Charging pile component inventory management method based on Harris eagle optimization algorithm

Abstract

The invention discloses a charging pile component inventory management method based on a Harris eagle optimization algorithm, and the method comprises the following steps: building an inventory management model, and defining a target function which aims at minimizing the cost; initializing parameters of the Harlins eagle optimization algorithm; generating an initial population; evaluating the population; updating the population, wherein an updating strategy comprises an exploration stage and a utilization stage; and terminating the algorithm and outputting an optimal solution and a fitness value. According to the charging pile part inventory management method based on the Harris eagle optimization algorithm, the Harris eagle optimization algorithm is applied to a power grid charging pile part management process, so that the power grid charging pile part management process algorithm can adapt to a complex working environment, and the task execution efficiency is improved; and risks of conflicts, waste and the like are reduced to the greatest extent, so that greater benefits and safety are brought to integrated and automatic power grid charging pile part management work.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence, and in particular relates to a charging pile component inventory management method based on the Harris Eagle optimization algorithm. Background Art

[0002] With the popularization of electric vehicles and the expansion of the market scale, the demand for charging pile parts has increased dramatically. In order to meet the growing user needs, the management and operational efficiency of charging pile parts have become particularly important. In the charging pile parts management warehouse of the power grid company, due to the current wide variety of charging pile parts and different usage scenarios, enterprises can save money and make better use of their resources by reasonably controlling the storage of charging pile parts materials. Poor timeliness and low efficiency are the result of the old model's inability to fully utilize the data and information stored in the materials of charging pile components. The problem of data utilization has become a major bottleneck restricting the leapfrog development of the material management development of power grid enterprises. If power grid enterprises can make full use of effective data in the procurement and production process, it will significantly improve the management level of charging pile components of power grid companies and reduce operating costs. The core of realizing a coordinated charging pile component management process is to effectively coordinate and optimize resources to meet the needs of multiple parties and improve overall efficiency, and to achieve goals such as how to optimize the order quantity of charging pile components and how to plan scheduling under special circumstances. At the same time, it is necessary to fully consider the efficiency and accuracy of the scheduling algorithm to reduce the waste of resources. Since the charging pile component management process involves many variables and constraints, the calculation difficulty of finding the optimal solution is high. Therefore, it is particularly important to develop an efficient optimization algorithm. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a charging pile component inventory management method based on the Harris Eagle optimization algorithm, which is applied to the power grid charging pile component management process, so that the power grid charging pile component management process algorithm can adapt to the complex working environment, improve the task execution efficiency, and minimize the risks of conflict and waste, thereby bringing greater benefits and safety to the integrated and automated power grid charging pile component management work.

[0004] The technical solution of the present invention is: a charging pile component inventory management method based on Harris Eagle optimization algorithm, comprising the following steps:

[0005] (1) Establishing an inventory management model and defining an objective function, wherein the objective of the objective function is to minimize costs;

[0006] (2) Initialize the parameters of the Harris Eagle optimization algorithm;

[0007] (3) Generate the initial population: Input inventory management data from 1 to 3 months to randomly generate a group of Harris Hawk individuals, each of which represents a potential solution;

[0008] (4) Evaluate the population: Calculate the fitness value of each Harris Hawk individual in step (3), use the fitness function to measure the performance of each Harris Hawk individual in a specific problem, and evaluate the quality of each Harris Hawk individual solution. The evaluation criteria for the quality of the individual solution are: in the current population, identify the Harris Hawk individual with the highest fitness value, and the position of the Harris Hawk individual is used as the position of the prey, and the position of the prey represents the best solution;

[0009] (5) Update the population: update the position of each eagle according to the position of the prey and the strategy of the eagle group. The update strategy includes: exploration phase and utilization phase. Exploration phase: when the eagle group is far away from the prey, it simulates the hunting behavior of Harris's hawks and conducts extensive search in the entire solution space. Utilization phase: when the eagle group surrounds the prey, it simulates the raid behavior of Harris's hawks and conducts in-depth search for good areas. The utilization phase includes four hunting methods: gentle encirclement, tight encirclement, accelerated dive-type gentle encirclement and accelerated dive-type tight encirclement.

[0010] (6) Terminate the algorithm and output the optimal solution and fitness value: If the maximum number of iterations or iteration accuracy is reached, the algorithm terminates and outputs the optimal solution and fitness value as the optimal management plan for the inventory management model; if the number of iterations or iteration accuracy is not reached, the Logistic-Tent mapping and Gaussian mutation strategy are introduced to return to step (4) and continue iterating until the number of iterations or iteration accuracy is reached and the optimal solution and fitness value are output as the optimal management plan for the inventory management model.

[0011] Furthermore, the objective function is expressed as:

[0012]

[0013] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D ij represents the initial inventory of the i-th material in the j-th batch, H i，j represents the ordering cost of the i-th material in the j-th batch, E i，j represents the inventory cost of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, L i，j Represents the scheduling cost of the i-th material in the j-th batch.

[0014] Furthermore, the objective function includes the following constraints:

[0015] (1) Each batch of orders must be completed and can only be completed once. The expression is:

[0016]

[0017] Where J represents the set of transport batches, j represents the transport batch and j∈J, N g，j represents the execution status of the jth batch in order g, where g represents the order number and g∈G, and G represents the order set;

[0018] (2) Update the resource supply limit constraints of charging pile components, which are expressed as follows:

[0019]

[0020] Where i represents the charging equipment component and i∈I, J represents the set of transport batches, j represents the transport batch and j∈J, d j represents the duration of transport batch j, V g，i represents the demand for material i by order g, O i，j represents the supply of material i in the jth batch, N g，j represents the decision variable;

[0021] (3) The initial inventory of the jth batch of orders is equal to the sum of the inventory at the end of the previous batch and the order quantity of the jth batch of orders. Its expression is:

[0022]

[0023] Among them, AD i，j represents the initial inventory of material i in batch j, Bd i，j-1 represents the inventory quantity of material i in batch j-1, CF i，j represents the purchase quantity of material i in the ,th batch, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts and i∈I, j represents transport batches and j∈J;

[0024] (4) The initial inventory of charging pile parts cannot exceed the storage space limit, which is expressed as:

[0025]

