AGV state intelligent management method and system

Abstract

The invention discloses an AGV state intelligent management method and system, and relates to the technical field of AGV state management, and the method comprises the following steps: testing the energy consumption rule of an AGV in different load states and different motion states; the loading record, the motion record and the battery electric quantity of the AGV are collected; based on the loading record, the motion record and the battery capacity, whether the energy consumption of the AGV is abnormal or not is analyzed in combination with an energy consumption rule; real-time data of the AGV are collected, and visualization processing is carried out on the real-time data and the judgment result of whether the energy consumption is abnormal or not; the method is used for solving the problem that when an existing AGV state management technology is used for carrying out anomaly detection on an AGV battery, factors influencing battery power consumption are not comprehensively considered, and consequently the anomaly monitoring result of the AGV battery is prone to deviation.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV status management, and specifically to an intelligent management method and system for AGV status. Background Art

[0002] AGV status management technology refers to a comprehensive technical system that integrates technologies such as the Internet of Things, big data analysis, and artificial intelligence to monitor, analyze, and optimize key indicators such as the real-time operating status, health, and task execution efficiency of automated guided vehicles (AGVs) throughout their life cycle. Its core goal is to achieve efficient, stable, and safe operation of AGVs through data-driven decision-making and reduce operation and maintenance costs.

[0003] When the existing AGV status management technology performs abnormal detection on the AGV battery, it usually judges whether there is an abnormality in the AGV battery by analyzing data such as the current and voltage of the AGV battery. Since the AGV is an automated guided vehicle and is usually used for cargo handling, this involves the load of the AGV. The output power of the battery is different under different load conditions of the AGV. Therefore, the data such as current and voltage itself has large fluctuations and is not easy to analyze. Moreover, the current and voltage of the AGV battery are different under different motion states and different loads of the AGV. Especially when the AGV starts and stops, the power consumption is greater than that during uniform driving. Therefore, in the analysis process, it is necessary to analyze in combination with different motion states and different loads of the AGV. For example, in the patent application with the publication number CN115267567A, "A Logistics AGV Battery Mobile Remote Monitoring System, Method, Computer Device, and Storage Medium" is disclosed. This solution analyzes through data such as current and voltage, and does not analyze in combination with different motion states and different loads of the AGV during the analysis process, resulting in deviations in the monitoring results of the AGV battery. The existing AGV status management technology also has the problem that the factors affecting battery power consumption are not considered comprehensively when performing abnormal detection on the AGV battery, resulting in easy deviation of the abnormal monitoring results of the AGV battery. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to some extent. By setting up energy consumption test groups in different load states and motion states for testing, recording the test results, then analyzing the energy consumption law of the AGV based on the test results, collecting the loading records, motion records and battery power of the AGV, and then based on the loading records and motion records, combining the energy consumption law calculation formula and floating parameters to calculate the reasonable range of the energy consumption of the AGV, and then analyzing whether there is an abnormality in the energy consumption of the AGV based on the battery power and the reasonable range. Finally, collecting the real-time data of the AGV and visualizing the real-time data and the judgment result of whether there is an abnormality in the energy consumption, so as to solve the problem that the existing AGV status management technology still has insufficient consideration of the factors affecting battery power consumption when detecting abnormalities in the AGV battery, resulting in deviations in the abnormal monitoring results of the AGV battery.

[0005] To achieve the above object, in the first aspect, the present application provides an intelligent management method for AGV status, including the following steps:

[0006] Test the energy consumption law of the AGV in different motion states under different load states;

[0007] Collect the loading records, motion records and battery power of the AGV;

[0008] Based on the loading records, motion records and battery power, analyze whether there is an abnormality in the energy consumption of the AGV by combining the energy consumption law;

[0009] Collect the real-time data of the AGV and visualize the real-time data and the judgment result of whether there is an abnormality in the energy consumption.

[0010] Further, testing the energy consumption law of the AGV in different motion states under different load states includes the following sub-steps:

[0011] Set up energy consumption test groups in different load states and motion states for testing, and record the test results;

[0012] Analyze the energy consumption law of the AGV based on the test results.

[0013] Further, setting up energy consumption test groups in different load states and motion states for testing and recording the test results includes the following sub-steps:

[0014] Obtain the historical operation database, which records historical operation records, and the historical operation records record load records. Query the load records of each historical operation record, obtain the number of times different load records appear, and name it the load frequency;

[0015] Sort the load frequencies in descending order to obtain a frequency sequence, and acquire the load frequencies ranked among the top preset grouping numbers in the frequency sequence. Mark the corresponding load records as G n , where n is a positive integer and n is the serial number of G, and the maximum value of n is the preset grouping number;

[0016] Set an energy consumption test group with the preset grouping number plus one, and set the test load for the energy consumption test group, that is, G n , and each G n corresponds to an energy consumption test group. Mark the energy consumption test group corresponding to G n as E n , and mark the remaining one energy consumption test group as E0. The test load corresponding to E0 is G0, which is set to 0;

[0017] For any E n or E0, set a speed test group with the preset grouping number plus one, marked as S i , where i is a positive integer and 0 ≤ i ≤ the preset grouping number;

[0018] Conduct tests on S i in the test site. The test site is a straight road without obstacle occlusion. Each time the AGV travels to and fro, it experiences two starts and stops. For S0, set two AGVs, named the first AGV and the second AGV respectively. Control the first AGV to travel to and fro in the test site for a preset number of times, and record the number of starts and stops of the first AGV as 2×Q, marked as N1, where Q is the preset number. Control the second AGV to travel to and fro in the test site for a preset number of times, and each time it travels to the midpoint of the test site, an additional start and stop is made. Record the number of starts and stops of the second AGV as 3×Q, marked as N2. At the same time, record the electricity consumed by the first AGV and the second AGV, marked as R1 and R2 respectively. Thus, the analysis process for S0 ends;

[0019] For any S other than S0 i , mark the preset grouping number as A, obtain the maximum traveling speed of the AGV, named the maximum driving speed, divide the maximum driving speed into A equal parts, and obtain the speeds corresponding to each equal division point. Mark them in ascending order as H j , where j is a positive integer and j is the serial number of H, 1 ≤ j ≤ A. Set the test speed of S i as H i ;

[0020] Each S other than S0 i is provided with a test AGV. Control the test AGV to travel at H i , and travel to and fro in the test site for a preset number of times. Each S other than S0 iThe start-stop times are both N1, record the power consumed by the tested AGV, and mark it as R i , for each S except S0 i conduct Q tests, R i Take the average value of the Q tests. So far, the analysis process for any S except S0 i ends, and the analysis process for any E n or E0 ends;

[0021] The N1, N2, R1, R2, and R i are the test results.

