Intelligent guided vehicle charging control method, system and medium based on fuzzy algorithm

Abstract

The present invention proposes a charging control method for intelligent guided vehicles based on a fuzzy algorithm, comprising: S1, obtaining four types of IGV charging influencing factors, which are IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance; S2, establishing membership functions corresponding to the four types of IGV charging influencing factors; S3, dynamically evaluating the IGV's charging demand through a fuzzy algorithm based on the membership function obtained in S2; S4, controlling the designated IGV to go to a designated charging area for charging based on the IGV's charging demand. The present invention further proposes a computer-readable storage medium that applies the above-mentioned intelligent guided vehicle charging control method, and proposes an intelligent guided vehicle charging control system based on a fuzzy algorithm. The present invention improves the charging efficiency of the port's intelligent guided vehicle group and the utilization rate of the intelligent guided vehicle group in port operations, thereby significantly improving the working efficiency of the IGV and the operational efficiency of the entire terminal.

Description

Technical Field

[0001] The present invention relates to the technical field of charging control for intelligent guided vehicles, and in particular to a charging control method, system and medium for intelligent guided vehicles based on a fuzzy algorithm. Background Art

[0002] Intelligent Guided Vehicles (IGVs) are one of the core devices responsible for horizontal transportation in automated container terminals and undertake multiple critical transportation tasks. In the operation scenario of an automated terminal, IGVs involve interactions with multiple devices and systems, including automatic interaction with quay cranes, automatic interaction with rail cranes, interaction with charging systems, position confirmation between other IGVs, and human-machine interaction when removing and installing locks. In particular, interaction with the charging system is a fundamental link to ensure the continuous operation of IGVs. Therefore, it can be seen that IGVs interact in complex ways with numerous devices and are at a key position in terminal operations. Improving their work efficiency and operating time is crucial to overall terminal efficiency.

[0003] Currently, the IGV charging strategy used at ports is relatively simple, deciding whether to charge based solely on the vehicle's remaining battery charge. While this approach can meet basic operational requirements, it fails to fully consider factors such as the urgency of the task, the availability of charging stations, and the real-time system load. This results in charging resource utilization efficiency far from being maximized, leaving significant room for improvement. Furthermore, general fuzzy control methods fail to carefully consider specific operating conditions and instead employ relatively simple linear control, resulting in insufficient and irrational resource utilization. For example, the simplicity of existing IGV charging control strategies prevents the maximization of existing port operating resources, prevents accurate charging adjustments based on on-site operating conditions, and results in inaccurate control and inadequate and inappropriate resource utilization.

[0004] In existing technology, the VMS (vehicle management system) reads the real-time power level of each IGV and determines whether the battery level is less than 30%. If so, the VMS sends a charging instruction to the IGV. After receiving the instruction, the IGV selects the nearest available charging station to charge. If not, charging does not proceed. This IGV charging control strategy is relatively simple and does not effectively utilize idle charging stations. When the demand for IGVs increases, there may be a shortage of available IGVs, affecting the transportation efficiency of the terminal.

[0005] In order to improve the overall efficiency of the terminal, there is an urgent need for a more intelligent IGV charging strategy that can make dynamic decisions based on multiple factors. It is necessary not only to optimize the charging timing but also to ensure that the IGV can continue to operate efficiently under high-load operating conditions. Summary of the Invention

[0006] In view of this, it is necessary to propose a charging control method, system and medium for intelligent guided vehicles based on fuzzy algorithm to address the above problems, so as to overcome several shortcomings in the above background technology and solve the following technical problems: how to improve the charging efficiency of the port intelligent guided vehicle group and the utilization rate of the intelligent guided vehicle group in port operations.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention proposes a charging control method for an intelligent guided vehicle based on a fuzzy algorithm, which includes the following steps:

[0009] Step S1, obtaining four types of IGV charging influencing factors, which are IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance;

[0010] Step S2: Establish membership functions corresponding to the four types of IGV charging influencing factors, thereby obtaining the IGV battery power membership function, the IGV task status membership function, the IGV real-time operation demand membership function, and the IGV charging distance membership function;

[0011] Step S3, dynamically evaluating the charging demand of the IGV using a fuzzy algorithm based on the membership function obtained in step S2;

[0012] Step S4: Control the designated IGV to go to the designated charging area for charging according to the charging demand of the IGV.

