Bicycle part AGV automatic carrying scheduling method based on edge calculation

Abstract

The invention discloses a bicycle part AGV automatic carrying scheduling method based on edge calculation, and relates to the technical field of cargo scheduling management, the system constructs a stacking index Dx by accurately collecting the weight m, the volume Tj, the density Md, the center-of-gravity position and the shortest distance zlj between the center-of-gravity position and the edge of cargos, and accurate classification and risk assessment of the cargos are achieved. The generated cargo risk management index Hflxs is based on flammable and combustible properties, humidity sensitivity and vulnerability, helps to timely send a classification instruction, distributes cargoes to a proper area, optimizes the loading and unloading process, collects ground path information in real time, and calculates a plurality of transportation paths by using a Dijkstra algorithm. The path risk index Hzzs is calculated by deeply analyzing the path gradient, the turning frequency and the turning angle and compared with the safety threshold value X, the low-risk path is preferentially selected, and potential risks in transportation are effectively reduced. In addition, through reasonable utilization of the stacking adaptation area, the storage space is maximized, and the storage efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of goods scheduling management, in particular to an AGV automatic handling scheduling method for bicycle parts based on edge computing. BACKGROUND

[0002] In modern logistics and warehouse management, the loading and scheduling process of goods is crucial, directly affecting the efficiency and safety of the entire supply chain. In actual operation, goods are usually stacked for storage and transportation. However, not all goods are suitable for high-level stacking. Different types of goods may face different challenges and risks during stacking. For example, due to the weight, volume, density, etc. of some goods, stacking instability may occur, affecting the utilization efficiency of warehouse space and the safety of goods.

[0003] Traditional goods loading and scheduling management technology mainly relies on manual classification and stacking of goods. This method is usually tedious and prone to errors, as manual operation is difficult to handle various complex goods attributes in real time and accurately. Manual classification not only takes time, but also easily overlooks some details, such as flammable and explosive properties, humidity sensitivity, and fragility, which may lead to safety hazards or damage to goods.

[0004] In terms of path optimization, traditional technology often does not fully consider the actual risks in the transportation process, especially in the transportation of heavy equipment. For example, equipment may face the risk of overturning on paths with steep slopes, and sharp turns may increase the probability of equipment instability. Without a systematic risk assessment mechanism, existing path planning methods cannot fully consider these factors, which may lead to equipment damage or accidents during transportation, thus there is an urgent need for a goods loading and scheduling management method and system that can solve the above problems. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides an AGV automatic handling scheduling method for bicycle parts based on edge computing to solve the problems mentioned in the background art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: an AGV automatic handling scheduling method for bicycle parts based on edge computing, comprising the following steps: Step one, platform login: the user inputs the account and password through the pre-set login interface of the automatic handling scheduling system, establishes and accesses the goods loading and unloading task center, and enters the system operation interface; Step two, goods information collection: real-time collection and registration of the weight m, volume Tj, density Md, center of gravity position and edge shortest distance zlj of the goods, construction of stacking index Dx; Step three, collect the packaging information, flammable and explosive information, humidity sensitivity information and fragility information of the goods, generate a goods classification set, and evaluate the flammability, humidity sensitivity and fragility: Step four, associate the goods classification set and the stacking index Dx to generate a goods risk management index Hflxs, and if the goods risk management index Hflxs exceeds the risk threshold P, issue a classification instruction; Step five, collect the ground path information of the heavy goods area, the light goods area, the fragile goods area and the stacking adaptive area in real time, including the path width and space limitation characteristics, and use Dijkstra algorithm to calculate the transportation path, and establish a regional path database; Step six, extract the path slope characteristics, turning number characteristics and turning angle characteristics in several transportation paths to analyze and obtain the path risk index Hzzs of each transportation path, and compare the path risk index Hzzs of several transportation paths with the safety threshold X respectively, preferentially select the path with a risk index Hzzs lower than the safety threshold X, and generate a path transportation instruction.