[0022] Furthermore, analyzing the energy consumption law of the AGV based on the test results includes the following sub-steps:

[0023] Calculate the start-stop power consumption of the AGV through the formula ER = (R1 - R2) / (N2 - N1), where ER is the start-stop power consumption;

[0024] Through the formula (R i - N1×ER) / (L×2×Q) calculate the power consumption efficiency corresponding to E n and E0, marked as R(t,i), where t is the subscript of E n and E0, L is the length of the test site, obtain the power consumption efficiency in E0, that is, R(0,i), with S i 's H i as the X-axis, and the corresponding R(0,i) as the Y-axis to establish a plane rectangular coordinate system, named the basic power consumption trend chart, and input R(0,i) according to H i into the basic power consumption trend chart;

[0025] Conduct polynomial regression analysis on the basic power consumption trend chart to obtain the basic power consumption trend equation. The format of the basic power consumption trend equation is Y = a×X 2 + b×X + c, where Y is R(0,i), X is H i , a is the coefficient of X 2 , b is the coefficient of X, c is the inherent constant of the basic power consumption trend equation, that is, R(0,i) = a×H i 2 + b×H i + c;

[0026] Construct the load correction formula R(t,i) = α(t,i)×(G t + 1)×Y, where α(t,i) is the load coefficient, and convert it to get R(t,i) = α(t,i)×(G t + 1)×(a×H i 2 + b×H i+c), solve for α(t, i) using R(t, i);

[0027] For each value of t, calculate the average value of α(t, i), denoted as α; t , with G t as the horizontal axis and α t as the vertical axis, establish a rectangular coordinate system in the plane, named the load coefficient selection graph. Enter α t into the load coefficient selection graph according to G t , perform polynomial regression analysis on the load coefficient selection graph to obtain the load coefficient selection equation, and at the same time obtain the maximum value of the residuals therein, denoted as the floating parameter;

[0028] Construct the energy consumption law calculation formula EW = α t ×(GV + 1)×Y, used to solve the driving power consumption rate of the AGV, where EW is the driving power consumption rate and GV is the load of the AGV.

[0029] Furthermore, collect the loading record, movement record, and battery power of the AGV. The loading record, movement record, and battery power together form an AGV monitoring record. Among them, the loading record is the actual load when the AGV makes each transportation, represented by the symbol F. The movement record includes the running speed, start-stop times, and driving length of the AGV during this transportation, represented by the symbols V, T, and U respectively. The battery power includes the battery power of the AGV at the start and end of this transportation, named the start power and end power respectively, represented by the symbols D1 and D2 respectively.

[0030] Furthermore, based on the loading record, movement record, and battery power, and at the same time combined with the energy consumption law to analyze whether there is an abnormality in the energy consumption of the AGV, including the following sub-steps:

[0031] Based on the loading record and movement record, and at the same time combined with the energy consumption law calculation formula and the floating parameter, calculate the reasonable range of the energy consumption of the AGV;

[0032] Based on the battery power and the reasonable range, analyze whether there is an abnormality in the energy consumption of the AGV.

[0033] Furthermore, based on the loading record and movement record, and at the same time combined with the energy consumption law calculation formula and the floating parameter, calculate the reasonable range of the energy consumption of the AGV, including the following sub-steps:

[0034] Substitute H i = V into the basic power consumption trend equation to calculate Y;

[0035] Substitute F into the load coefficient selection equation to calculate α t , obtain the floating parameter and denote it as K;

[0036] Substitute GV = F and M = T into the formula EW = (α t ±K)×(GV + 1)×Y to solve for the minimum and maximum values of EW, denoted as EW1 and EW2 respectively;

[0037] Calculate the minimum estimated power consumption and the maximum estimated power consumption of the AGV through the formulas C1 = U×EW1 + ER×M and C2 = U×EW2 + ER×M, where C1 is the minimum estimated power consumption and C2 is the maximum estimated power consumption;

[0038] The reasonable range [C1, C2] is formed by C1 and C2.

[0039] Furthermore, analyzing whether there is an abnormality in the energy consumption of the AGV based on the battery power and the reasonable range includes the following sub - steps:

[0040] Calculate D2 - D1 to obtain the actual power consumption of the AGV;

[0041] Query whether the actual power consumption is within the reasonable range. If so, output a normal usage signal; otherwise, output an abnormal usage signal;

[0042] If an abnormal usage signal is output, mark that there is an abnormality in the energy consumption of the AGV and send a maintenance message to the maintenance department.

[0043] Furthermore, collecting the real - time data of the AGV and visualizing the real - time data and the judgment result of whether there is an abnormality in the energy consumption includes the following sub - steps:

[0044] The real - time data includes the position information, real - time power, real - time load, and whether it is idle of the AGV;

[0045] Name the normal usage signal and the abnormal usage signal as battery fault judgment signals;

[0046] Visualize the real - time data and the battery fault judgment signals through visualization technology and display them to the user.

