Detailed implementation manners

[0015] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0017] The embodiments of the present invention provide a high-rate charging pile task allocation method for new energy vehicles based on the ant colony algorithm, including the following steps: Step S1: Perform pre-search according to the prediction results of the deep learning model of the high-rate charging pile, filter out the high-rate charging piles with a danger probability greater than the threshold, and form a candidate set; Step S2: Based on the candidate set, use a graph model to define the high-rate charging task set and the high-rate charging pile set of new energy vehicles; the high-rate charging task set includes several high-rate charging task nodes, and the high-rate charging pile set includes several high-rate charging pile nodes; Step S3: Construct a comprehensive cost function for high-rate charging task nodes and high-rate charging pile nodes, analyze and streamline the high-rate charging pile parameters in the candidate set through simulated pruning, and determine the weight coefficient of the comprehensive cost function based on the high-rate charging pile parameters after simulated pruning; Step S4: Conduct multiple rounds of search on the high-rate charging piles in the candidate set through the ant colony algorithm. The process of the ant colony algorithm includes initializing pheromone, simulating the construction of matching solutions, updating pheromone according to the comprehensive cost, iterating, and obtaining the optimal solution; Step S5: Perform local search optimization on the optimal solutions obtained by each ant; Step S6: Combine real-time data for matching optimization to complete the allocation.

[0018] In some embodiments, step S1 specifically includes the following steps: Step S11: Collect high-rate charging pile status data, environmental data and battery data, and then perform preprocessing, including data cleaning and feature engineering; the high-rate charging pile status data includes fault records, real-time temperature and power distribution; the environmental data includes temperature, humidity and fire-fighting facility status; the battery data includes state of health (SOH), state of charge (SOC) and number of charge and discharge cycles; Specifically, during preprocessing, data cleaning is first performed to remove missing values and outliers. For example, data with a temperature exceeding 100 °C is removed. Then, feature engineering is carried out to extract time series features, such as the temperature volatility in the past 24 hours. Composite features are calculated, such as power density and environmental heat dissipation efficiency. A dynamic temperature threshold based on the battery's state of health (SOH) can also be constructed. Step S12: Input the preprocessed high-rate charging pile data into the deep learning model for training. During the training process, based on historical fault cases and environmental data, predict the risk probability of the high-rate charging pile. ; Specifically, the deep learning model includes an input layer, a hidden layer, and an output layer. In the input layer, a multi-dimensional feature vector composed of static features such as the high-rate charging pile model and dynamic features such as real-time temperature is input. In the hidden layer, a multi-layer perceptron or a graph neural network is used to capture the spatial dependence of the high-rate charging pile, and the attention mechanism is used to weight the importance of different features. In the output layer, a single neuron outputs the risk probability. In the deep learning model, the binary cross-entropy is used as the loss function training objective, labels are set according to historical fault records, data augmentation is performed while processing the data, and noise is added to the fault data to improve the robustness of the model. Step S13: Plot the ROC curve, calculate the difference between the recall rate and the false alarm rate, and calculate the optimized threshold of the risk probability. The formula is expressed as: ; In the formula: is the vertical axis of the curve, representing the proportion of fault samples correctly predicted as high-risk by the deep learning model. is the horizontal axis of the curve, representing the proportion of normal samples wrongly predicted as high-risk by the deep learning model. is the optimized threshold. is the value of the threshold Threshold corresponding to the maximum value within the value range of the threshold Threshold. Specifically, using 100,000 historical fault cases, analyze the true positive rate (TPR) and false positive rate (FPR) at different thresholds through the ROC curve. The optimization goal is to maximize TPR - FPR, ensuring a high recall rate (e.g., TPR > 95%) while controlling the false alarm rate (e.g., FPR < 10%). In addition, the threshold is dynamically adjusted according to the environmental temperature. When the environmental temperature < 35 °C, the threshold is 0.3. When the environmental temperature ≥ 35 °C, the threshold is 0.2. Step S14: Filter according to the optimized threshold, and filter out high-rate charging piles with a risk probability greater than the optimized threshold. The filtering formula is expressed as: ; When the risk probability of the high-rate charging pile ≤ the optimized threshold , the high-rate charging pile is considered safe and included in the candidate set; otherwise, the high-rate charging pile is considered dangerous and not included in the candidate set.