[0026] Among them, AD i，j represents the initial inventory of material i in batch , P lrepresents the storage space limit of the ,th type of material, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents a charging equipment part and i∈I, j represents a transport batch and j∈J;

[0027] (5) The inventory of charging pile parts cannot be zero. The expression is:

[0028]

[0029] Among them, AD i，j represents the initial inventory of material i in the jth batch, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, and j represents the transport batch;

[0030] (6) Constrain the priority of supplying charging pile components, and the expression is:

[0031]

[0032] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, j represents transport batches, and F i，j represents the order quantity of the i-th material in the j-th batch, P i represents the priority coefficient of material i, PL represents the priority limit threshold;

[0033] (7) Constrain the completion time of the charging pile component scheduling, and its expression is:

[0034]

[0035] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, j represents transport batches, T i，j represents the scheduling time of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, TK J represents the maximum scheduling time of the jth batch;

[0036] (8) The order quantity each time cannot exceed the maximum remaining capacity of the warehouse for that type of product, which is expressed as:

[0037]

[0038] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, MAX e Indicates the maximum remaining capacity of the warehouse;

[0039] (9) Limit the total amount of charging pile components used, and the expression is:

[0040]

[0041] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, g represents the order number and g∈G, G represents the order set, R g，i represents the demand for material i for order g, RU i represents the maximum supply of material i;

[0042] (10) The order quantity cannot exceed the maximum supply capacity, which is expressed as:

[0043]

[0044] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, C s Indicates the maximum supply capacity of the supplier;

[0045] (11) Ordering cost cannot exceed ordering budget, which is expressed as:

[0046]

[0047] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, B i Indicates order budget;

[0048] (12) The scheduling balance of charging pile components is expressed as:

[0049]

[0050] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, F i，j-1 represents the order quantity of the i-th material in the j-1 batch, M i It represents the maximum scheduling quantity of material i in adjacent time periods;

[0051] (13) All expenses cannot exceed the total budget, which is expressed as:

[0052]

[0053] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈J, j represents the transport batch and j∈J, Dij represents the initial inventory of the i-th material in the j-th batch, H i，j represents the ordering cost of the i-th material in the j-th batch, E i，j represents the inventory cost of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, L i，j represents the scheduling cost of the i-th material in the j-th batch, and C represents the total budget;

[0054] (14) The order quantity must be greater than or equal to the order demand, and its expression is:

[0055]

[0056] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, E i，j represents the inventory cost of the i-th material in the j-th batch, U i，j It represents the demand for material i in the jth batch;

[0057] (15) Time-demand fluctuation limit, which is expressed as:

[0058]

[0059] Where: I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, g represents the order number and g∈G, G represents the order set, E i，j represents the inventory cost of the i-th material in the j-th batch, Q i (g) represents the upper limit of demand for material i in order g;

[0060] (16) The charging pile parts warehouse must reserve a certain amount of inventory to cope with emergency demand, which is expressed as:

[0061]

[0062] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, D i，jrepresents the initial inventory of the i-th material in the j-th batch, V i represents the emergency demand reserve of material i;

[0063] (17) The scheduling cost cannot exceed the budget, and its expression is:

[0064]

[0065] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, K i，j represents the dispatch quantity of the i-th material in the j-th batch, L i，j represents the scheduling cost of the i-th material in the j-th batch, C k represents the scheduling budget;

[0066] (18) Inventory quantity, purchase quantity and dispatch quantity are defined as non-negative numbers, and their expressions are:

[0067]

[0068]

[0069] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D i，j represents the initial inventory of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch;

[0070] (19) Define the value range of the decision variable, which is expressed as:

[0071]

[0072] Among them, N g，j Represents the decision variable, J represents the set of transport batches, j represents the transport batch and j∈J, g represents the order number and g∈G, and G represents the set of orders.

[0073] Furthermore, the initialization parameter settings of the Harris Hawk optimization algorithm include: population size, number of iterations, initial value of escape energy, search space limit, position initialization, fitness function and termination condition. The population size range is 20-100, the number of iterations is 500, the initial value of escape energy is randomly selected in the range of (-1, 1), and the position initialization is specifically: the position of each Harris Hawk is randomly distributed in the search space. The expression of the fitness function is:

[0074]

[0075] Among them, x is the variable of the solution, f(x) is the objective function; the termination condition is: the maximum number of iterations or iteration accuracy, the maximum number of iterations is 500, and the iteration accuracy is specifically: the objective function value remains unchanged in several consecutive iterations.

[0076] Furthermore, the hunting strategy of the Harris Hawk optimization algorithm is expressed as:

[0077]

[0078] Among them, X(t+1) represents the position of Harris Hawk at the t+1th iteration, X rabbit (t) represents the position of the prey at the tth iteration, X(t) represents the position of the Harris hawk at the tth iteration, r1, r2, r3, r4, q are all random numbers between (0, 1), X rand (t) represents the random position of the Harris Hawk population, LB represents X rand (t), UB represents the upper bound of X rand The lower bound of (t), X m (t) represents the mean position of the Harris hawk population;

[0079] The expression for the mean position of the Harris Hawk population is:

[0080]

[0081] Among them, X i (t) represents the position of the i-th Harris Hawk at the t-th iteration, and N represents the total number of Harris Hawks;

[0082] The expression of the prey escape energy of the Harris Hawk optimization algorithm is:

[0083]

[0084] Among them, E represents the escape energy at the current moment, E0 represents the initial escape energy, T represents the maximum number of iterations of the algorithm, and t represents the current number of iterations;

[0085] The expression of the initial escape energy is:

[0086] E0 = 2 × rand-1,

[0087] Here, rand is a random number in the interval (0, 1).

[0088] Furthermore, the entry condition of the exploration stage is: the prey's escape energy E>1, the entry condition of the utilization stage is: the prey's escape energy E<1, the adoption condition of the gentle encirclement is: the prey's escape energy E<0.5 and the escape probability λ<0.5, the adoption condition of the tight encirclement is: the prey's escape energy E>0.5 and the escape probability λ<0.5, the adoption condition of the accelerated dive-type gentle encirclement is: the prey's escape energy E<0.5 and the escape probability λ greater than 0.5, the adoption condition of the accelerated dive-type tight encirclement is: the prey's escape energy E>0.5 and the escape probability λ>0.5.