[0047] In a second aspect, the present application provides an intelligent management system for the AGV state, including an energy consumption analysis module, a status monitoring module, an abnormality analysis module, and a visualization processing module; the energy consumption analysis module, the status monitoring module, and the visualization processing module are respectively connected to the abnormality analysis module for data connection;

[0048] The energy consumption analysis module is used to test the energy consumption law of the AGV in different load states and different motion states;

[0049] The status monitoring module is used to collect the loading records, motion records, and battery power of the AGV;

[0050] The abnormal analysis module is used to analyze whether there is an abnormality in the energy consumption of the AGV based on the loading record, movement record, and battery power, and by combining the energy consumption law at the same time.

[0051] The visualization processing module is used to collect the real-time data of the AGV and perform visualization processing on the real-time data and the judgment result of whether there is an abnormality in the energy consumption.

[0052] Advantages of the present invention: By setting up energy consumption test groups in different load states and movement states for testing, recording the test results, and then analyzing the energy consumption law of the AGV based on the test results, the advantages are that different load states and movement states of the AGV are considered, and when analyzing, the power consumption of the AGV is used as the analysis object instead of current and voltage. If current and voltage are used for analysis, the analysis process will be more complex, and there will be large fluctuations in current and voltage, which is not conducive to monitoring the battery of the AGV. It improves the accuracy and comprehensiveness of the abnormal monitoring of the AGV battery in the AGV status management.

[0053] The present invention collects the loading record, movement record, and battery power of the AGV, then calculates the reasonable range of the energy consumption of the AGV based on the loading record and movement record, and by combining the energy consumption law calculation formula and floating parameters. Then, based on the battery power and the reasonable range, it analyzes whether there is an abnormality in the energy consumption of the AGV. Finally, it collects the real-time data of the AGV and performs visualization processing on the real-time data and the judgment result of whether there is an abnormality in the energy consumption. The advantage is that by analyzing the reasonable range of the energy consumption of the AGV based on the loading record, movement record, and battery power of the AGV, it can detect the subtle changes in the power consumption of the AGV battery, thereby judging whether there are imperceptible abnormalities in the AGV battery. At this time, the AGV battery does not have a fault, but there is a tendency to have a fault. Therefore, it can be used to predict the abnormality of the AGV battery, and it improves the accuracy and effectiveness of the abnormal monitoring of the AGV battery in the AGV status management. Description of the Drawings

[0054] Figure 1 is the principle block diagram of the system of the present invention;

[0055] Figure 2 is the basic power consumption trend diagram of the present invention;

[0056] Figure 3 is the load coefficient selection diagram of the present invention;

[0057] Figure 4 is the step flow chart of the method of the present invention. Detailed Embodiments

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1. Please refer to Figure 1 As shown in the figure, the present application provides an intelligent management system for the AGV state, including an energy consumption analysis module, a state monitoring module, an abnormal analysis module, and a visualization processing module; the energy consumption analysis module, the state monitoring module, and the visualization processing module are respectively connected to the abnormal analysis module for data connection;

[0060] The energy consumption analysis module is used to test the energy consumption law of the AGV when it is in different motion states under different load states; the energy consumption analysis module includes an energy consumption test unit and an energy consumption analysis unit;

[0061] The energy consumption test unit is used to set energy consumption test groups for different load states and motion states and conduct tests, and record the test results; in practical applications, the load of the AGV has a greater impact on the power consumption. When the AGV is moving, the power consumption during the process from stationary to uniform motion is higher than that during uniform motion, which is the same principle as that the tram consumes more power during frequent acceleration and deceleration than during uniform motion. Therefore, when analyzing the energy consumption of the AGV, the load and the number of start-stop times of the AGV should be taken into consideration;

[0062] The energy consumption test unit is configured with an energy consumption test strategy, and the energy consumption test strategy includes:

[0063] Obtain the historical operation database. The historical operation database records historical operation records, and the historical operation records record load records. Query the load records of each historical operation record, obtain the number of times different load records appear, and name it the load frequency;

[0064] Sort the load frequencies in descending order to obtain a frequency sequence, obtain the load frequencies in the frequency sequence that rank in the top preset grouping quantity, and sequentially mark the corresponding load records as G n , where n is a positive integer and n is the serial number of G, and the maximum value of n is the preset grouping quantity;

[0065] In practical applications, in this embodiment, the load limit of the AGV is taken as 100%, and the load is exemplified in the form of a percentage. For example, when the load limit is 100 kg, a load of 50% means that the current load of the AGV is 50 kg. There are no specific requirements for setting the preset number of groups, which is set by the user himself. In this embodiment, the preset number of groups is set to 5. The load frequency represents the number of times different loads appear in daily operation, that is, the 5 most common loads are selected as G n For analysis, in this embodiment, G1 to G5 are 80%, 50%, 60%, 30%, and 25% in sequence;

[0066] Set an energy consumption test group with the preset number of groups plus one, and set a test load for the energy consumption test group, that is, G n , and each G n corresponds to an energy consumption test group, and mark the energy consumption test group corresponding to G n as E n , and mark the remaining energy consumption test group as E0. The test load corresponding to E0 is G0, which is set to 0;

[0067] For any E n or E0, set a speed test group with the preset number of groups plus one, marked as S i , where i is a positive integer and 0 ≤ i ≤ the preset number of groups;

[0068] Test S i in the test site. The test site is a straight road without obstacle occlusion. The AGV will experience two starts and stops every time it travels back and forth. For S0, set two AGVs, named the first AGV and the second AGV respectively. Control the first AGV to travel back and forth a preset number of times in the test site, record the number of starts and stops of the first AGV as 2×Q, marked as N1, where Q is the preset number. Control the second AGV to travel back and forth a preset number of times in the test site, and perform an additional start and stop every time it reaches the midpoint of the test site. Record the number of starts and stops of the second AGV as 3×Q, marked as N2. At the same time, record the electricity consumed by the first AGV and the second AGV, marked as R1 and R2 respectively. So far, the analysis process for S0 ends;

