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

Abstract

The invention provides an intelligent guided vehicle charging control method based on a fuzzy algorithm, and the method comprises the steps: S1, obtaining four types of IGV charging influence factors which are the IGV battery power, the IGV task state, the IGV real-time operation demand quantity and the charging distance of the IGV; s2, membership functions corresponding to the four types of IGV charging influence factors are established respectively; s3, dynamically evaluating the charging demand of the IGV through a fuzzy algorithm according to the membership function obtained in S2; and S4, controlling the specified IGV to go to the specified charging area for charging according to the charging demand of the IGV. The invention also provides a computer readable storage medium applying the intelligent guided vehicle charging control method, and also provides an intelligent guided vehicle charging control system based on a fuzzy algorithm. The charging efficiency of the intelligent guided vehicle group of the port and the utilization rate of the intelligent guided vehicle group in port operation are improved, so that the working efficiency of the IGV and the operation efficiency of the whole wharf are remarkably improved.

Description

Technical Field

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

[0002] The intelligent guided vehicle (IGV) is one of the core equipment responsible for horizontal transportation in the automated container terminal, and undertakes many key transportation tasks. In the operation scenario of the automated terminal, the IGV involves the interaction of multiple devices and systems, including automatic interaction with the quay crane, automatic interaction with the rail crane, interaction with the charging system, position confirmation with other IGVs, and human-computer interaction when disassembling and installing locks. In particular, the interaction with the charging system is the basic link to ensure the continuous operation of the IGV. It can be seen that the IGV has complex interactions with many devices and is in a key position in the operation of the terminal. Improving its work efficiency and operating time is crucial to the overall terminal efficiency.

[0003] At present, the IGV charging strategy adopted at the terminal is relatively simple, and whether to charge is determined only based on the remaining power of the vehicle itself. Although this method can meet basic operating needs, it does not fully consider factors such as the urgency of the operating task, the utilization of charging piles, and the real-time system load, resulting in the utilization efficiency of charging resources far from being maximized, and there is a large room for improvement. In addition, the general fuzzy control method cannot carefully consider the specific actual working conditions, and adopts a relatively simple linear control, which has the disadvantages of insufficient and unreasonable resource utilization. For example: due to the simplicity of the existing IGV charging control strategy, it cannot maximize the utilization of existing port operating resources, cannot make accurate charging adjustments according to the on-site operating conditions, the control is not precise enough, and the resource utilization is insufficient and inappropriate.

[0004] In the prior art, the VMS (vehicle management system) reads the real-time power of each IGV, and the system determines whether the battery power 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 pile to charge. If not, it does not charge. This IGV charging control strategy is relatively simple and does not effectively utilize the idle charging piles. When the demand for IGV increases, it may cause 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, and the control method comprises the following steps:

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

[0010] Step S2, respectively establishing 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;

[0011] Step S3, dynamically evaluating the charging demand of the IGV through a fuzzy algorithm according to the membership function obtained in step S2;

[0012] Step S4, controlling 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 of values, executing step S1.

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

[0016] Step S23, determining the allocation weights of the corresponding IGV charging influencing factors according to a hierarchical analysis method; and then executing 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 through the membership degree obtained in step S31;

[0020] Step S33, according to the weight distribution of each IGV charging influencing factor, the fuzzy relationship matrix R is converted into a judgment matrix, 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] Further, 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), u(x) is the correlation between the battery power and the charging demand, and x is the battery power.

[0026] Further, in step S2, the membership degree of the corresponding 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 IGV is used to reflect the number of IGVs used in the terminal operation at that time. When the real-time operation demand of IGVs corresponding to the state of having vehicles 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 IGVs corresponding to the state of having no vehicles but having task arrangement 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 but having 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.

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

[0031] IGV charging influencing factor acquisition module, used to obtain four types of IGV charging influencing factors: IGV battery power, IGV task 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, so as to obtain 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 membership function of the module according to the membership function and dynamically evaluate the charging demand of the IGV 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 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 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; 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 relatively low cost; it makes the IGV charging control more precise and the calculated results are in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a working flow chart of the charging control method of the intelligent guided vehicle 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 involved in the present invention;

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

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

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further clearly and completely described in combination with the embodiments of the present invention. It should be noted that the described embodiments 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 "upper", "lower", "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. They 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 limitations on the present invention.