[0019] Specifically, high-rate charging piles are screened according to the optimized threshold, and high-rate charging piles with a danger probability > threshold are excluded to form a candidate set; for example, when the threshold is 0.3, the candidate set is the set of high-rate charging piles with a danger probability ≤ 0.3; a secondary check is also performed on the candidate set, and the battery and cable temperatures are monitored in real time. If the cable temperature continues to rise, it is excluded.

[0020] In some embodiments, in step S2, the high-rate charging task set and high-rate charging pile graph model of the new energy vehicle are represented as: G=(C,T,W,E); In the formula: T is the set of high-rate charging task nodes of the new energy vehicle to be allocated; C is the set of high-rate charging pile nodes of the new energy vehicle; E is the distance from the new energy vehicle to the high-rate charging pile or the edge connecting two high-rate charging pile nodes; W is the comprehensive cost for the new energy vehicle to reach the high-rate charging pile.

[0021] In some embodiments, in step S2, a graph model is used to define the high-rate charging task set and high-rate charging pile set of the new energy vehicle, which specifically includes the following steps: Step S21: High-rate charging task set: T=(t1,t2,...,t n ), where t1,t2,...,t n are the first high-rate charging task node, the second high-rate charging task node, and the nth high-rate charging task node respectively; the information included in the high-rate charging task node: high-rate charging task number, high-rate charging task location, and the charging amount required for the high-rate charging task. Step S22: High-rate charging pile set: C=(c1,c2,...,c m ), where c1,c2,...,c m are the first high-rate charging pile node, the second high-rate charging pile node, and the mth high-rate charging pile node respectively; the information included in the high-rate charging pile node: high-rate charging pile number, high-rate charging pile location, the current load of the high-rate charging pile, and the maximum load capacity of the high-rate charging pile.

[0022] In some embodiments, step S3 specifically includes the following steps: Step S31: Analyze and streamline the high-rate charging pile parameters in the high-rate charging pile candidate set through simulated pruning to remove unnecessary high-rate charging pile parameters in the candidate set. The simulated pruning operation can be expressed as: ; Among them, P is a set of parameters, and w i is a parameter weight, Q is a pruning threshold, and P pruned is the set of parameters after pruning, which only contains parameters with weights not less than the pruning threshold Q; Specifically, simulated pruning will follow certain rules, such as deleting redundant information or adjusting parameters; Step S32: Use statistical methods to evaluate the correlation or influence degree between each set of high-power charging pile parameters after simulated pruning and the comprehensive cost function, so as to determine the weight coefficient of each set of high-power charging pile parameters after simulated pruning on the comprehensive cost function. The statistical methods include the correlation coefficient method or the regression analysis method; Specifically, taking parameters such as distance, estimated charging time, and current load as examples for weight coefficient definition: Generally, the value of the distance weight coefficient is 0.3 - 0.5. If the distance factor has a greater impact on the comprehensive cost in the charging scenario, for example, in long-distance transportation or when the high-power charging piles are relatively scattered in the city, a larger value can be taken, such as 0.45 - 0.5; if the distance factor has a relatively small impact, such as when the high-power charging piles are relatively concentrated in the city center area and the vehicle driving distance is generally short, a smaller value can be taken, such as 0.3 - 0.35; The value of the estimated charging time weight coefficient β is 0.2 - 0.4. When the charging power of the high-power charging piles varies greatly and the user is more sensitive to the charging time, β can take a larger value, such as 0.35 - 0.4; if the charging power of the high-power charging piles is relatively stable and the user has less strict requirements for the charging time, β can take a smaller value, such as 0.2 - 0.25; The value of the current load weight coefficient γ is 0.1 - 0.3. During the charging peak period, the load of the high-power charging piles is generally high, and γ can take a larger value, such as 0.25 - 0.3. During the non-peak period, the load of the high-power charging piles is low, and γ can take a smaller value, such as 0.1 - 0.15; At the same time, in order to ensure the rationality of the weight coefficient, it is necessary to satisfy α + β + γ = 1; Step S33: Construct the comprehensive cost function of the high-power charging task node and the high-power charging pile node, which is expressed by the formula: ; In the formula: is the comprehensive cost of using the j-th high-power charging pile node c i at the i-th high-power charging task node t j ; are all weight coefficients, ; For the current position of the i-th high-rate charging task node t i to the distance of the j-th high-rate charging pile node c j ; For the i-th high-rate charging task node t i using the j-th high-rate charging pile node c j The estimated high-rate charging time; For the j-th high-rate charging pile node c j The current load.