[0013] Furthermore, before step S1, the control method further includes the following steps:

[0014] Step S100, reading the current power of the IGV. When the current power of the IGV is within a range, step S1 is executed.

[0015] Furthermore, between step S2 and step S3, the control method further includes the following steps:

[0016] Step S23: Determine the distribution weight of each IGV charging influencing factor according to a hierarchical analysis method; then execute step S3.

[0017] Furthermore, step S3 includes the following sub-steps:

[0018] Step S31, obtaining the membership degrees corresponding to the four types of IGV charging influencing factors according to the membership function obtained in step S2;

[0019] Step S32, establishing a fuzzy relationship matrix R using the membership degree obtained in step S31;

[0020] In step S33 , the fuzzy relationship matrix R is converted into a judgment matrix according to the weight distribution of the corresponding IGV charging influencing factors, and the charging demand of the IGV is obtained from the judgment matrix.

[0021] Furthermore, in step S32 , a fuzzy relationship matrix R is established by formulating a membership table.

[0022] Furthermore, the range value is 30% to 70% of the full charge of the IGV;

[0023] In step S2, the membership degree of the IGV battery power membership function is

[0024]

[0025] In formula (1), u1(x) is the correlation between the battery power and the charging demand, and x is the battery power.

[0026] Furthermore, in step S2, the membership degree of the IGV task state membership function is

[0027]

[0028] In formula (2), when the corresponding IGV task state is no task, the membership degree of the corresponding IGV task state membership function is 1, and when the corresponding IGV task state is a task, the membership degree of the corresponding IGV task state membership function is 0.

[0029] Furthermore, in step S2, the real-time operation demand of IGVs is used to reflect the number of IGVs used in terminal operations at that time. When the real-time operation demand of IGVs corresponding to the state of having vehicles but no task arrangements is greater than a first value, the membership degree of the membership function of the corresponding IGV real-time operation demand is set to 1; when the real-time operation demand of IGVs corresponding to the state of having no vehicles and having task arrangements is greater than a second value and less than the first value, the corresponding IGV operation is given priority in production; when the real-time operation demand of IGVs corresponding to the state of having no vehicles and having task arrangements is greater than 0 and less than the second value, and the membership function of the corresponding IGV real-time operation demand decreases exponentially, the corresponding IGV operation is given priority in charging.

[0030] The present invention also provides an intelligent guided vehicle charging control system based on fuzzy algorithm, comprising:

[0031] The IGV charging influencing factor acquisition module is used to obtain four types of IGV charging influencing factors: IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance;

[0032] The membership function establishment module is used to establish the membership functions corresponding to the four types of IGV charging influencing factors, thereby obtaining the IGV battery power membership function, the IGV task status membership function, the IGV real-time operation demand membership function, and the IGV charging distance membership function;

[0033] A charging demand dynamic evaluation module is used to establish a module membership function based on the membership function and dynamically evaluate the charging demand of IGVs through a fuzzy algorithm;

[0034] The IGV control module is used to control the designated IGV to go to the designated charging area for charging according to the charging demand determined by the charging demand dynamic evaluation module.

[0035] The present invention further proposes a computer-readable storage medium, which stores a computer program, characterized in that when the computer program is executed by a processor, the steps of the intelligent guided vehicle charging control method based on fuzzy algorithm as described in any one of the above items are implemented.

[0036] The beneficial effects of the present invention are:

[0037] The present invention improves the charging efficiency of the port's intelligent guided vehicle fleet and its utilization rate in port operations, thereby significantly improving the working efficiency of the IGVs and the operational efficiency of the entire terminal. The present invention also has the following advantages: it can make dynamic decisions based on multiple factors; it can be easily deployed on a large scale at a low cost; it makes the IGV charging control more precise and the calculated results more realistic. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a workflow diagram of the intelligent guided vehicle charging control method based on fuzzy algorithm of the present invention;

[0039] Figure 2 A graph showing the relationship between the open circuit voltage and the state of charge of an IGV cell according to the present invention;

[0040] Figure 3 A coordinate diagram of the IGV battery power membership function involved in the present invention;

[0041] Figure 4 This is a coordinate diagram of the IGV real-time operation demand membership function involved in the present invention. DETAILED DESCRIPTION

[0042] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be further clearly and completely described below in conjunction with the embodiments of the present invention. It should be noted that the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] It should be understood that the directions or positional relationships indicated by terms such as "up", "down", "front", "back", "left", and "right" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0044] Terms such as "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the quantity of the technical features indicated. Therefore, the definition of "first," "second," "third," and "fourth" may explicitly or implicitly include one or more of such features.