[0044] The terms "first", "second", "third", "fourth", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the definition of "first", "second", "third", "fourth" features may explicitly or implicitly include one or more of the 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, the four types of IGV charging influencing factors are IGV battery power, IGV task status, IGV real-time operation demand and IGV charging distance;

[0049] Step S2, respectively establishing 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;

[0050] Step S3, dynamically evaluating the charging demand of the IGV through a fuzzy algorithm according to the membership function obtained in step S2;

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

[0052] The intelligent guided vehicle charging control method based on fuzzy algorithm of this embodiment improves the charging strategy, incorporates factors such as battery power, task priority, charging pile idle rate and operation demand into the decision-making system, can significantly improve the work efficiency of IGV, and thus improve the operational efficiency of the entire terminal; it also adopts fuzzy algorithm technology, has the advantages of low investment cost, easy deployment, and simple later maintenance, can make dynamic decisions based on multiple factors, and can ensure that IGV can continue to operate efficiently under high-load operation conditions; the intelligent guided vehicle charging control method based on fuzzy algorithm of this embodiment can fully consider the distance between IGV and charging pile, save the mileage of IGV to the charging pile, and after adjustment by the control method of this embodiment, the natural box of each charging operation of the port increases by about one time, and the power consumption of each standard box is also reduced. It can also make dynamic decisions of the port based on multiple factors, can be low-cost, can be large-scale and simple to deploy port IGV, make the IGV group control more refined, and the calculation result is in line with reality. The actual effect can be referred to Table 1 below. The test data of 41 weeks in Table 1 are the port monitoring data without implementing the technical solution of the present invention, and the test data of 43 weeks are the port monitoring data after two weeks of implementing the technical solution of the present invention.

[0053]

[0054] Table 1

[0055] In Table 1, it is found that compared with the 41 weeks before the adjustment, the number of charging times of 100,000 boxes (IGV operation TEU) decreased from 2609 times to about 1740 times in the 43rd and 44th weeks after the adjustment using this scheme, a decrease of 33.3%, indicating that this scheme can reduce the number of IGV charging times and improve operating efficiency; the mileage to the charging pile decreased from 1043.6km to about 652.9km, a decrease of 37.4%, indicating that this scheme 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 time, 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 of values, executing step S1.

[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 allocation weights corresponding to the charging influencing factors of each IGV 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 through the membership degree obtained in step S31;

[0064] Step S33, according to the weight distribution of each IGV charging influencing factor, the fuzzy relationship matrix R is converted into a judgment matrix, 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 value 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), u(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. The larger the gap in demand vehicles, the less urgent charging will be.

[0071] like Figure 3 As shown, 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 terminal operations at that time. When the real-time operation demand of IGVs corresponding to the state of having vehicles 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 IGVs corresponding to the state of having no vehicles but having task arrangement is greater than a second 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 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 needs of actual operations, and the degree of subordination decreases nonlinearly, which fits the actual situation of "operations are prioritized 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] IGV charging influencing factor acquisition module, used to obtain four types of IGV charging influencing factors: IGV battery power, IGV task 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, so as to obtain 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 membership function of the module according to the membership function and dynamically evaluate the charging demand of the IGV 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 Embodiment 1 are implemented.

[0085] Example 4

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

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

[0088] Example 5

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

[0090] In step S100, if Figure 2 The curve shown shows the nonlinear relationship between the single cell voltage (OCV) and the charge (SOC); it can be seen that when the SOC is low or high, the change of OCV with SOC is small, while in the middle area (20%-80% SOC), the sensitivity of OCV to SOC is significantly enhanced; Figure 2 The middle trace represents the OCV value measured after the battery is charged (labeled as “after charge”). Figure 2 The other track in the graph represents the OCV value after discharge (labeled as “afterdischarge”). This difference in the 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 sets the minimum charging threshold to 30% considering the power consumed by the IGV to the charging pile and the battery consumption itself. At the same time, in order to meet the actual operation needs, extend the charging interval, and improve production efficiency, key factors such as task status and real-time operation needs are introduced into the fuzzy algorithm, and the maximum charging threshold is set to 70%;

[0093] The improved charging strategy takes into full account four key influencing factors: battery power, task 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 the nearest available charging pile for charging; and when the battery power is above 70%, the IGV does not need to be charged. Therefore, the key control interval of the improved strategy is IGVs with power in the range of 30%-70%;

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

[0095] The IGV mission status is another important factor in determining the 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: "with mission" and "without 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 operation demand is defined as the number of IGVs required for the operation minus the number of currently available IGVs, reflecting the difference between the operation demand and the currently deployable resources; the terminal predicts the IGV demand based on the berthing plan for the next few days, but due to factors such as maintenance and repair, the actual number of IGVs that can be deployed may be lower than the demand, thus forming a demand gap; the lower the demand, the higher the IGV charging demand membership. Based on this feature, a membership function for the operation demand is constructed.