[0023] Specifically, when constructing the comprehensive cost function, safety parameters, price cost parameters, distance parameters, charging speed parameters, etc. are considered; among them, safety parameters include the risk of battery thermal runaway, the risk of uneven power distribution, the risk of cable overheating, the risk of insufficient environmental heat dissipation, etc.; charging speed parameters include the output power of the charging pile, etc.

[0024] In some embodiments, step S4 specifically includes the following steps: Step S41: Initialize the pheromone concentration of each pair of high-rate charging task node - high-rate charging pile node connections, and the formula is expressed as: ; In the formula: is the pheromone concentration from the i-th high-rate charging task node t i to the j-th high-rate charging pile node c j , that is, an indicator of the attractiveness or selection probability of this path; It can be understood that each ant selects a path according to the probability, and the probability is determined by the pheromone concentration and the attractiveness of the path. The path attractiveness is determined by the predicted high-rate charging demand and the availability of the high-rate charging pile. In practical applications, the initial pheromone concentration of each pair of high-rate charging task node - high-rate charging pile node can be set according to historical data. For example, if a certain high-rate charging pile has been frequently used by a certain type of task in the past, a higher initial pheromone concentration can be set to reflect its attractiveness; Step S42: Conduct multiple rounds of simulated searches according to the weight coefficients of the comprehensive cost function. Each round of simulated search is carried out around different combinations of weight coefficients, aiming to obtain the optimal solutions under different influencing factors. The specific steps of each round of simulated search are as follows: Preset the maximum number of iterations MAX_ITER and the pheromone threshold ; The maximum number of iterations MAX_ITER indicates that the ant will try to match the high-rate charging task node - high-rate charging pile node for up to MAX_ITER rounds; the pheromone threshold , which is used to judge whether the pheromone is high enough to affect the selection; Each ant randomly selects an unassigned high-rate charging task node and a high-rate charging pile node, which is expressed by the formula: ; In the formula: is the probability that the ant selects to match the \(i\)-th high-rate charging task node \(t\) i to the \(j\)-th high-rate charging pile node \(c\) j ; represents the information for evaluating the matching of the \(i\)-th high-rate charging task node \(t\) to be assigned and the \(j\)-th high-rate charging pile node \(c\) based on other influencing factors or rules i and the \(j\)-th high-rate charging pile node \(c\) j ; \(N\) is the set of the \(i\)-th high-rate charging task node \(t\) i and the \(k\)-th high-rate charging pile node \(c\) k ; Update the corresponding relationship between the selected high-rate charging task node and the high-rate charging pile node, and mark the high-rate charging task node as assigned; Specifically, the ant randomly selects a high-rate charging task node from the unassigned high-rate charging tasks. For example, electric vehicle A needs high-rate charging, and then randomly selects one from the available high-rate charging piles (such as high-rate charging pile 1). The selection probability is jointly determined by the pheromone concentration and heuristic information (such as the idle state of the high-rate charging pile, the expected high-rate charging time, etc.); Once the high-rate charging task node and the high-rate charging pile node are selected, then update the corresponding relationship between them, record that electric vehicle A will use high-rate charging pile 1, update the status of electric vehicle A to assigned, and remove it from the unassigned task list to avoid repeated assignment; Step S43: Calculate the comprehensive cost of the current matching solution through the comprehensive cost function, and update the pheromone concentration according to the comprehensive cost, which is expressed by the formula: ; In the formula, is the pheromone concentration on the path from the \(i\)-th high-rate charging task node \(t\) i to the \(j\)-th high-rate charging pile node \(c\) j ; is the pheromone evaporation coefficient; represents the remaining part of the original pheromone concentration; is the pheromone increment of the path from the \(i\)-th high-rate charging task node \(t\) i to the \(j\)-th high-rate charging pile node \(c\) j ; Specifically, takes values between 0 and 1, It is jointly determined by the comprehensive cost function and heuristic information (such as the idle state of high-power charging piles, the expected high-power charging time, etc.); in practical applications, whenever an ant completes a high-power charging task allocation, it calculates the comprehensive cost of this path to update the pheromone concentration; thus, better paths will obtain more pheromones, making subsequent ants more inclined to choose these paths, which helps subsequent ants to more effectively utilize the results of previous selections; Step S44: When the preset maximum number of iterations is reached or the change rate of pheromone concentration is less than the preset pheromone threshold, stop updating the pheromone and the simulated matching pheromone iteration, record the ant matching in this round of simulated search as the optimal solution, and mark the weight coefficient of the comprehensive cost function in this round of simulated search; Step S45: Repeat Step S43 - Step S44 until the weight coefficients of the comprehensive cost function are all marked, obtain the optimal solution for each ant, that is, finally obtain the optimal solution set for multiple rounds of simulated search.