[0045] Example 1

[0046] like Figure 1 As shown:

[0047] This embodiment proposes a charging control method for an intelligent guided vehicle based on a fuzzy algorithm, comprising the following steps:

[0048] Step S1, obtaining four types of IGV charging influencing factors, which are IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance;

[0049] Step S2: Establish membership functions corresponding to the four types of IGV charging influencing factors, thereby obtaining the IGV battery power membership function, the IGV task status membership function, the IGV real-time operation demand membership function, and the IGV charging distance membership function;

[0050] Step S3, dynamically evaluating the charging demand of the IGV using a fuzzy algorithm based on the membership function obtained in step S2;

[0051] Step S4: Control the designated IGV to go to the designated charging area for charging according to the charging demand of the IGV.

[0052] The fuzzy algorithm-based intelligent guided vehicle charging control method of this embodiment improves the charging strategy by incorporating factors such as battery power, task priority, charging pile idle rate, and operational demand into the decision-making system. This significantly improves the operating efficiency of IGVs, thereby enhancing the operational efficiency of the entire terminal. It also utilizes fuzzy algorithm technology, which has the advantages of low investment cost, easy deployment, and simple subsequent maintenance. It can make dynamic decisions based on multiple factors, ensuring that IGVs can continue to operate efficiently under high-load operating conditions. The fuzzy algorithm-based intelligent guided vehicle charging control method of this embodiment fully considers the distance between the IGV and the charging pile, reducing the mileage the IGV travels to the charging pile. After adjusting the control method of this embodiment, the natural box capacity of each charging operation at the port increased by approximately double, while the power consumption per standard box was also reduced. It can also make dynamic port decisions based on multiple factors, enabling low-cost, large-scale, and simple deployment of port IGVs, more precise IGV group control, and realistic calculation results. The actual results can be seen in Table 1 below. The test data for week 41 in Table 1 represents port monitoring data without implementing the technical solution of the present invention, and the test data for week 43 represents port monitoring data after implementing the technical solution of the present invention for two weeks.

[0053]

[0054] Table 1

[0055] In Table 1, statistics show that compared with the 41 weeks before the adjustment, the number of charging times for 100,000 boxes (IGV operation TEU) decreased from 2,609 times to about 1,740 times in the 43rd and 44th weeks after the adjustment using this solution, a decrease of 33.3%, indicating that this solution can reduce the number of IGV charging times and improve operating efficiency; the mileage to the charging pile decreased from 1,043.6 km to about 652.9 km, a decrease of 37.4%, indicating that this solution fully considers the distance between the IGV and the charging pile, and the charging strategy adopted effectively saves the mileage of the IGV to the charging pile. After the adjustment, the natural box of each charging operation increased by about one-fold, and the power consumption per standard box was also reduced. By comparing the main parameters of IGV charging before and after the adjustment, the technical effect of the present invention is proved.

[0056] Optimally, before step S1, the control method further includes the following steps:

[0057] Step S100, reading the current power of the IGV. When the current power of the IGV is within a range, step S1 is executed.

[0058] Optimally, between step S2 and step S3, the control method further includes the following steps:

[0059] In step S23 , which is executed after step S2 , the weights of the respective IGV charging influencing factors are determined according to a hierarchical analysis method; and then step S3 is executed.

[0060] Specifically, the control method is a complex charging control scheme that combines charging decisions when the battery is low on power, priority scheduling of task status, dynamic balance between real-time job demand and charging demand, optimization of charging pile idle rate, multi-factor weight setting, and integration of fuzzy reasoning and control strategies.

[0061] Optimally, step S3 includes the following sub-steps:

[0062] Step S31, obtaining the membership degrees corresponding to the four types of IGV charging influencing factors according to the membership function obtained in step S2;

[0063] Step S32, establishing a fuzzy relationship matrix R using the membership degree obtained in step S31;

[0064] In step S33 , the fuzzy relationship matrix R is converted into a judgment matrix according to the weight distribution of the corresponding IGV charging influencing factors, and the charging demand of the IGV is obtained from the judgment matrix.

[0065] Optimally, in step S32 , a fuzzy relationship matrix R is established by formulating a membership table.