[0089] Furthermore, the Harris Hawk position update expression under the mild surround is:

[0090] X(t+1)=ΔX(t)-E|JX rabbit (t)-X(t)| (26),

[0091] ΔX(t)=X rabbit (t)-X(t) (27)

[0092] Where X(t+1) represents the position of the Harris Hawk at the t+1th iteration, ΔX(t) represents the difference between the positions of the Harris Hawk and the prey at the iteration, J represents the random jumping force of the prey, E represents the escape energy at the current moment, X(t) represents the position of the Harris Hawk at the tth iteration, and X rabbit (t) represents the position of the prey at the tth iteration;

[0093] The expression of the prey random jumping force is:

[0094] J = 2(1-r5),

[0095] Where J represents the random jumping force of the prey, and r5 represents a random number in the interval (0, 1);

[0096] The Harris Hawk position update expression under the tight encirclement is:

[0097] X(t+1)=X rabbit (t)-E|ΔX(t)| (28)

[0098] Where X(t+1) represents the position of the Harris Hawk at the t+1th iteration, ΔX(t) represents the difference between the positions of the Harris Hawk and the prey at the iteration, E represents the escape energy at the current moment, and X rabbit (t) represents the position of the prey at the tth iteration;

[0099] The Harris Hawk position update expression under the accelerated dive gentle encirclement is:

[0100]

[0101] Y=X rabbit (t)-E|JX rabbit -X(t)| (29)

[0102] Z=Y+S×LF(D) (30)

[0103] Where X(t) represents the position of the Harris Hawk at the tth iteration, X(t+1) represents the position of the Harris Hawk at the t+1th iteration, Y represents the Harris Hawk’s current estimate or prediction of the prey’s position, and X rabbit (t) represents the position of the prey at the tth iteration, E represents the escape energy at the current moment, J represents the random jumping force of the prey, Z represents the updated prey position or predicted position, S represents the scaling factor, LF(D) represents the Levy flight function, and D represents the dimension of the solution space;

[0104] The expression of the Levy flight function is:

[0105]

[0106] Where x represents the step size parameter, μ and v both represent random numbers in (0, 1), β represents the distribution parameter controlling Levy flight and β = 1.5, and σ represents the standard deviation;

[0107] The Harris Hawk position update expression under the accelerated dive close encirclement is:

[0108]

[0109] Y=X rabbit (t)-E|JX rabbit (t)-X m (t)| (34),

[0110] Z=Y+S×LF(D) (35)

[0111] Where X(t) represents the position of the Harris Hawk at the tth iteration, X(t+1) represents the position of the Harris Hawk at the t+1th iteration, Y represents the Harris Hawk’s current estimate or prediction of the prey’s position, and X rabbit (t) represents the position of the prey at the tth iteration, E represents the escape energy at the current moment, J represents the random jumping force of the prey, Z represents the updated prey position or predicted position, S represents the scaling factor, LF(D) represents the Levy flight function, and D represents the dimension of the solution space;

[0112] The expression of the Levy flight function is:

[0113]

[0114] Wherein, x represents the step size parameter, μ and v both represent random numbers in (0, 1), β represents the distribution parameter controlling Levy flight and β=1.5, and σ represents the standard deviation.

[0115] Furthermore, an adaptive weight mechanism is introduced into the four hunting methods in the utilization stage, and the adaptive weight expression of the adaptive weight mechanism is:

[0116]

[0117] Where T represents the maximum number of iterations, t represents the current number of iterations, and ω represents the adaptive weight factor;

[0118] The expression for updating the prey position after introducing the adaptive weight mechanism is:

[0119] X′ rabbit =ω×X rabbit (37),

[0120] Among them, X rabbit represents the location of the prey, X rabbit Represents the updated prey position.

[0121] Furthermore, the expression of the Logistic-Tent mapping is:

[0122]

[0123] Among them, x i+1 represents the state of the mapping at step i+1, x i represents the state of the mapping at the i-th step, r represents the control parameter in the mapping, where the chaotic orbit state value range is (0, 1);

[0124] The expression of the Gaussian mutation strategy is:

[0125] X′=X+N(0,σ 2 ) (39),

[0126] Among them, X′ represents the new solution after mutation, σ represents the standard deviation, X represents the original solution, N(0,σ 2 ) means that the mean is 0 and the variance is σ 2 A random variable with a Gaussian distribution.

[0127] Beneficial effects of the present invention: The charging pile component inventory management method based on the Harris Hawk optimization algorithm of the present invention links the hunting strategy of the Harris Hawk optimization algorithm with the power grid charging pile component management process. In the power grid charging pile component management process, the charging pile component management is managed by multiple departments at the same time. Like the Harris Hawk, the power grid charging pile component management needs to be reasonably controlled to save money and make better use of its resources. By drawing on the hunting strategy and dynamic adaptability of the Harris Hawk, the charging pile component inventory management method can adapt to complex working environments, improve task execution efficiency, and minimize the risks of conflicts and waste, thereby bringing greater benefits and safety to the field of integrated and automated power grid charging pile component management; by introducing the adaptive weight mechanism into the Harris Hawk optimization algorithm, the algorithm's optimization performance and ability to avoid local optimality are enhanced, the algorithm's approach speed to the optimal solution is accelerated, and at the same time, the diversity of solutions is ensured to be maintained; by introducing The Logistic-Tent is mapped into the Harris Eagle optimization algorithm, which increases the uncertainty and randomness in the search process, improves the global search capability of the algorithm, prevents local optimality, accelerates the convergence speed, increases the diversity of the population, and has strong robustness and adaptability, thereby significantly improving the overall performance of the algorithm; in addition, by introducing the Gaussian mutation strategy into the Harris Eagle optimization algorithm, the exploration ability of the algorithm is significantly enhanced, and by introducing moderate randomness and diversity, it avoids falling into local optimality and improves the effect of global search; in short, the charging pile component inventory management method based on the Harris Eagle optimization algorithm of the present invention applies the Harris Eagle optimization algorithm to the power grid charging pile component management process, so that the power grid charging pile component management process algorithm can adapt to complex working environments, improve task execution efficiency, and minimize the risks of conflicts and waste, thereby bringing greater benefits and safety to the integrated and automated power grid charging pile component management work. BRIEF DESCRIPTION OF THE DRAWINGS

[0128] Figure 1 It is a schematic diagram of an algorithm flow of a charging pile component inventory management method based on the Harris Eagle optimization algorithm of the present invention.