[0025] In some embodiments, Step S5 specifically is to perform local search optimization on the optimal solution obtained by each ant using the simulated annealing algorithm; Specifically, after the ant colony algorithm search, 3 ants obtained 3 different optimal solutions, which are respectively: Solution A: Recommend high-power charging pile P1, which has a high safety factor, a moderate charging price, but is far from the user; Solution B: Recommend high-power charging pile P2, which has a fast charging speed, but a relatively high price and an average safety record; Solution C: Recommend high-power charging pile P3, which is the closest to the user, has a relatively low price, but a slow charging speed; In some embodiments, Step S5 includes the following steps: Step S51: Take the optimal solution of each ant as the initial solution, and set the initial temperature T0, the termination temperature end and the cooling rate ; For example, set the initial parameters to take Solution A, Solution B, and Solution C as the initial solutions respectively, set the initial temperature T0 = 100, the termination temperature T end = 1, and the cooling rate = 0.9; Step S52: According to the high-power charging pile j of the current solution, calculate the comprehensive score S(j) of the high-power charging pile j; then randomly select a high-power charging pile in the neighborhood of the current solution, and calculate the comprehensive score of the high-power charging pile ; The comprehensive score S formula is expressed as: Wherein: are the weight coefficients of Safety, Speed, Price, and Distance respectively; Specifically, taking solution A as an example, a high-rate charging pile is randomly selected within its neighborhood, w1 = 0.3, w2 = 0.2, w3 = 0.2, w4 = 0.3, and the comprehensive score S1 of the high-rate charging pile of the current solution is calculated as 80; the comprehensive score S4 of a high-rate charging pile randomly selected within the neighborhood of the current solution is 82; Step S53: According to the Metropolis criterion, if , then accept the high-rate charging pile ; if , then calculate the acceptance probability , and the formula is expressed as: ; Wherein: T is the temperature; P M is the acceptance probability; Step S54: Update the temperature according to the temperature reduction strategy , and the formula is expressed as: ; Step S55: Repeat Step S52 - Step S54 until the termination temperature is reached, and the optimal solution after local search optimization for each ant is obtained as the result of local search optimization.

[0026] Specifically, the optimized solutions A', B', and C' are obtained as the results of local search optimization.

[0027] In some embodiments, Step S6 is specifically: combining the real-time needs of the user, selecting the solution with the highest matching degree as the plan to complete the high-rate charging pile task allocation; Specifically, the real-time needs of the user are that they hope the charging speed is as fast as possible and the price is not too high; the system evaluates the matching degree of the optimized solutions A', B', and C' according to the real-time needs of the user; Solution A': Recommend charging pile P4, with a general charging speed and a moderate price; Solution B': Recommend charging pile P5, with a fast charging speed and a slightly higher price; Solution C': Recommend charging pile P6, with a slow charging speed and a cheap price; The system calculates the matching degree of each solution with the user's needs and finds that the matching degree of Solution B' is the highest. Therefore, Solution B' is used as the final plan to complete the high-rate charging pile task allocation and recommend the high-rate charging pile to the user.