[0069] In practical applications, set 6 energy consumption test groups. Then the test loads corresponding to E1 to E5 are G1 to G5 in sequence, and the test load of E0 is 0. Taking E0 as an example, set 6 speed test groups, that is, S0 to S5, 0 ≤ i ≤ 5. The setting of the preset number only needs to ensure that all E nThe battery power of the AGV in each test in E0 has obvious changes, so as to obtain the power consumption of the AGV. In this embodiment, the preset number is set to 10. In S0, N1 and N2 are calculated to be 20 and 30 respectively, and R1 and R2 are recorded to be 17% and 19% respectively. The test speed of the AGV in the S0 test can be set to the maximum driving speed;

[0070] For any S except S0 i , mark the preset number of groups as A, obtain the maximum speed of the AGV, name it the maximum driving speed, divide the maximum driving speed into A equal parts, obtain the speed corresponding to each equal point, and mark them from small to large as H j , where j is a positive integer and j is the serial number of H, 1≤j≤A, and S i The test speed is set to H i ;

[0071] Each S except S0 i A test AGV is set in each test area to control the test AGV to H i Drive and drive a preset number of times back and forth in the test site. Each S except S0 i The number of starts and stops is N1, and the power consumed by the test AGV is recorded and marked as R i , for each S except S0 i Perform Q tests, R i Take the average value of Q tests, and then for any S except S0 i The analysis process is completed, and for any E n Or the analysis process of E0 ends;

[0072] N1, N2, R1, R2 and R i This is the test result;

[0073] In practical applications, i In the example of S1, A=5, the maximum speed is 10km / h, 10km / h is divided into 5 equal parts, and the equal points are 2km / h, 4km / h, 6km / h and 8km / h, which correspond to H1 to H4, H j The maximum driving speed is included in , so H5 is 10km / h. The test speed corresponding to S1 is H1, that is, 2km / h. The test AGV is controlled to travel at a speed of 2km / h, travel 10 round trips in the test site, and the number of starts and stops is 20. The recorded R1 is 13%. Similarly, the recorded R2 to R5 are 14%, 15%, 16%, and 17% respectively. The analysis process for E0 is completed, E nThe analysis process can refer to E0, and the test results N1 and N2 are 20 and 30 respectively, R1 and R2 are 17% and 19% respectively, and R1 to R5 are 13%, 14%, 15%, 16% and 17% in sequence. Removing the percentage signs gives 0.13, 0.14, 0.15, 0.16 and 0.17;

[0074] The energy consumption analysis unit is used to analyze the energy consumption law of the AGV based on the test results;

[0075] The energy consumption analysis unit is configured with an energy consumption analysis strategy, and the energy consumption analysis strategy includes:

[0076] Calculate the start-stop power consumption of the AGV through the formula ER = (R1 - R2) / (N2 - N1), where ER is the start-stop power consumption;

[0077] Please refer to Figure 2 as shown, calculate E through the formula (R i - N1×ER) / (L×2×Q), where n the power consumption efficiency corresponding to E and E0 is marked as R(t,i), where t is the subscript of E n and E0, L is the length of the test site, obtain the power consumption efficiency in E0, that is, R(0,i), and establish a plane rectangular coordinate system with H i of S i as the X-axis and the corresponding R(0,i) as the Y-axis, named the basic power consumption trend chart, and input R(0,i) into the basic power consumption trend chart according to H i ;

[0078] Perform polynomial regression analysis on the basic power consumption trend chart to obtain the basic power consumption trend equation. The format of the basic power consumption trend equation is Y = a×X 2 + b×X + c, where Y is R(0,i), X is H i , a is the coefficient of X 2 , b is the coefficient of X, c is the inherent constant of the basic power consumption trend equation, that is, R(0,i) = a×H i 2 + b×H i + c;

[0079] In practical applications, the length L of the test site in this embodiment is 100m, and it is calculated that ER = 0.2%. Taking E0 as an example, it is calculated that R(0,1) to R(0,5) are 0.045 / km, 0.05 / km, 0.055 / km, 0.06 / km and 0.065 / km in sequence. The constructed basic power consumption trend chart is as Figure 2 shown. Through polynomial regression analysis, the basic power consumption trend equation is obtained as Y = 0×X 2+0.0025×X + 0.04, that is, Y = 0.0025×X + 0.04, where a = 0, b = 0.0025, c = 0.04;

[0080] Construct the load correction formula R(t,i) = α(t,i)×(G t +1)×Y, where α(t,i) is the load coefficient, and after conversion, R(t,i) = α(t,i)×(G t +1)×(a×H i 2 +b×H i +c), and solve for α(t,i) through R(t,i);

[0081] Please refer to Figure 3 as shown. For each value of t, calculate the average value of α(t,i), denoted as α t , with G t as the horizontal axis and α t as the vertical axis to establish a rectangular coordinate system, named the load coefficient selection graph. Enter α t into the load coefficient selection graph according to G t , perform polynomial regression analysis on the load coefficient selection graph to obtain the load coefficient selection equation, and at the same time obtain the maximum value of the residuals therein, denoted as the floating parameter;

[0082] Construct the energy consumption law calculation formula EW = α t ×(GV + 1)×Y, which is used to solve the driving power consumption rate of the AGV. Among them, EW is the driving power consumption rate, and GV is the load of the AGV;