[0129] Figure 2 It is a schematic diagram of a grid charging pile parts management process of a charging pile parts inventory management method based on the Harris Eagle optimization algorithm of the present invention. DETAILED DESCRIPTION

[0130] like Figure 2As shown in the figure, the management process of power grid charging pile parts is as follows: the management of power grid charging pile parts can be divided into four modules, namely, equipment management center, power grid equipment warehouse, power grid equipment use area and power grid equipment parts supplier. By installing a radio frequency identification system (RFID) on power grid equipment in the power grid equipment warehouse, the information of power grid equipment can be automatically identified to track the location and status of items in the supply chain in real time. In inventory management, inventory data can be updated in real time to improve logistics efficiency. Warehouse equipment information can be transmitted to the equipment management center in real time through the Internet. In the power grid equipment use area, through the Internet or a dedicated communication network, the equipment management center can remotely start, stop or adjust the operating parameters of the equipment, monitor the use of the equipment in real time, and quickly receive relevant alarms when the equipment fails, so that technicians can use remote connection technology to remotely diagnose and troubleshoot the equipment. As the hub of the entire process, the equipment management center plays a decisive role. The equipment management center uses information transmission technology to obtain the use of power grid equipment and power grid warehouse data in real time. It can coordinate the warehouse to distribute power grid equipment to the use site and place orders to power grid equipment suppliers in a timely manner to prevent warehouse goods shortages. In an emergency, the power grid equipment can be directly transported from the supplier to the demand site. In addition, a monitoring platform is set up in the equipment management center to display the operating status and parameters of the equipment in real time, support monitoring of multiple devices and multiple parameters, and set early warning rules through data analysis and machine learning algorithms. When the equipment parameters exceed the set threshold, an early warning message is automatically issued; the equipment management center has the ability to store equipment operation and maintenance records in the database, thereby accumulating historical data, and through the application of advanced big data analysis technology, in-depth mining of these data can be carried out to reveal the operating mode of the equipment and potential areas for improvement. Based on these analysis results, equipment operation and maintenance strategies can be formulated or adjusted to improve the efficiency of equipment use and extend its service life; the core of the equipment management center is to coordinate the entry and exit of equipment in the power grid equipment warehouse to minimize operating costs; at the same time, the ordering and use of equipment must meet the global optimality, avoid equipment delivery timeouts, and balance the workload of each area.

[0131] like Figure 1 As shown, a charging pile component inventory management method based on Harris Eagle optimization algorithm includes the following steps:

[0132] (1) Establishing an inventory management model and defining an objective function, wherein the objective of the objective function is to minimize costs;

[0133] (2) Initialize the parameters of the Harris Eagle optimization algorithm;

[0134] (3) Generate the initial population: Input inventory management data from 1 to 3 months to randomly generate a group of Harris Hawk individuals, each of which represents a potential solution;

[0135] (4) Evaluate the population: Calculate the fitness value of each Harris Hawk individual in step (3), use the fitness function to measure the performance of each Harris Hawk individual in a specific problem, and evaluate the quality of each Harris Hawk individual solution. The evaluation criteria for the quality of the individual solution are: in the current population, identify the Harris Hawk individual with the highest fitness value, and the position of the Harris Hawk individual is used as the position of the prey, and the position of the prey represents the best solution;

[0136] (5) Update the population: update the position of each eagle according to the position of the prey and the strategy of the eagle group. The update strategy includes: exploration phase and utilization phase. Exploration phase: when the eagle group is far away from the prey, it simulates the hunting behavior of Harris's hawks and conducts extensive search in the entire solution space. Utilization phase: when the eagle group surrounds the prey, it simulates the raid behavior of Harris's hawks and conducts in-depth search for good areas. The utilization phase includes four hunting methods: gentle encirclement, tight encirclement, accelerated dive-type gentle encirclement and accelerated dive-type tight encirclement.

[0137] (6) Terminate the algorithm and output the optimal solution and fitness value: If the maximum number of iterations or iteration accuracy is reached, the algorithm terminates and outputs the optimal solution and fitness value as the optimal management plan for the inventory management model; if the number of iterations or iteration accuracy is not reached, the Logistic-Tent mapping and Gaussian mutation strategy are introduced to return to step (4) and continue iterating until the number of iterations or iteration accuracy is reached and the optimal solution and fitness value are output as the optimal management plan for the inventory management model.

[0138] Preferably, the objective function is expressed as:

[0139]

[0140] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D ij represents the initial inventory of the i-th material in the j-th batch, H i，j represents the ordering cost of the i-th material in the j-th batch, E i，j represents the inventory cost of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, L i，j Represents the scheduling cost of the i-th material in the j-th batch.

[0141] Preferably, the objective function includes the following constraints:

[0142] (1) Each batch of orders must be completed and can only be completed once. The expression is:

[0143]

[0144] Where J represents the set of transport batches, j represents the transport batch and j∈J, N g，j represents the execution status of the jth batch in order g, where g represents the order number and g∈G, and G represents the order set;

[0145] (2) Update the resource supply limit constraints of charging pile components, which are expressed as follows:

[0146]

[0147] Where i represents the charging equipment component and i∈I, J represents the set of transport batches, j represents the transport batch and j∈J, d j represents the duration of transport batch j, V g，i represents the demand for material i by order g, O i，j represents the supply of material i in the jth batch, N g，j represents the decision variable;

[0148] (3) The initial inventory of the jth batch of orders is equal to the sum of the inventory at the end of the previous batch and the order quantity of the jth batch of orders. Its expression is:

[0149]

[0150] Among them, AD i，j represents the initial inventory of material i in batch j, Bd i，j-1 represents the inventory quantity of material i in batch j-1, CF i，j represents the purchase quantity of material i in the jth batch, I represents the set of all charging equipment parts, J represents the set of transportation batches, i represents charging equipment parts and i∈I, j represents transportation batches and j∈J;

[0151] (4) The initial inventory of charging pile parts cannot exceed the storage space limit, which is expressed as:

[0152]

[0153] Among them, AD i，j represents the initial inventory of material i in batch , P l represents the storage space limit of the ,th type of material, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents a charging equipment part and i∈I, j represents a transport batch and j∈J;