[0028] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm, characterized in that: The following steps are involved: Step S1: Pre-search based on the prediction results of the deep learning model of high-rate charging piles, filter high-rate charging piles with a danger probability greater than a threshold, and form a candidate set; Step S2: Based on the candidate set, a graph model is used to define a high-rate charging task set and a high-rate charging pile set for new energy vehicles; the high-rate charging task set includes a number of high-rate charging task nodes, and the high-rate charging pile set includes a number of high-rate charging pile nodes; Step S3: constructing a comprehensive cost function of high-rate charging task nodes and high-rate charging pile nodes, analyzing and streamlining the high-rate charging pile parameters in the candidate set through simulated pruning, and determining the weight coefficient of the comprehensive cost function based on the high-rate charging pile parameters after simulated pruning; Step S4: Conduct multiple rounds of searches for high-rate charging piles in the candidate set using an ant colony algorithm. The process of the ant colony algorithm includes initializing pheromones, simulating and building matching solutions, updating pheromones according to comprehensive costs, iterating, and obtaining the optimal solution. Step S5: Perform local search optimization on the optimal solution obtained by each ant; Step S6: Perform matching optimization in combination with real-time data to complete the allocation.

2. The method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm as claimed in claim 1, characterized in that: Step S1 specifically includes the following steps: Step S11: collect high-rate charging pile status data, environmental data and battery data, and then perform preprocessing, which includes data cleaning and feature engineering; the high-rate charging pile status data includes fault records, real-time temperature and power distribution; the environmental data includes temperature, humidity and fire protection facility status; the battery data includes health status, charge status and number of charge and discharge cycles; Step S12: Input the pre-processed high-rate charging pile data into the deep learning model for training. During the training process, the danger probability of the high-rate charging pile is predicted based on historical failure cases and environmental data. ; Step S13: Draw the ROC curve, calculate the difference between the recall rate and the false alarm rate, and calculate the optimized threshold of the danger probability. The formula is expressed as: ； Where: The vertical axis of the curve represents the proportion of fault samples correctly predicted as high risk by the deep learning model; The horizontal axis of the curve represents the proportion of normal samples that are incorrectly predicted as high-risk by the deep learning model; is the optimized threshold; is the value of the threshold Threshold corresponding to the maximum value obtained within the value range of the threshold Threshold; Step S14: Screening is performed according to the optimized threshold value to filter high-rate charging piles whose danger probability is greater than the optimized threshold value. The filtering formula is expressed as: ； When the high-rate charging pile is dangerous ≤ optimized threshold , the high-rate charging pile is considered safe and included in the candidate set; otherwise, the high-rate charging pile is considered dangerous and not included in the candidate set.

3. The method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm as claimed in claim 2, characterized in that: In step S2, the high-rate charging task set and high-rate charging pile graph model of new energy vehicles are represented as: G=(C, T, W, E); Where: T is the set of high-rate charging task nodes to be assigned to new energy vehicles; C is the set of high-rate charging pile nodes for new energy vehicles; E is the distance from the new energy vehicle to the high-rate charging pile or the edge connecting two high-rate charging pile nodes; W is the comprehensive cost of the new energy vehicle to reach the high-rate charging pile.

4. The method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm as claimed in claim 3, characterized in that: In step S2, a graph model is used to define a high-rate charging task set and a high-rate charging pile set for new energy vehicles, which specifically includes the following steps: Step S21: high rate charging task set: T=(t1, t2, ..., t n ), where t1, t2, ..., t n They are the first high-rate charging task node, the second high-rate charging task node and the nth high-rate charging task node respectively; the high-rate charging task node contains the following information: the high-rate charging task number, the high-rate charging task location and the charging amount required by the high-rate charging task; Step S22: High rate charging pile set: C=(c1,c2,...,c m ), where c1, c2, ..., c m They are the first high-rate charging pile node, the second high-rate charging pile node and the mth high-rate charging pile node respectively; the high-rate charging pile node contains the following information: the high-rate charging pile number, the high-rate charging pile location, the current load of the high-rate charging pile and the maximum load capacity of the high-rate charging pile.