[0066] Optimally, the range is 30% to 70% of the full charge of the IGV;

[0067] In step S2, the membership degree of the IGV battery power membership function is

[0068]

[0069] In formula (1), u1(x) is the correlation between the battery power and the charging demand, and x is the battery power.

[0070] Specifically, the control method of this embodiment can make an "S"-shaped IGV battery power membership function based on the actual vehicle power reduction situation, which is more practical than ordinary linear control and can fully consider operational needs. When the demand vehicle gap is larger, charging is less urgent.

[0071] like Figure 3 As shown in Figure 1, the IGV battery power membership function is represented by an "S" type function, which meets the actual situation that the lower the battery power, the more it needs to be charged.

[0072] Optimally, in step S2, the membership degree of the IGV task state membership function is

[0073]

[0074] In formula (2), when the corresponding IGV task state is no task, the membership degree of the corresponding IGV task state membership function is 1, and when the corresponding IGV task state is a task, the membership degree of the corresponding IGV task state membership function is 0.

[0075] Optimally, in step S2, the real-time operation demand of IGV is used to reflect the number of IGVs used in the terminal operation at that time. When the real-time operation demand of IGV corresponding to the state of having a vehicle but no task arrangement is greater than a first value, the membership degree of the membership function of the corresponding IGV real-time operation demand is set to 1; when the real-time operation demand of IGV corresponding to the state of having no vehicle and having a task arrangement is greater than a second value, the corresponding IGV operation is given priority in production; when the real-time operation demand of IGV corresponding to the state of having no vehicle and having a task arrangement is greater than 0 and less than the second value, and the membership function of the corresponding IGV real-time operation demand decreases exponentially, the corresponding IGV operation is given priority in charging.

[0076] Specifically, the real-time operation demand of IGV fully considers the actual operation needs, and the membership degree decreases nonlinearly, which is in line with the actual situation of "operation priority when there are too few vehicles".

[0077] Example 2

[0078] This embodiment proposes a charging control system for an intelligent guided vehicle based on a fuzzy algorithm, including:

[0079] The IGV charging influencing factor acquisition module is used to obtain four types of IGV charging influencing factors: IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance;

[0080] The membership function establishment module is used to establish the membership functions corresponding to the four types of IGV charging influencing factors, thereby obtaining the IGV battery power membership function, the IGV task status membership function, the IGV real-time operation demand membership function, and the IGV charging distance membership function;

[0081] A charging demand dynamic evaluation module is used to establish a module membership function based on the membership function and dynamically evaluate the charging demand of IGVs through a fuzzy algorithm;

[0082] The IGV control module is used to control the designated IGV to go to the designated charging area for charging according to the charging demand determined by the charging demand dynamic evaluation module.

[0083] Example 3

[0084] This embodiment proposes a computer-readable storage medium, which stores a computer program, characterized in that when the computer program is executed by a processor, the steps of the intelligent guided vehicle charging control method based on fuzzy algorithm as described in any one of Example 1 are implemented.

[0085] Example 4

[0086] Example 4 is an optimized design of any technical solution in Example 1;

[0087] The IGV battery charge membership function curve can use a linear function instead of the optimal "S" function.

[0088] Example 5

[0089] Example 5 is an optimized design of Example 1;

[0090] In step S100, if Figure 2 The curves shown demonstrate the nonlinear relationship between cell voltage (OCV) and state of charge (SOC). It can be seen that at low or high SOC, the variation of OCV with SOC is small, while in the intermediate range (20%-80% SOC), the sensitivity of OCV to SOC increases significantly. Figure 2 The middle trace represents the OCV value of the battery after charging (labeled as “after charge”). Figure 2 The other trace in the graph represents the OCV value after discharge (labeled as "afterdischarge"). This difference in charge and discharge paths shows the voltage hysteresis effect, which is the result of battery material properties and polarization effects.

[0091] According to the data provided by the "IGV Technical Machinery Manual", the utilization rate of IGV equipment is about 70%, which is equivalent to an average annual operating time of about 6,000 hours; taking into account the internal resistance of the IGV electronic control system and external connection lines, the operating voltage range of the battery system is limited to between 580V and 730V. Based on the OCV-SOC curve of a single battery cell in the above figure, the entire system consists of 180 battery cells in series. Therefore, when the SOC is 20%, the system voltage is 3.3V×180=594V, and when the SOC is 80%, the system voltage is 4.05V×180=729V. The operating voltage range corresponding to the upper and lower limits of the power meets the system requirements (580V-730V). If the SOC is lower than 20%, the system voltage will be lower than 580V, which will trigger the low voltage fault alarm mechanism of the IGV;

[0092] The terminal considers the power consumption of IGVs going to charging stations and the battery's own consumption, setting the minimum charging threshold at 30%. At the same time, to meet actual operational needs, extend charging intervals, and improve production efficiency, the fuzzy algorithm incorporates key factors such as mission status and real-time operational requirements, setting the maximum charging threshold at 70%.