[0154] (5) The inventory of charging pile parts cannot be zero. The expression is:

[0155]

[0156] Among them, AD i，j represents the initial inventory of material i in the ,th batch, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, and j represents the transport batch;

[0157] (6) Constrain the priority of supplying charging pile components, and the expression is:

[0158]

[0159] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, j represents transport batches, and F i，j represents the order quantity of the i-th material in the j-th batch, P i represents the priority coefficient of material i, PL represents the priority limit threshold;

[0160] (7) Constrain the completion time of the charging pile component scheduling, and its expression is:

[0161]

[0162] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, j represents transport batches, T i，j represents the scheduling time of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, TK J represents the maximum scheduling time of the jth batch;

[0163] (8) The order quantity each time cannot exceed the maximum remaining capacity of the warehouse for that type of product, which is expressed as:

[0164]

[0165] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, MAX e Indicates the maximum remaining capacity of the warehouse;

[0166] (9) Limit the total amount of charging pile components used, and the expression is:

[0167]

[0168] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, g represents the order number and g∈G, G represents the order set, R g，i represents the demand for material i for order g, and RUi represents the maximum supply of material i;

[0169] (10) The order quantity cannot exceed the maximum supply capacity, which is expressed as:

[0170]

[0171] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, C s Indicates the maximum supply capacity of the supplier;

[0172] (11) Ordering cost cannot exceed ordering budget, which is expressed as:

[0173]

[0174] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, B i Indicates order budget;

[0175] (12) The scheduling balance of charging pile components is expressed as:

[0176]

[0177] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, F i，j-1 represents the order quantity of the i-th material in the j-1 batch, M i It represents the maximum scheduling quantity of material i in adjacent time periods;

[0178] (13) All expenses cannot exceed the total budget, which is expressed as:

[0179]

[0180] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈J, j represents the transport batch and j∈J, D ij represents the initial inventory of the i-th material in the j-th batch, H i，j represents the ordering cost of the i-th material in the j-th batch, E i，j represents the inventory cost of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, L i，j represents the scheduling cost of the i-th material in the j-th batch, and C represents the total budget;

[0181] (14) The order quantity must be greater than or equal to the order demand, and its expression is:

[0182]

[0183] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, E i，j represents the inventory cost of the i-th material in the j-th batch, U i，j It represents the demand for material i in the jth batch;

[0184] (15) Time-demand fluctuation limit, which is expressed as:

[0185]

[0186] Where: I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, g represents the order number and g∈G, G represents the order set, E i，j represents the inventory cost of the i-th material in the j-th batch, Q i (g) represents the upper limit of demand for material i in order g;

[0187] (16) The charging pile parts warehouse must reserve a certain amount of inventory to cope with emergency demand, which is expressed as:

[0188]

[0189] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, D i，j represents the initial inventory of the i-th material in the j-th batch, V i represents the emergency demand reserve of material i;

[0190] (17) The scheduling cost cannot exceed the budget, and its expression is:

[0191]

[0192] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, K i，j represents the dispatch quantity of the i-th material in the j-th batch, L i，j represents the scheduling cost of the i-th material in the j-th batch, C k represents the scheduling budget;

[0193] (18) Inventory quantity, purchase quantity and dispatch quantity are defined as non-negative numbers, and their expressions are:

[0194]

[0195]

[0196] Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D i，j represents the initial inventory of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch;

[0197] (19) Define the value range of the decision variable, which is expressed as:

[0198]

[0199] Among them, N g，j Represents the decision variable, J represents the set of transport batches, j represents the transport batch and j∈J, g represents the order number and g∈G, and G represents the set of orders.

[0200] Preferably, the initialization parameter settings of the Harris Hawk optimization algorithm include: population size, number of iterations, initial value of escape energy, search space limit, position initialization, fitness function and termination condition, the population size range is 20-100, the number of iterations is 500, the initial value of escape energy is randomly selected in the range of (-1, 1), the position initialization is specifically: the position of each Harris Hawk is randomly distributed in the search space, and the expression of the fitness function is:

[0201]

[0202] Among them, x is the variable of the solution, f(x) is the objective function; the termination condition is: the maximum number of iterations or iteration accuracy, the maximum number of iterations is 500, and the iteration accuracy is specifically: the objective function value remains unchanged in several consecutive iterations.

[0203] Preferably, the hunting strategy of the Harris Hawk optimization algorithm is expressed as:

[0204]

[0205] Among them, X(t+1) represents the position of Harris Hawk at the t+1th iteration, X rabbit (t) represents the position of the prey at the tth iteration, X(t) represents the position of the Harris hawk at the tth iteration, r1, r2, r3, r4, q are all random numbers between (0, 1), X rand (t) represents the random position of the Harris Hawk population, LB represents X rand (t), UB represents the upper bound of X rand The lower bound of (t), X m (t) represents the mean position of the Harris hawk population;

[0206] The expression for the mean position of the Harris Hawk population is:

[0207]

[0208] Among them, X i (t) represents the position of the i-th Harris Hawk at the t-th iteration, and N represents the total number of Harris Hawks;

[0209] The expression of the prey escape energy of the Harris Hawk optimization algorithm is:

[0210]

[0211] Among them, E represents the escape energy at the current moment, E0 represents the initial escape energy, T represents the maximum number of iterations of the algorithm, and t represents the current number of iterations;

[0212] The expression of the initial escape energy is:

[0213] E0 = 2 × rand-1,

[0214] Here, rand is a random number in the interval (0, 1).

[0215] Preferably, the entry condition of the exploration stage is: the prey's escape energy E>1, the entry condition of the utilization stage is: the prey's escape energy E<1, the adoption condition of the gentle encirclement is: the prey's escape energy E<0.5 and the escape probability λ<0.5, the adoption condition of the tight encirclement is: the prey's escape energy E>0.5 and the escape probability λ<0.5, the adoption condition of the accelerated dive-type gentle encirclement is: the prey's escape energy E<0.5 and the escape probability λ greater than 0.5, the adoption condition of the accelerated dive-type tight encirclement is: the prey's escape energy E>0.5 and the escape probability λ>0.5.