[0083] In practical applications, construct the load correction formula to get R(t,i) = α(t,i)×(G t +1)×(0.0025×H i +0.04), where R(t,i), G t and H i are all known. The G t +1 in the formula is to prevent G tWhen = 0, the formula has no solution. Solve each α(t,i). Taking t = 0 as an example, where G0 is 0, R(0,1) to R(0,5) are 0.045 / km, 0.05 / km, 0.055 / km, 0.06 / km, and 0.065 / km respectively, and the corresponding H1 to H5 are 2km / h, 4km / h, 6km / h, 8km / h, and 10km / h respectively. There is no R(0,0) and H0. The solved α(0,1) to α(0,5) are 1, 1, 1, 1, and 1 respectively. Since the load of the AGV at t = 0 is 0, the conditions during testing are exactly the same as the basic power consumption trend equation, and when the load is the same, the fluctuation of α(t,i) is small, and there is no fluctuation when there is no load. Therefore, at t = 0, α(0,i) are all 1, and thus α0 = 1. Similarly, calculate α1 to α5 as 2.68, 2.12, 2.3, 1.82, and 1.71 respectively. The constructed load factor selection graph is as Figure 3 shown. Through polynomial regression analysis, the load factor selection equation is obtained as α t = 0.4355×G t 2 + 1.2775×G t + 1.3779, where the floating parameter is 0.02, and the floating parameter is reserved to two decimal places.

[0084] The status monitoring module is used to collect the loading records, movement records, and battery power of the AGV; among them, the loading record is the actual load during each transportation of the AGV, represented by the symbol F, the movement record includes the running speed, start-stop times, and driving length of the AGV during this transportation, represented by the symbols V, T, and U respectively, and the battery power includes the battery power of the AGV at the start and end of this transportation, named start power and end power respectively, represented by the symbols D1 and D2 respectively;

[0085] In actual application, taking a set of monitoring data of loading record, movement record, and battery power as an example, where F is 35%, V is 5km / h, T is 10, U is 1km, D1 is 76%, and D2 is 60%.

[0086] The abnormal analysis module is used to analyze whether there is an abnormality in the energy consumption of the AGV based on the loading record, movement record, and battery power, and at the same time combined with the energy consumption law; the abnormal analysis module includes a range calculation unit and an abnormal judgment unit;

[0087] The range calculation unit is used to calculate the reasonable range of the energy consumption of the AGV based on the loading record and movement record, and at the same time combined with the energy consumption law calculation formula and the floating parameter;

[0088] The range calculation unit is configured with a range calculation strategy, and the range calculation strategy includes:

[0089] Substitute H i = V into the basic power consumption trend equation to calculate Y;

[0090] Substitute F into the load coefficient selection equation to calculate α t , and obtain the floating parameter and mark it as K;

[0091] Substitute GV = F, M = T into the formula EW = (α t ± K) × (GV + 1) × Y to solve for the minimum and maximum values of EW, and mark them as EW1 and EW2 respectively;

[0092] Calculate the minimum estimated power consumption and the maximum estimated power consumption of the AGV through the formulas C1 = U × EW1 + ER × M and C2 = U × EW2 + ER × M, where C1 is the minimum estimated power consumption and C2 is the maximum estimated power consumption;

[0093] Form a reasonable range [C1, C2] from C1 and C2;

[0094] In practical applications, substitute H i = 5 km / h into Y = 0.0025 × X + 0.04 to solve for Y = 0.0525, and substitute G t = F = 35% into α t = 0.4355 × G t 2 + 1.2775 × G t + 1.3779 to solve for α t = 1.87837375. If it is an infinite decimal, retain six decimal places. The floating parameter K = 0.02, and further solve for EW1 and EW2 to be 0.132 and 0.135 respectively, that is, 13.2% and 13.5%. Then further calculate C1 and C2 to be 15.2% and 15.5% respectively. Since the battery power is displayed as an integer percentage, round 15.2% and 15.5% to get C1 and C2 to be 15% and 16% respectively, that is, the reasonable range is [15%, 16%];

[0095] The abnormal judgment unit is used to analyze whether there is an abnormality in the energy consumption of the AGV based on the battery power and the reasonable range;

[0096] The abnormal judgment unit is configured with an abnormal judgment strategy, and the abnormal judgment strategy includes:

[0097] Calculate D2 - D1 to obtain the actual power consumption of the AGV;

[0098] Query whether the actual power consumption is within the reasonable range. If so, output a normal usage signal; otherwise, output an abnormal usage signal;

[0099] If an abnormal output usage signal is generated, it indicates that there is an abnormality in the energy consumption of the AGV, and a maintenance message is sent to the maintenance department.

[0100] In practical applications, the calculated actual power consumption is 16%. By comparison, the actual power consumption is within the reasonable range, and a normal output usage signal is output.

[0101] The visualization processing module is used to collect the real-time data of the AGV and perform visualization processing on the real-time data and the judgment result of whether there is an abnormality in the energy consumption.

[0102] The visualization processing module is configured with a visualization processing strategy, which includes:

[0103] The real-time data includes the position information, real-time power, real-time load, and whether it is idle of the AGV.

[0104] The normal usage signal and the abnormal usage signal are named battery fault judgment signals.

[0105] Through visualization technology, the real-time data and the battery fault judgment signal are visually processed and displayed to the user.

[0106] In practical applications, existing visualization technologies are used to visually process the real-time data and the battery fault judgment signal, and no specific description is provided in this embodiment.

[0107] Embodiment 2, please refer to Figure 4 As shown, the present application provides an intelligent management method for the AGV state, including the following steps:

[0108] Step S1, test the energy consumption law of the AGV when it is in different motion states under different load states; Step S1 includes the following sub-steps:

[0109] Step S101, set energy consumption test groups for different load states and motion states and conduct tests, and record the test results.

[0110] Step S101 includes the following sub-steps:

[0111] Step S1011, obtain the historical operation database, which records historical operation records, and the load records are recorded in the historical operation records. Query the load records of each historical operation record, obtain the number of times different load records appear, and name it the load frequency.

[0112] Step S1012, sort the load frequencies in descending order to obtain a frequency sequence, obtain the load frequencies ranked in the top preset grouping number in the frequency sequence, and sequentially mark the corresponding load records as G n , where n is a positive integer and n is the serial number of G, and the maximum value of n is the preset grouping number.