[0093] The improved charging strategy fully considers four key influencing factors: battery power, mission status, real-time operation requirements, and the idle rate of charging piles. For IGVs with battery power below 30%, the charging strategy is consistent with the original strategy, giving priority to charging at the nearest available charging pile; when the battery power is above 70%, the IGV does not need to be charged. Therefore, the key control range of the improved strategy is IGVs with a battery power range of 30%-70%.

[0094] Battery power is the main factor determining charging priority. The fleet management system reads the power of each IGV in real time to determine whether the battery power is within the range of 30%-70%. If the conditions are met, a battery power membership function is established based on the battery power to dynamically evaluate its charging needs.

[0095] The IGV mission status is another important factor in determining charging priority; the program is set so that the IGV cannot go directly to the charging station when carrying a container to perform a mission, and must complete the current mission first; the mission status is therefore divided into two states: "mission" and "no mission", among which the charging demand when there is no mission is significantly higher than when there is a mission.

[0096] The real-time IGV operational demand is defined as the number of IGVs required for the operation minus the number of currently available IGVs, reflecting the difference between operational demand and currently available resources. Terminals predict IGV demand based on berthing plans for the coming days, but due to factors such as maintenance and repairs, the actual number of IGVs available for deployment may fall short of demand, creating a demand gap. The lower the demand, the higher the IGV charging demand membership. Based on this characteristic, a membership function for operational demand was constructed.

[0097] In addition, the distance between the IGV and the charging station is also one of the key factors affecting charging demand; the shorter the distance, the higher the charging demand, and its membership value also increases accordingly; by dynamically regulating the IGV in the 30%-70% power range, this improved strategy flexibly adjusts the charging priority after considering various factors, effectively optimizing resource utilization and improving system operation efficiency.

[0098] Example 6

[0099] Example 6 is an optimized design of Example 1;

[0100] In step S3, a fuzzy set F of IGV charging influencing factors x is established and expressed as where x i The corresponding membership degree is u(x i ), where the membership u(x i ) is larger, the greater the charging demand is, and the more it should be sent to charge first; according to the actual situation on site, the four key factors affecting the IGV charging demand are found; according to the actual situation, the four key influencing factors are taken, and the fuzzy set expression is One of the influencing factors is battery power;

[0101] First, combined with the relationship between the open circuit voltage (OCV) and state of charge (SOC) of a single battery observed during IGV operation at an automated terminal, when the battery is discharged, the voltage decreases as the charge decreases, and the closer the discharge curve is to zero, the steeper the curve is, indicating that the more the IGV needs to be charged, the larger the membership function should be to fit the actual discharge curve. The membership function should change slowly in this section and maintain a high membership degree (the membership degree u(x) has been defined). i )The larger it is, the more it should be sent to charge first);

[0102] In addition, when the voltage is high, the battery discharge curve changes more smoothly and can maintain a high output voltage, which fits the actual discharge curve. The membership function should change smoothly in this section and maintain a low membership degree.

[0103] Therefore, based on the actual IGV battery discharge situation, the membership curve can be designed to be "S" shaped, as shown in the following figure:

[0104] Car battery power level

[0105] Where u1(x) is the correlation between battery power and charging demand, and x is the battery power.

[0106] The second influencing factor is the task status:

[0107] Real-time mission status affiliation of intelligent guided vehicles

[0108] The third influencing factor is the workload:

[0109] Operation demand = Number of IGVs required for the operation - Number of IGVs currently operating, with x representing the operation demand. Operation demand reflects the number of IGVs currently operating at the terminal. When the number of idle IGVs is greater than 30, it indicates that the terminal is operating lightly, and the membership is set to 1. When the number of unavailable IGVs is greater than 20, it indicates that the terminal is busy and production needs to be guaranteed as much as possible. When the number of unavailable IGVs is between 0 and 20, the membership value conforms to the form of an exponential function. When the operation demand gap exceeds 10, the membership function decreases rapidly, indicating that operation takes precedence over charging.