[0216] Preferably, the Harris Hawk position update expression under the mild surround is:

[0217] X(t+1)=ΔX(t)-E|JX rabbit (t)-X(t)| (26),

[0218] ΔX(t)=X rabbit (t)-X(t) (27)

[0219] Where X(t+1) represents the position of the Harris Hawk at the t+1th iteration, ΔX(t) represents the difference between the positions of the Harris Hawk and the prey at the iteration, J represents the random jumping force of the prey, E represents the escape energy at the current moment, X(t) represents the position of the Harris Hawk at the tth iteration, and X rabbit (t) represents the position of the prey at the tth iteration;

[0220] The expression of the prey random jumping force is:

[0221] J = 2(1-r5),

[0222] Where J represents the random jumping force of the prey, and r5 represents a random number in the interval (0, 1);

[0223] The Harris Hawk position update expression under the tight encirclement is:

[0224] X(t+1)=X rabbit (t)-E|ΔX(t)| (28)

[0225] Where X(t+1) represents the position of the Harris Hawk at the t+1th iteration, ΔX(t) represents the difference between the positions of the Harris Hawk and the prey at the iteration, E represents the escape energy at the current moment, and X rabbit (t) represents the position of the prey at the tth iteration;

[0226] The Harris Hawk position update expression under the accelerated dive gentle encirclement is:

[0227]

[0228] Y=X rabbit (t)-E|JX rabbit -X(t)| (29)

[0229] Z=Y+S×LF(D) (30)

[0230] Where X(t) represents the position of the Harris Hawk at the tth iteration, X(t+1) represents the position of the Harris Hawk at the t+1th iteration, Y represents the Harris Hawk’s current estimate or prediction of the prey’s position, and X rabbit (t) represents the position of the prey at the tth iteration, E represents the escape energy at the current moment, J represents the random jumping force of the prey, Z represents the updated prey position or predicted position, S represents the scaling factor, LF(D) represents the Levy flight function, and D represents the dimension of the solution space;

[0231] The expression of the Levy flight function is:

[0232]

[0233] Where x represents the step size parameter, μ and v both represent random numbers in (0, 1), β represents the distribution parameter controlling Levy flight and β = 1.5, and σ represents the standard deviation;

[0234] The Harris Hawk position update expression under the accelerated dive close encirclement is:

[0235]

[0236] Y=X rabbit (t)-E|JX rabbit (t)-X m (t)| (34),

[0237] Z=Y+S×LF(D) (35)

[0238] Where X(t) represents the position of the Harris Hawk at the tth iteration, X(t+1) represents the position of the Harris Hawk at the t+1th iteration, Y represents the Harris Hawk’s current estimate or prediction of the prey’s position, and X rabbit (t) represents the position of the prey at the tth iteration, E represents the escape energy at the current moment, J represents the random jumping force of the prey, Z represents the updated prey position or predicted position, S represents the scaling factor, LF(D) represents the Levy flight function, and D represents the dimension of the solution space;

[0239] The expression of the Levy flight function is:

[0240]

[0241] Wherein, x represents the step size parameter, μ and v both represent random numbers in (0, 1), β represents the distribution parameter controlling Levy flight and β=1.5, and σ represents the standard deviation.

[0242] Preferably, an adaptive weight mechanism is introduced into the four hunting methods in the utilization stage (since the scheduling and replenishment of charging pile materials need to consider factors such as the actual urgency and inventory quantity, it is necessary to introduce an adaptive weight mechanism into the Harris Eagle optimization algorithm), and the adaptive weight expression of the adaptive weight mechanism is:

[0243]

[0244] Where T represents the maximum number of iterations, t represents the current number of iterations, and ω represents the adaptive weight factor;

[0245] The expression for updating the prey position after introducing the adaptive weight mechanism is:

[0246] X′ rabbit =ω×X rabbit (37),

[0247] Among them, X rabbit represents the location of the prey, X rabbit Represents the updated prey position.

[0248] Preferably, the expression of the Logistic-Tent mapping (due to the wide variety of charging pile components of the power grid company and the complex application scenarios, the introduction of chaotic sequences can finely adjust the positions of individuals, improve the accuracy and efficiency of the search process, and thus find a better solution in a shorter time) is:

[0249]

[0250] Among them, x i+1 represents the state of the mapping at step i+1, x i represents the state of the mapping at the i-th step, r represents the control parameter in the mapping, where the chaotic orbit state value range is (0, 1);

[0251] The expression of the Gaussian mutation strategy is:

[0252] X′=X+N(0,σ 2 ) (39),

[0253] Among them, X′ represents the new solution after mutation, σ represents the standard deviation, X represents the original solution, N(0,σ 2 ) means that the mean is 0 and the variance is σ 2 A random variable with a Gaussian distribution.

[0254] Although the specific implementation of the present invention is introduced above in conjunction with the accompanying drawings, it does not limit the protection scope of the present invention. Those skilled in the art should understand that those non-creative modifications or equivalent replacements of the technical solution of the present invention, which do not depart from the protection scope of the technical solution, should be included in the scope of the claims of the present invention.

Claims

1. A charging pile component inventory management method based on Harris Eagle optimization algorithm, It is characterized in that The following steps are involved: (1) Establishing an inventory management model and defining an objective function, wherein the objective of the objective function is to minimize costs; (2) Initialize the parameters of the Harris Eagle optimization algorithm; (3) Generate the initial population: Input inventory management data from 1 to 3 months to randomly generate a group of Harris Hawk individuals, each of which represents a potential solution; (4) Evaluate the population: Calculate the fitness value of each Harris Hawk individual in step (3), use the fitness function to measure the performance of each Harris Hawk individual in a specific problem, and evaluate the quality of each Harris Hawk individual solution. The evaluation criteria for the quality of the individual solution are: in the current population, identify the Harris Hawk individual with the highest fitness value, and the position of the Harris Hawk individual is used as the position of the prey, and the position of the prey represents the best solution; (5) Update the population: update the position of each eagle according to the position of the prey and the strategy of the eagle group. The update strategy includes: exploration phase and utilization phase. Exploration phase: when the eagle group is far away from the prey, it simulates the hunting behavior of Harris's hawks and conducts extensive search in the entire solution space. Utilization phase: when the eagle group surrounds the prey, it simulates the raid behavior of Harris's hawks and conducts in-depth search for good areas. The utilization phase includes four hunting methods: gentle encirclement, tight encirclement, accelerated dive-type gentle encirclement and accelerated dive-type tight encirclement. (6) Terminate the algorithm and output the optimal solution and fitness value: If the maximum number of iterations or iteration accuracy is reached, the algorithm terminates and outputs the optimal solution and fitness value as the optimal management plan for the inventory management model; if the number of iterations or iteration accuracy is not reached, the Logistic-Tent mapping and Gaussian mutation strategy are introduced to return to step (4) and continue iterating until the number of iterations or iteration accuracy is reached and the optimal solution and fitness value are output as the optimal management plan for the inventory management model.