[0113] Step S1013: Set up an energy consumption test group with the preset grouping quantity increased by one, and set the test load for the energy consumption test group, i.e., G n , each G n corresponds to an energy consumption test group, and mark the energy consumption test group corresponding to G n as E n , mark the remaining one energy consumption test group as E0, the test load corresponding to E0 is G0, and set it to 0;

[0114] Step S1014: For any E n or E0, set up a speed test group with the preset grouping quantity increased by one, and mark it as S i , where i is a positive integer and 0 ≤ i ≤ the preset grouping quantity;

[0115] Step S1015: Test S i in the test site. The test site is a straight road without obstacle occlusion. Each time the AGV travels to and fro, it will experience two starts and stops. For S0, set up two AGVs, named the first AGV and the second AGV respectively. Control the first AGV to travel to and fro in the test site for a preset number of times, record the number of starts and stops of the first AGV as 2×Q, and mark it as N1, where Q is the preset number. Control the second AGV to travel to and fro in the test site for a preset number of times, and when it reaches the midpoint of the test site each time, perform an additional start and stop. Record the number of starts and stops of the second AGV as 3×Q, and mark it as N2. At the same time, record the power consumed by the first AGV and the second AGV, and mark them as R1 and R2 respectively. Thus, the analysis process for S0 ends;

[0116] Step S1016: For any S i except S0, mark the preset grouping quantity as A, obtain the maximum traveling speed of the AGV, name it the maximum traveling speed, divide the maximum traveling speed into A equal parts, obtain the speed corresponding to each equal division point, and mark them as H j in ascending order, where j is a positive integer and j is the serial number of H, 1 ≤ j ≤ A, and set the test speed of S i as H i ;

[0117] Step S1017: Set up a test AGV in each S i except S0, control the test AGV to travel at H i , and travel to and fro in the test site for a preset number of times. The number of starts and stops of each S i except S0 is N1. Record the power consumed by the test AGV, and mark it as R i , conduct Q tests on each S i except S0, Ri Take the average of Q tests. So far, for any S except S0 i the analysis process ends, and for any E n or the analysis process of E0 ends;

[0118] Step S1018, N1, N2, R1, R2, and R i are the test results;

[0119] Step S102, analyze the energy consumption law of the AGV based on the test results;

[0120] Step S102 includes the following sub-steps:

[0121] Step S1021, calculate the start-stop power consumption of the AGV through the formula ER = (R1 - R2) / (N2 - N1), where ER is the start-stop power consumption;

[0122] Step S1022, calculate the power consumption efficiency corresponding to E i - N1×ER) / (L×2×Q) and mark it as R(t,i), where t is the subscript of E n and E0, L is the length of the test site, obtain the power consumption efficiency in E0, that is, R(0,i), take S n as the X-axis, and the corresponding R(0,i) as the Y-axis to establish a plane rectangular coordinate system, named the basic power consumption trend chart, and input R(0,i) into the basic power consumption trend chart according to H i of H i ; i Input R(0,i) into the basic power consumption trend chart;

[0123] Step S1023, perform polynomial regression analysis on the basic power consumption trend chart to obtain the basic power consumption trend equation. The format of the basic power consumption trend equation is Y = a×X 2 + b×X + c, where Y is R(0,i), X is H i , a is the coefficient of X 2 , b is the coefficient of X, c is the inherent constant of the basic power consumption trend equation, that is, R(0,i) = a×H i 2 + b×H i + c;

[0124] Step S1024, construct a load correction formula R(t,i) = α(t,i)×(G t + 1)×Y, where α(t,i) is the load coefficient, and transform it to get R(t,i) = α(t,i)×(G t + 1)×(a×H i 2 + b×H i+c), solve for α(t,i) using R(t,i);

[0125] Step S1025, for each value of t, calculate the average value of α(t,i), denoted as α t , with G t as the horizontal axis and α t as the vertical axis, establish a rectangular coordinate system in the plane, named the load factor selection graph, and input α t into the load factor selection graph according to G t , conduct a polynomial regression analysis on the load factor selection graph to obtain the load factor selection equation, and at the same time obtain the maximum value of the residuals therein, denoted as the floating parameter;

[0126] Step S1026, construct the energy consumption law calculation formula EW = α t ×(GV + 1)×Y, used to solve the driving power consumption rate of the AGV, where EW is the driving power consumption rate and GV is the load of the AGV;

[0127] Step S2, collect the loading records, movement records, and battery power of the AGV; the loading records, movement records, and battery power together form an AGV monitoring record, where the loading record is the actual load during each transportation of the AGV, denoted by the symbol F, the movement record includes the running speed, start-stop times, and driving length of the AGV during this transportation, denoted by the symbols V, T, and U respectively, and the battery power includes the battery power of the AGV at the start and end of this transportation, named the start power and end power respectively, denoted by the symbols D1 and D2 respectively;

[0128] Step S3, based on the loading records, movement records, and battery power, and at the same time combined with the energy consumption law, analyze whether there is an abnormality in the energy consumption of the AGV; Step S3 includes the following sub-steps:

[0129] Step S301, based on the loading records and movement records, and at the same time combined with the energy consumption law calculation formula and the floating parameter, calculate the reasonable range of the energy consumption of the AGV;

[0130] Step S301 includes the following sub-steps:

[0131] Step S3011, substitute H i = V into the basic power consumption trend equation to calculate Y;

[0132] Step S3012, substitute F into the load factor selection equation to calculate α t , obtain the floating parameter and denote it as K;

[0133] Step S3013, substitute GV = F, M = T into the formula EW = (α tSolve for the minimum and maximum values of EW, denoted as EW1 and EW2 respectively, by (±K)×(GV + 1)×Y;

[0134] Step S3014, calculate the minimum estimated power consumption and the maximum estimated power consumption of the AGV through the formulas C1 = U×EW1 + ER×M and C2 = U×EW2 + ER×M, where C1 is the minimum estimated power consumption and C2 is the maximum estimated power consumption;