[0110] like Figure 4 As shown, the real-time operation demand of each IGV is

[0111]

[0112] The fourth influencing factor is the distance from the charging station:

[0113] According to the vehicle coordinates (x1, y1) and the empty pile coordinates (x2, y2), the straight-line distance between the empty pile and the vehicle can be obtained. The shortest path for the vehicle path

[0114] Distance from charging pile

[0115] Therefore, the closer to the empty pile, the greater the degree of membership.

[0116] The control objects of the present invention are all IGVs. For convenience, this embodiment selects the actual operating data of the test scheme of four vehicles to demonstrate the feasibility of the technical solution of the present invention, as follows:

[0117] The actual conditions of the four IGV vehicles used in the test are shown in Table 2:

[0118]

[0119] Table 2

[0120] The first step of the test scheme is to establish the fuzzy relationship matrix R;

[0121] According to the above expression, the membership table of the four cars is made, as shown in Table 3 below:

[0122]

[0123] Table 3

[0124] In this way, the fuzzy relationship matrix R is determined:

[0125]

[0126] The second step of the test scheme is to establish a weight vector A;

[0127] The Analytic Hierarchy Process (AHP) was used to determine the weights of each influencing factor. A hierarchical model was constructed, with charging decisions as the target layer and each influencing factor as the criterion layer. The weight distribution of each factor was calculated using a pairwise comparison method to obtain a reasonable weighting scheme.

[0128] The hierarchical model is constructed, including:

[0129] Target layer: Determine the charging decision target weight in the charging control strategy;

[0130] Criteria layer: includes four key factors that affect charging demand, namely battery level, task status, real-time operation requirements, and proximity to charging stations;

[0131] The third step of the test plan is to build a judgment matrix:

[0132] Battery level Task Status Operation demand Distance from charging station Battery level 1 3 3 5 Task Status 1 / 3 1 1 3 Operation demand 1 / 3 1 1 3 Distance from charging station 1 / 5 1 / 3 1 / 3 1

[0133] Table 4

[0134] In this judgment matrix (as shown in Table 4), the relative importance of battery power to task status is 3, indicating that battery power is more important than task status; the relative importance of battery power to job demand is 3, indicating that battery power is more important than job demand, and so on for other factors;

[0135] The fourth step of the test scheme is to calculate the weights. The sum of each column in the judgment matrix is ​​calculated, including the sum of the battery power column: 1+1 / 3+1 / 3+1 / 5=1.87, the sum of the task status column: 3+1+1+1 / 3=5.33, the sum of the operation demand column: 3+1+1+1 / 3=5.33, and the sum of the distance to the charging station column: 5+3+3+1=12.

[0136] Get the normalized matrix (see Table 5 below):

[0137] Battery level Task Status Operation demand Distance from charging station Battery level 0.53 0.56 0.56 0.42 Task Status 0.18 0.19 0.19 0.25 Operation demand 0.18 0.19 0.19 0.25 Distance from charging station 0.11 0.06 0.06 0.08

[0138] Table 5

[0139] Calculate the weight of each factor: battery charge weight = (0.53 + 0.56 + 0.56 + 0.42) / 4 ≈ 0.52, task status weight = (0.18 + 0.19 + 0.19 + 0.25) / 4 ≈ 0.2, operation demand weight = (0.18 + 0.19 + 0.19 + 0.25) / 4 ≈ 0.2, charging station distance weight = (0.11 + 0.06 + 0.06 + 0.08) / 4 ≈ 0.08;

[0140] The fifth step of the test scheme is consistency test; following the rounded weight vector of approximately A = (0.5, 0.2, 0.2, 0.1), the judgment matrix

[0141] calculate Get λ max ≈4.1, RI = 0.9, CR = CI / Ri ≈ 0.04 < 0.1, indicating that the consistency of the judgment matrix is ​​acceptable and the weights are valid;

[0142] The sixth step of the test program is to obtain the final evaluation;

[0143] According to the weights obtained by the hierarchical analysis method, the weights of the influencing factors in the decision-making are A = (0.5, 0.2, 0.2, 0.1), so the comprehensive evaluation is:

[0144]

[0145] The result of the comprehensive evaluation should try to take the maximum value max. From this, we can know that car 2 should be charged the most, followed by car 1, then car 3, and finally car 4.