2. According to claim 1, a charging pile component inventory management method based on Harris Eagle optimization algorithm, It is characterized in that The expression of the objective function is: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D ij represents the initial inventory of the i-th material in the j-th batch, H i，j represents the ordering cost of the i-th material in the j-th batch, E i，j represents the inventory cost of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, L i，j Represents the scheduling cost of the i-th material in the j-th batch.

3. According to the charging pile component inventory management method based on Harris Eagle optimization algorithm according to claim 1, It is characterized in that The objective function includes the following constraints: (1) Each batch of orders must be completed and can only be completed once. The expression is: Where J represents the set of transport batches, j represents the transport batch and j∈J, N g，j represents the execution status of the jth batch in order g, where g represents the order number and g∈G, and G represents the order set; (2) Update the resource supply limit constraints of charging pile components, which are expressed as follows: Where i represents the charging equipment component and i∈I, J represents the set of transport batches, j represents the transport batch and j∈J, d j represents the duration of transport batch j, V g，i represents the demand for material i by order g, O i，j represents the supply of material i in the jth batch, N g，j represents the decision variable; (3) The initial inventory of the jth batch of orders is equal to the sum of the inventory at the end of the previous batch and the order quantity of the jth batch of orders. Its expression is: Among them, AD i，j represents the initial inventory of material i in batch j, Bd i，j-1 represents the inventory quantity of material i in batch j-1, CF i，j represents the purchase quantity of material i in the jth batch, I represents the set of all charging equipment parts, J represents the set of transportation batches, i represents charging equipment parts and i∈I, j represents transportation batches and j∈J; (4) The initial inventory of charging pile parts cannot exceed the storage space limit, which is expressed as: Among them, AD i，j represents the initial inventory of material i in batch j, P l represents the storage space limit of the ,th type of material, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents a charging equipment part and i∈I, j represents a transport batch and j∈J; (5) The inventory of charging pile parts cannot be zero. The expression is: Among them, AD i，j represents the initial inventory of material i in the jth batch, I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, and j represents the transport batch; (6) Constrain the priority of supplying charging pile components, and the expression is: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, j represents transport batches, and F i，j represents the order quantity of the i-th material in the j-th batch, P i represents the priority coefficient of material i, PL represents the priority limit threshold; (7) Constrain the completion time of the charging pile component scheduling, and its expression is: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents charging equipment parts, j represents transport batches, T i，j represents the scheduling time of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, TK J represents the maximum scheduling time of the jth batch; (8) The order quantity each time cannot exceed the maximum remaining capacity of the warehouse for that type of product, which is expressed as: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, MAX e Indicates the maximum remaining capacity of the warehouse; (9) Limit the total amount of charging pile components used, and the expression is: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, g represents the order number and g∈G, G represents the order set, R g，i represents the demand for material i for order g, RU j represents the maximum supply of material i; (10) The order quantity cannot exceed the maximum supply capacity, which is expressed as: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, C s Indicates the maximum supply capacity of the supplier; (11) Ordering cost cannot exceed ordering budget, which is expressed as: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, B i Indicates order budget; (12) The scheduling balance of charging pile components is expressed as: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, F i，j represents the order quantity of the i-th material in the j-th batch, F i，j-1 represents the order quantity of the i-th material in the j-1 batch, M i It represents the maximum scheduling quantity of material i in adjacent time periods; (13) All expenses cannot exceed the total budget, which is expressed as: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D ij represents the initial inventory of the i-th material in the j-th batch, H i，j represents the ordering cost of the i-th material in the j-th batch, E i，j represents the inventory cost of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, L i，j represents the scheduling cost of the i-th material in the j-th batch, and C represents the total budget; (14) The order quantity must be greater than or equal to the order demand, and its expression is: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, E i，j represents the inventory cost of the i-th material in the j-th batch, U i，j It represents the demand for material i in the jth batch; (15) Time-demand fluctuation limit, which is expressed as: Where: I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, g represents the order number and g∈G, G represents the order set, E i，j represents the inventory cost of the i-th material in the j-th batch, Q i (g) represents the upper limit of demand for material i in order g; (16) The charging pile parts warehouse must reserve a certain amount of inventory to cope with emergency demand, which is expressed as: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, D i，j represents the initial inventory of the i-th material in the j-th batch, V i represents the emergency demand reserve of material i; (17) The scheduling cost cannot exceed the budget, and its expression is: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, K i，j represents the dispatch quantity of the i-th material in the j-th batch, L i，j represents the scheduling cost of the i-th material in the j-th batch, C k represents the scheduling budget; (18) Inventory quantity, purchase quantity and dispatch quantity are defined as non-negative numbers, and their expressions are: Where I represents the set of all charging equipment parts, J represents the set of transport batches, i represents the charging equipment parts and i∈I, j represents the transport batch and j∈J, D i，j represents the initial inventory of the i-th material in the j-th batch, F i，j represents the order quantity of the i-th material in the j-th batch, K i，j represents the dispatch quantity of the i-th material in the j-th batch; (19) Define the value range of the decision variable, which is expressed as: Among them, N g，j Represents the decision variable, J represents the set of transport batches, j represents the transport batch and j∈J, g represents the order number and g∈G, and G represents the set of orders.

4. According to claim 1, a charging pile component inventory management method based on Harris Eagle optimization algorithm, It is characterized in that The initialization parameter settings of the Harris Hawk optimization algorithm include: population size, number of iterations, initial value of escape energy, search space limit, position initialization, fitness function and termination condition. The population size range is 20-100, the number of iterations is 500, the initial value of escape energy is randomly selected in the range of (-1, 1), and the position initialization is specifically: the position of each Harris Hawk is randomly distributed in the search space. The expression of the fitness function is: Among them, x is the variable of the solution, f(x) is the objective function; the termination condition is: the maximum number of iterations or iteration accuracy, the maximum number of iterations is 500, and the iteration accuracy is specifically: the objective function value remains unchanged in several consecutive iterations.