[0135] Step S3015, form a reasonable range [C1, C2] from C1 and C2;

[0136] Step S302, analyze whether there is an abnormality in the energy consumption of the AGV based on the battery power and the reasonable range;

[0137] Step S302 includes the following sub-steps:

[0138] Step S3021, calculate D2 - D1 to obtain the actual power consumption of the AGV;

[0139] Step S3022, query whether the actual power consumption is within the reasonable range. If so, output a normal usage signal; otherwise, output an abnormal usage signal;

[0140] Step S3023, if an abnormal usage signal is output, mark that there is an abnormality in the energy consumption of the AGV and send a maintenance message to the maintenance department;

[0141] Step S4, collect the real-time data of the AGV, and perform visualization processing on the real-time data and the judgment result of whether there is an abnormality in the energy consumption; Step S4 includes the following sub-steps:

[0142] Step S401, the real-time data includes the position information, real-time power, real-time load, and whether it is idle of the AGV;

[0143] Step S402, name the normal usage signal and the abnormal usage signal as battery fault judgment signals;

[0144] Step S403, perform visualization processing on the real-time data and the battery fault judgment signals through visualization technology and display them to the user.

[0145] Embodiment 3. The present application provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in an intelligent management method for the state of an AGV are run to achieve the following functions: testing the energy consumption law of the AGV when it is in different motion states under different load states; collecting the loading records, motion records, and battery power of the AGV; analyzing whether there is an abnormality in the energy consumption of the AGV based on the loading records, motion records, and battery power, and combining the energy consumption law; collecting the real-time data of the AGV, and visualizing the real-time data and the judgment result of whether there is an abnormality in the energy consumption.

[0146] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0147] Embodiment 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in an intelligent management method for the state of an AGV are run to achieve the following functions: testing the energy consumption law of the AGV when it is in different motion states under different load states; collecting the loading records, motion records, and battery power of the AGV; analyzing whether there is an abnormality in the energy consumption of the AGV based on the loading records, motion records, and battery power, and combining the energy consumption law; collecting the real-time data of the AGV, and visualizing the real-time data and the judgment result of whether there is an abnormality in the energy consumption.

[0148] Through the description of the above embodiments, the embodiments of the present invention may be provided as a method, a system or a computer program product. Based on such an understanding, the above technical solution, in essence or the part that contributes to the prior art, may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0149] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of systems, modules and units can be in electrical, mechanical or other forms.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent management method for AGV status, characterized in that It includes the following steps: Test the energy consumption law of the AGV when it is in different motion states under different load conditions; Collect the loading records, motion records and battery power of the AGV; Based on the loading records, motion records and battery power, and combined with the energy consumption law, analyze whether there is any abnormality in the energy consumption of the AGV; Collect the real-time data of the AGV, and perform visualization processing on the real-time data and the judgment result of whether there is any abnormality in the energy consumption; 2. The intelligent management method for the AGV state according to claim 1, characterized in that, Testing the energy consumption law of the AGV when it is in different motion states under different load conditions includes the following sub-steps: Set up energy consumption test groups for different load conditions and motion states and conduct tests, and record the test results; Analyze the energy consumption law of the AGV based on the test results; 3. The intelligent management method for an AGV state according to claim 2, characterized in that, Setting up energy consumption test groups for different load conditions and motion states and conducting tests, and recording the test results includes the following sub-steps: Obtain the historical operation database, which records historical operation records. The historical operation records record load records. Query the load records of each historical operation record, and obtain the number of occurrences of different load records, named load frequency; Sort the load frequencies in descending order to obtain a frequency sequence, and acquire the load frequencies in the frequency sequence that rank among the top preset number of groups. Mark the corresponding load records as G n , where n is a positive integer and n is the serial number of G, and the maximum value of n is the preset number of groups; Set up an energy consumption test group with the preset number of groups plus one, and set the test load for the energy consumption test group, i.e., G n , each G n corresponds to an energy consumption test group. Mark the energy consumption test group corresponding to G n as E n , mark the remaining one energy consumption test group as E0, the test load corresponding to E0 is G0, and set it to 0; For any E n Or E0, set a speed test group with the preset number of groups plus one, marked as S i , where i is a positive integer and 0 ≤ i ≤ the preset number of groups; Test S in the test site i The test is carried out in a straight road without obstacle occlusion. Each round trip of the AGV will experience two starts and stops. For S0, two AGVs are set up, named the first AGV and the second AGV respectively. Control the first AGV to travel back and forth in the test site for a preset number of times, record the number of starts and stops of the first AGV as 2×Q, marked as N1, where Q is the preset number. Control the second AGV to travel back and forth in the test site for a preset number of times, and each time it travels to the midpoint of the test site, an additional start and stop is performed. Record the number of starts and stops of the second AGV as 3×Q, marked as N2. At the same time, record the power consumption of the first AGV and the second AGV, marked as R1 and R2 respectively. So far, the analysis process for S0 is completed; For any S other than S0 i , mark the preset number of groups as A, obtain the maximum speed of the AGV traveling, name it the maximum traveling speed, divide the maximum traveling speed into A equal parts, obtain the speeds corresponding to each equal division point, and mark them as H in ascending order j , where j is a positive integer and j is the serial number of H, 1 ≤ j ≤ A, set the test speed of S i to H i ; Each S except S0 i is provided with a test AGV, and the test AGV is controlled to travel at a speed of H i and travel back and forth a preset number of times in the test site. The start-stop times of each S except S0 i are both N1. Record the power consumed by the test AGV and mark it as R i . For each S except S0 i conduct Q tests. R i Take the average value of the Q tests. So far, the analysis process for any S except S0 i ends, and the analysis process for any E n or E0 ends; The N1, N2, R1, R2, and R i are the test results.