[0146] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A charging control method for an intelligent guided vehicle based on fuzzy algorithm, characterized in that: The control method comprises the following steps: Step S1, obtaining four types of IGV charging influencing factors, which are IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance; Step S2: Establish membership functions corresponding to the four types of IGV charging influencing factors, thereby obtaining the IGV battery power membership function, the IGV task status membership function, the IGV real-time operation demand membership function, and the IGV charging distance membership function; Step S3, dynamically evaluating the charging demand of the IGV using a fuzzy algorithm based on the membership function obtained in step S2; Step S4: Control the designated IGV to go to the designated charging area for charging according to the charging demand of the IGV.

2. The charging control method for an intelligent guided vehicle based on fuzzy algorithm according to claim 1 is characterized in that: Before step S1, the control method further includes the following steps: Step S100, reading the current power of the IGV. When the current power of the IGV is within a range, step S1 is executed.

3. The charging control method for an intelligent guided vehicle based on fuzzy algorithm according to claim 2 is characterized in that: Between step S2 and step S3, the control method further includes the following steps: Step S23: Determine the distribution weight of each IGV charging influencing factor according to a hierarchical analysis method; then execute step S3.

4. The charging control method for an intelligent guided vehicle based on fuzzy algorithm according to claim 3 is characterized in that: Step S3 includes the following sub-steps: Step S31, obtaining the membership degrees corresponding to the four types of IGV charging influencing factors according to the membership function obtained in step S2; Step S32, establishing a fuzzy relationship matrix R using the membership degree obtained in step S31; In step S33 , the fuzzy relationship matrix R is converted into a judgment matrix according to the weight distribution of the corresponding IGV charging influencing factors, and the charging demand of the IGV is obtained from the judgment matrix.

5. The charging control method for an intelligent guided vehicle based on fuzzy algorithm according to claim 4 is characterized in that: In step S32 , a fuzzy relationship matrix R is established by formulating a membership table.

6. The charging control method for an intelligent guided vehicle based on a fuzzy algorithm according to any one of claims 2 to 5, characterized in that: The range value is 30% to 70% of the full charge of the IGV; In step S2, the membership degree of the IGV battery power membership function is In formula (1), u1(x) is the correlation between the battery power and the charging demand, and x is the battery power.

7. The charging control method for an intelligent guided vehicle based on a fuzzy algorithm according to any one of claims 1 to 4, characterized in that: In step S2, the membership degree of the IGV task state membership function is In formula (2), when the corresponding IGV task state is no task, the membership degree of the corresponding IGV task state membership function is 1, and when the corresponding IGV task state is a task, the membership degree of the corresponding IGV task state membership function is 0.

8. The charging control method for an intelligent guided vehicle based on a fuzzy algorithm according to any one of claims 1 to 4, characterized in that: In step S2, the real-time operation demand of IGVs is used to reflect the number of IGVs used in terminal operations at that time. When the real-time operation demand of IGVs corresponding to the state of having vehicles but no task arrangements is greater than a first value, the membership degree of the membership function of the corresponding IGV real-time operation demand is set to 1; when the real-time operation demand of IGVs corresponding to the state of having no vehicles and having task arrangements is greater than a second value and less than the first value, the corresponding IGV operation is given priority in production; when the real-time operation demand of IGVs corresponding to the state of having no vehicles and having task arrangements is greater than 0 and less than the second value, and the membership function of the corresponding IGV real-time operation demand decreases exponentially, the corresponding IGV operation is given priority in charging.

9. An intelligent guided vehicle charging control system based on fuzzy algorithm, characterized in that: include: The IGV charging influencing factor acquisition module is used to obtain four types of IGV charging influencing factors: IGV battery power, IGV mission status, IGV real-time operation demand, and IGV charging distance; The membership function establishment module is used to establish the membership functions corresponding to the four types of IGV charging influencing factors, thereby obtaining the IGV battery power membership function, the IGV task status membership function, the IGV real-time operation demand membership function, and the IGV charging distance membership function; A charging demand dynamic evaluation module is used to establish a module membership function based on the membership function and dynamically evaluate the charging demand of IGVs through a fuzzy algorithm; The IGV control module is used to control the designated IGV to go to the designated charging area for charging according to the charging demand determined by the charging demand dynamic evaluation module.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is executed by a processor, the steps of the intelligent guided vehicle charging control method based on fuzzy algorithm as described in any one of claims 1 to 8 are implemented.

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