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

[0098] Example 6

[0099] Embodiment 6 is an optimized design of Embodiment 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 charging demand of IGV 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 the battery power;

[0101] First, combined with the relationship between the open circuit voltage (OCV) and the state of charge (SOC) of a single battery observed during the IGV operation of the automated terminal, when the battery is discharged, the voltage decreases as the power decreases, and the closer the discharge curve is to zero, the steeper the curve is, indicating that the IGV needs to be charged more, and the larger the membership function should be, which fits the actual discharge curve. The membership function should change slowly in this section and maintain a high membership degree (the membership degree u(x 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 slowly and can maintain a higher output voltage, which fits the actual discharge curve. The membership function should change smoothly in this section and maintain a lower membership degree.

[0103] Therefore, combined with the actual IGV battery discharge situation, the membership curve can be designed as an "S" type, as shown in the following figure:

[0104]

[0105] Where u(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]

[0108] The third influencing factor is the operation demand:

[0109] Operation demand = number of IGVs required for operation - number of IGVs for current operation, operation demand is represented by x; operation demand reflects the number of IGVs used for terminal operation at that time. When the number of idle IGVs is greater than 30, it means that the terminal operation volume is not large at this time, and the membership is set to 1; when the number of vacant IGVs is greater than 20, it means that the terminal operation is busy at this time and production needs to be guaranteed as much as possible; when the number of vacant IGVs is in the range of 0 to 20, the membership value conforms to the form of an exponential function, and when the gap in operation demand is greater than 10, the membership function decreases faster, indicating that operation takes precedence over charging;

[0110] like Figure 4As 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]

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

[0116] The control object of the present invention is all IGVs. For convenience, this embodiment selects the actual operation data of the test scheme of four vehicles to prove the feasibility of the technical solution of the present invention, as follows:

[0117] The actual conditions of the four IGV vehicles used for testing 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:

[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) is used to determine the weight of each influencing factor. By constructing a hierarchical model, the charging decision is taken as the target layer and each influencing factor is taken as the criterion layer. The weight distribution of each factor is calculated by the pairwise comparison method, so as to obtain a reasonable weight scheme.

[0128] The hierarchical model is constructed, including:

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

[0130] Criteria layer: includes four key factors that affect charging demand, namely battery power, task status, real-time operation demand and distance from charging pile;

[0131] The third step of the test scheme is to construct a judgment matrix:

[0132] Battery level Task Status Operation Demand Charging station distance Battery level 1 3 3 5 Task Status 1 / 3 1 1 3 Operation Demand 1 / 3 1 1 3 Charging station distance 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 and task status is 3, indicating that battery power is more important than task status; the relative importance of battery power and job demand is 3, indicating that battery power is more important than job demand, and the same applies to other factors;

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

[0136] The normalized matrix is ​​obtained (as shown in Table 5 below):

[0137] Battery level Task Status Operation Demand Charging station distance 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 Charging station distance 0.11 0.06 0.06 0.08

[0138] Table 5

[0139] Calculate the weight of each factor: battery power 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 pile distance weight = (0.11 + 0.06 + 0.06 + 0.08) / 4 ≈ 0.08;

[0140] The fifth step of the test scheme is consistency check; following the rounded weight vector, it is approximately A=(0.5, 0.2, 0.2, 0.1), and 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 scheme is to obtain a final evaluation;

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

[0144]

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

[0146] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached 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, the four types of IGV charging influencing factors are IGV battery power, IGV task status, IGV real-time operation demand and IGV charging distance; Step S2, respectively establishing 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; Step S3, dynamically evaluating the charging demand of the IGV through a fuzzy algorithm according to the membership function obtained in step S2; Step S4, controlling 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 of 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 of values, executing step S1.

3. The charging control method of 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, determining the distribution weights of the corresponding IGV charging influencing factors according to a hierarchical analysis method; and then executing 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 through the membership degree obtained in step S31; Step S33, according to the weight distribution of each IGV charging influencing factor, the fuzzy relationship matrix R is converted into a judgment matrix, and the charging demand of the IGV is obtained from the judgment matrix.

5. The charging control method of 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 fuzzy algorithm according to any one of claims 2 to 5, characterized in that: The range value is 30% - 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), u(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 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 fuzzy algorithm according to any one of claims 1 to 4, characterized in that: In step S2, the real-time operation demand of IGV 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 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 IGVs corresponding to the state of having no vehicles but having task arrangement 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 but having 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.

9. An intelligent guided vehicle charging control system based on fuzzy algorithm, characterized in that: include: IGV charging influencing factor acquisition module, used to obtain four types of IGV charging influencing factors: IGV battery power, IGV task 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, so as to obtain 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 membership function of the module according to the membership function and dynamically evaluate the charging demand of the IGV 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, 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 claims 1 to 8 are implemented.

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