4. The intelligent management method for an AGV state according to claim 3, wherein, Analyzing the energy consumption law of the AGV based on the test results includes the following sub-steps: Calculate the start-stop power consumption of the AGV through the formula ER=(R1-R2) / (N2-N1), where ER is the start-stop power consumption; Calculate E through the formula (R i - N1 × ER) / (L × 2 × Q), and the power consumption efficiency corresponding to E n and E0 is marked as R(t, i), where t is the subscript of E n and E0, L is the length of the test site, and obtain the power consumption efficiency in E0, that is, R(0, i). Taking S i of H i as the X-axis and the corresponding R(0, i) as the Y-axis to establish a plane rectangular coordinate system, named the basic power consumption trend chart, and input R(0, i) into the basic power consumption trend chart according to H i ; Perform polynomial regression analysis on the basic power consumption trend graph to obtain the basic power consumption trend equation. The format of the basic power consumption trend equation is Y = a×X 2 + b×X + c, where Y is R(0, i) and X is H i , a is the coefficient of X 2 , b is the coefficient of X, and c is the inherent constant of the basic power consumption trend equation, that is, R(0, i) = a×H i 2 + b×H i + c; Construct the load correction formula \(R(t, i)=\alpha(t, i)\times(G t + 1)\times Y\), where \(\alpha(t, i)\) is the load coefficient, and transform it to get \(R(t, i)=\alpha(t, i)\times(G t + 1)\times(a\times H i 2 + b\times H i + c)\), and solve for \(\alpha(t, i)\) through \(R(t, i)\); For each value of t, calculate the average value of α(t,i), denoted as α t , with G t as the horizontal axis and α t as the vertical axis, establish a rectangular coordinate system in the plane, named the load factor selection diagram, and input α t into the load factor selection diagram according to G t Perform polynomial regression analysis on the load factor selection diagram to obtain the load factor selection equation, and at the same time obtain the maximum value of the residuals therein, denoted as the floating parameter; Construct the energy consumption law calculation formula EW = α t ×(GV + 1)×Y, which is used to solve the driving power consumption rate of the AGV. Among them, EW is the driving power consumption rate, and GV is the load of the AGV.

5. The intelligent management method for an AGV state according to claim 4, wherein, Collect the loading records, motion records and battery power of the AGV. The loading records, motion records and battery power together form an AGV monitoring record. Among them, the loading record is the actual load of the AGV each time it transports, represented by the symbol F. The motion record includes the running speed, start-stop times and driving length of the AGV during this transportation, represented by the symbols V, T and U respectively. The battery power includes the battery power of the AGV at the start and end of this transportation, named start power and end power respectively, represented by the symbols D1 and D2 respectively; 6. The intelligent management method for an AGV state according to claim 5, wherein Based on the loading records, motion records and battery power, and combined with the energy consumption law, analyzing whether there is any abnormality in the energy consumption of the AGV includes the following sub-steps: Based on the loading records and motion records, and combined with the energy consumption law calculation formula and floating parameters, calculate the reasonable range of the energy consumption of the AGV; Analyze whether there is any abnormality in the energy consumption of the AGV based on the battery power and the reasonable range; 7. The intelligent management method for an AGV state according to claim 6, characterized in that, Based on the loading records and motion records, and combined with the energy consumption law calculation formula and floating parameters, calculating the reasonable range of the energy consumption of the AGV includes the following sub-steps: Substitute H i = V into the basic power consumption trend equation to calculate Y; Substitute F into the load factor selection equation to calculate α t , obtain the floating parameter and label it as K; Substitute GV = F and M = T into the formula EW = (α t ±K)×(GV + 1)×Y to solve for the minimum and maximum values of EW, denoted as EW1 and EW2 respectively; Calculate the minimum estimated power consumption and the maximum estimated power consumption of the AGV through the formulas C1 = U×EW1 + ER×M and C2 = U×EW2 + ER×M, where C1 is the minimum estimated power consumption and C2 is the maximum estimated power consumption; The reasonable range [C1, C2] is composed of C1 and C2; 8. The intelligent management method for an AGV state according to claim 7, characterized in that Analyzing whether there is any abnormality in the energy consumption of the AGV based on the battery power and the reasonable range includes the following sub-steps: Calculate D2 - D1 to obtain the actual power consumption of the AGV; Query whether the actual power consumption is within the reasonable range. If so, output a normal usage signal, otherwise output an abnormal usage signal; If an abnormal output usage signal is generated, it indicates that there is an abnormality in the energy usage of the AGV, and a maintenance message is sent to the maintenance department.

9. The intelligent management method for an AGV state according to claim 8, characterized in that Collect the real-time data of the AGV, and the visualization process of the real-time data and the judgment result of whether there is an abnormality in the energy usage includes the following sub-steps: The real-time data includes the position information, real-time power, real-time load, and whether it is idle of the AGV. Name the normal usage signal and the abnormal usage signal as battery fault judgment signals. Visualize the real-time data and the battery fault judgment signal through visualization technology and display them to the user.

10. An intelligent management system for AGV status, which is used to implement the intelligent management method for AGV status described in any one of claims 1-9, characterized in that, It includes an energy consumption analysis module, a status monitoring module, an abnormality analysis module, and a visualization processing module; the energy consumption analysis module, the status monitoring module, and the visualization processing module are respectively connected to the abnormality analysis module for data connection. The energy consumption analysis module is used to test the energy consumption law of the AGV when it is in different motion states under different load conditions. The status monitoring module is used to collect the loading records, motion records, and battery power of the AGV. The abnormality analysis module is used to analyze whether there is an abnormality in the energy usage of the AGV based on the loading records, motion records, and battery power, and at the same time, in combination with the energy consumption law. The visualization processing module is used to collect the real-time data of the AGV and visualize the real-time data and the judgment result of whether there is an abnormality in the energy usage.

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