5. According to claim 1, a charging pile component inventory management method based on Harris Eagle optimization algorithm, It is characterized in that The hunting strategy of the Harris Hawk optimization algorithm is expressed as: Among them, X(t+1) represents the position of Harris Hawk at the t+1th iteration, X rabbit (t) represents the position of the prey at the tth iteration, X(t) represents the position of the Harris hawk at the tth iteration, and r 1 , r 2 , r 3 , r 4 , q are all random numbers between (0, 1), X rand (t) represents the random position of the Harris Hawk population, LB represents X rand (t), UB represents the upper bound of X rand The lower bound of (t), X m (t) represents the mean position of the Harris hawk population; The expression for the mean position of the Harris Hawk population is: Among them, X i (t) represents the position of the i-th Harris Hawk at the t-th iteration, and N represents the total number of Harris Hawks; The expression of the prey escape energy of the Harris Hawk optimization algorithm is: Among them, E represents the escape energy at the current moment, E 0 represents the initial escape energy, T represents the maximum number of iterations of the algorithm, and t represents the current number of iterations; The expression of the initial escape energy is: AND 0 =2×rand-1, Here, rand is a random number in the interval (0, 1).

6. A charging pile component inventory management method based on Harris Eagle optimization algorithm according to claim 1, It is characterized in that The entry condition of the exploration stage is: the prey's escape energy E>1, the entry condition of the utilization stage is: the prey's escape energy E<1, the adoption condition of the gentle encirclement is: the prey's escape energy E<0.5 and the escape probability λ<0.5, the adoption condition of the tight encirclement is: the prey's escape energy E>0.5 and the escape probability λ<0.5, the adoption condition of the accelerated dive-type gentle encirclement is: the prey's escape energy E<0.5 and the escape probability λ greater than 0.5, the adoption condition of the accelerated dive-type tight encirclement is: the prey's escape energy E>0.5 and the escape probability λ>0.

5.

7. The charging pile component inventory management method based on Harris Eagle optimization algorithm according to claim 1, It is characterized in that The Harris Hawk position update expression under the mild encirclement is: X(t+1)=ΔX(t)-E|JX rabbit (t)-X(t)| (26), ΔX(t)=X rabbit (t)-X(t) (27), Where X(t+1) represents the position of the Harris Hawk at the t+1th iteration, ΔX(t) represents the difference between the positions of the Harris Hawk and the prey at the iteration, J represents the random jumping force of the prey, E represents the escape energy at the current moment, X(t) represents the position of the Harris Hawk at the tth iteration, and X rabbit (t) represents the position of the prey at the tth iteration; The expression of the random jumping force of the prey is: J＝2(1-r 5 )， Where J represents the random jumping force of the prey, r 5 Represents a random number in the interval (0, 1); The Harris Hawk position update expression under the tight encirclement is: X(t+1)=X rabbit (t)-E|ΔX(t)| (28), Where X(t+1) represents the position of the Harris Hawk at the t+1th iteration, ΔX(t) represents the difference between the positions of the Harris Hawk and the prey at the iteration, E represents the escape energy at the current moment, and X rabbit (t) represents the position of the prey at the tth iteration; The Harris Hawk position update expression under the accelerated dive gentle encirclement is: Y=X rabbit (t)-E|JX rabbit -X(t)| (29), Z=Y+S×LF(D) (30), Where X(t) represents the position of the Harris Hawk at the tth iteration, X(t+1) represents the position of the Harris Hawk at the t+1th iteration, Y represents the Harris Hawk’s current estimate or prediction of the prey’s position, and X rabbit (t) represents the position of the prey at the tth iteration, E represents the escape energy at the current moment, J represents the random jumping force of the prey, Z represents the updated prey position or predicted position, S represents the scaling factor, LF(D) represents the Levy flight function, and D represents the dimension of the solution space; The expression of the Levy flight function is: Where x represents the step size parameter, μ and v both represent random numbers in (0, 1), β represents the distribution parameter controlling Levy flight and β = 1.5, and σ represents the standard deviation; The Harris Hawk position update expression under the accelerated dive close encirclement is: Y=X rabbit (t)-E|JX rabbit (t)-X m (t)| (34), Z=Y+S×LF(D) (35) Where X(t) represents the position of the Harris Hawk at the tth iteration, X(t+1) represents the position of the Harris Hawk at the t+1th iteration, Y represents the Harris Hawk’s current estimate or prediction of the prey’s position, and X rabbit (t) represents the position of the prey at the tth iteration, E represents the escape energy at the current moment, J represents the random jumping force of the prey, Z represents the updated prey position or predicted position, S represents the scaling factor, LF(D) represents the Levy flight function, and D represents the dimension of the solution space; The expression of the Levy flight function is: Wherein, x represents the step size parameter, μ and v both represent random numbers in (0, 1), represents the distribution parameter controlling Levy flight and β=1.5, and σ represents the standard deviation.

8. The charging pile component inventory management method based on Harris Eagle optimization algorithm according to claim 1, It is characterized in that An adaptive weight mechanism is introduced into the four hunting methods in the utilization stage, and the adaptive weight expression of the adaptive weight mechanism is: Where T represents the maximum number of iterations, t represents the current number of iterations, and ω represents the adaptive weight factor; The expression for updating the prey position after introducing the adaptive weight mechanism is: X rabbit =ω×X rabbit (37), Among them, X rabbit represents the location of the prey, X rabbit Represents the updated prey position.

9. The charging pile component inventory management method based on Harris Eagle optimization algorithm according to claim 1, It is characterized in that The expression of the Logistic-Tent mapping is: Among them, x i+1 represents the state of the mapping at step i+1, x i represents the state of the mapping at the i-th step, r represents the control parameter in the mapping, where the chaotic orbit state value range is (0, 1); The expression of the Gaussian mutation strategy is: X′=X+N(0,σ 2 ) (39), Among them, X′ represents the new solution after mutation, σ represents the standard deviation, X represents the original solution, N(0,σ 2 ) means that the mean is 0 and the variance is σ 2 A random variable with a Gaussian distribution.

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