5. The method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm as claimed in claim 4, characterized in that: Step S3 specifically includes the following steps: Step S31: Analyze and simplify the high-rate charging pile parameters in the high-rate charging pile candidate set by simulated pruning to remove unnecessary high-rate charging pile parameters in the candidate set. The simulated pruning operation can be expressed as: ； Among them, P is the parameter set, w i For parameters The weight, Q is the pruning threshold, P pruned It is the pruned parameter set, which only contains parameters whose weights are not less than the pruning threshold Q; Step S32: using a statistical method to evaluate the correlation or influence between each high-rate charging pile parameter after simulated pruning and the comprehensive cost function, so as to determine the weight coefficient of each high-rate charging pile parameter after simulated pruning on the comprehensive cost function, the statistical method including a correlation coefficient method or a regression analysis method; Step S33: construct a comprehensive cost function of high-rate charging task nodes and high-rate charging pile nodes, the formula is expressed as: ； Where: is the i-th high rate charging task node t i Use the jth high-rate charging pile node c j The overall cost of are weight coefficients, ; is the i-th high rate charging task node t i The current location to the jth high-rate charging pile node c j distance; is the i-th high rate charging task node t i Use the jth high-rate charging pile node c j Estimated high rate charging time; is the jth high rate charging pile node c j The current load.

6. The method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm as claimed in claim 5, characterized in that: Step S4 specifically includes the following steps: Step S41: Initialize the pheromone concentration of each pair of high-rate charging task node-high-rate charging pile node connection, the formula is expressed as: ； Where: For the i-th high rate charging task node t i To the jth high-rate charging pile node c j pheromone concentration, an indicator of the attractiveness of this path or the probability of selection; Step S42: Carry out multiple rounds of simulation search according to the weight coefficient of the comprehensive cost function. The specific steps of each round of simulation search are as follows: Preset the maximum number of iterations and pheromone threshold; Each ant randomly selects an unassigned high-rate charging task node and a high-rate charging pile node, and the formula is expressed as: ； Where: For the ant, select the i-th high-rate charging task node t i Match to the jth high rate charging pile node c j probability; represents the evaluation of the i-th high-rate charging task node t to be assigned based on other influencing factors or rules i and the jth high rate charging pile node c j The matching information; N is the i-th high-rate charging task node t i and the kth high-rate charging pile node c k A collection of; Update the correspondence between the selected high-rate charging task node and the high-rate charging pile node, and mark the high-rate charging task node as allocated; Step S43: Calculate the comprehensive cost of the current matching solution through the comprehensive cost function, and update the pheromone concentration according to the comprehensive cost. The formula is expressed as: ； In the formula, For the i-th high rate charging task node t i To the jth high-rate charging pile node c j pheromone concentration along the path; is the pheromone volatility coefficient; represents the remaining portion of the original pheromone concentration; For the i-th high rate charging task node t i To the jth high-rate charging pile node c j The pheromone increment of the path; Step S44: when the preset maximum number of iterations is reached or the pheromone concentration change rate is less than the preset pheromone threshold, the pheromone updating and pheromone simulation matching iterations are stopped, the ant matching of this round of simulation search is recorded as the optimal solution, and the weight coefficient of the comprehensive cost function of this round of simulation search is marked; Step S45: Repeat steps S43 and S44 until all weight coefficients of the comprehensive cost function are marked and the optimal solution for each ant is obtained, that is, the optimal solution set of multiple rounds of simulated search is finally obtained.

7. The method for allocating tasks of high-rate charging piles for new energy vehicles based on ant colony algorithm as claimed in claim 6, characterized in that: Step S5 specifically includes performing local search optimization on the optimal solution obtained by each ant using a simulated annealing algorithm, including the following steps: Step S51: taking the optimal solution of each ant as the initial solution, setting the initial temperature, the end temperature and the cooling rate; Step S52: Calculate the comprehensive score S(j) of the high-rate charging pile j according to the current solution; then randomly select a high-rate charging pile in the neighborhood of the current solution. , calculate high rate charging pile Overall rating ; The comprehensive score S formula is expressed as: Where: They are the weight coefficients of safety, charging speed, price and distance respectively; Step S53: According to the Metropolis criterion, if , then accept high rate charging pile ;if , then calculate the acceptance probability , the formula is: ； Where: T is temperature; P M is the probability of acceptance; Step S54: updating the temperature according to the cooling strategy; Step S55: Repeat steps S52 to S54 until the termination temperature is reached, and the optimal solution after local search optimization of each ant is obtained as the result of local search optimization.

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