[0007] Meanwhile, the automatic handling scheduling system includes a platform login module, a first acquisition module, a second acquisition module, a goods area classification module and a path optimization module. The platform login module is used to pre-set a goods loading and unloading and scheduling management platform, and a user enters an operation interface through an account and a password to establish a goods loading and unloading task center. The first acquisition module is used to collect and register the weight m, volume Tj, density Md, center of gravity position and edge shortest distance zlj of the goods in real time, and construct a stacking index Dx. The second acquisition module is used to collect and obtain the goods packaging information, goods flammable and explosive information, goods humidity sensitivity information and goods fragile sensitivity information, and extract relevant characteristics for deep calculation and analysis to obtain the flammable and explosive index yryb, humidity sensitivity index Sdmg and fragility index Crx to generate a goods classification set. The goods area classification module is used to associate the goods classification set and the stacking index Dx to generate a goods risk management index Hflxs, and if the goods risk management index Hflxs exceeds the risk threshold P, issue a classification instruction. The path optimization module uses Dijkstra algorithm to calculate a plurality of transportation paths of the goods from the storage area to the loading and unloading area after receiving the classification instruction, establishes a regional path database, extracts path slope characteristics, turning number characteristics and turning angle characteristics in the plurality of transportation paths, analyzes a path risk index Hzzs of each transportation path, and compares the path risk index Hzzs of the plurality of transportation paths with a safety threshold X respectively, and preferentially selects a transportation path with a path risk index Hzzs lower than the safety threshold X for transportation.

[0008] Preferably, the stacking index Dx is obtained by extracting the weight m, volume Tj, density Md and center of gravity position and edge shortest distance zlj of the goods, and after non-dimensional processing, the stacking index Dx is calculated by the following formula: ； In the formula, h The higher the density Md and the lower the center of gravity zlj, the higher the stacking index Dx, indicating that the goods are suitable for stacking in the warehouse or transportation process; the larger the volume, the lower the index, indicating that the goods are not suitable for high-layer stacking.

[0009] Preferably, the flammable and explosive index yryb is calculated by the following formula: ； In the formula, is the flammable ingredient content of the goods, is the known maximum flammable ingredient content, is the storage temperature of the goods, is the critical temperature of the goods, is the storage pressure of the goods, is the maximum safe pressure threshold of the goods storage; is the flash point of the goods; 、 、 and are weight coefficients.

[0010] Preferably, the humidity sensitivity index Sdmg is obtained by the following steps: S11, measuring the mass of the goods material in the dry state by using a precision balance to obtain the initial mass and the first intensity , and measuring the mass of the weight of the goods material after absorbing moisture after being exposed to standard humidity conditions for 24 hours and the second intensity , and calculating the moisture absorption amount by the following formula: and the rate of change of intensity ； S12, according to the amount of moisture absorption and the rate of change of intensity , the humidity sensitivity coefficient is calculated by the following formula S13, the surface area of the cargo material is measured by BET method , and the pore volume of the material is measured by gas adsorption method or mercury intrusion method , after normalization, the cargo material moisture absorption coefficient is calculated by the following formula ； S14, after normalization of the humidity sensitivity coefficient and the material moisture absorption coefficient , the humidity sensitivity index is calculated by the following formula Sdmg ； In the formula, H is the current humidity of the cargo storage environment, is the moisture absorption coefficient of the cargo material, is the time when the cargo is exposed to the humidity environment, is the humidity tolerance time of the cargo, 、 and is expressed as a weight coefficient.

[0011] Preferably, the fragility index Crx is obtained by the following steps: S21, first, the maximum stress that the cargo can withstand is obtained by experiment , and the hardness of the cargo material is obtained by hardness test , after normalization, the impact resistance is calculated by the following formula S22, the cargo transportation vibration frequency is obtained by vibration analyzer simulation experiment , and the frequency at which the cargo naturally resonates when vibrating , the cargo resonance frequency is simulated and calculated by the following formula :​​​​​​​​ ; ; ; In the formula, This indicates the damping coefficient of the cargo packaging. This represents the ratio of the natural logarithm of the amplitude between two consecutive vibration periods, describing the rate of energy decay during cargo vibration. and The amplitude of the cargo in two consecutive periods; π is the mathematical constant pi, with a value set to 3.14159; It is a constant used in the damping coefficient calculation formula to normalize the effect of the logarithmic decrease; S23. Simulate the maximum drop height that the cargo can withstand without damage. It can be obtained by calculating using the following formula: ; In the formula, m is the weight of the goods. It is gravitational acceleration; S24. Simulate the ability of cargo packaging to protect goods from external impacts and vibrations, and calculate the packaging protection coefficient using the following formula. : ; In the formula, It represents the strength of the current cargo packaging material, indicating the cushioning performance value of the packaging, which is determined through experiments; S25, Impact resistance 、 Cargo Resonance Frequency 、 The maximum drop height that the goods can withstand without damage and packaging protection factor After dimensionless processing, the vulnerability index is calculated using the following formula. Crx : ； In the formula, This indicates the vertical height of the current position of the goods from the ground. 、 、 and It is represented as a weighting coefficient.

[0012] Preferably, the cargo area classification module includes an association unit and a classification scheduling unit; The associated unit is used to generate the cargo risk management index Hflxs by the following associated formula after the flammable and explosive index yryb, the humidity sensitivity index Sdmg, the fragility index Crx and the stacking index Dx are dimensionless processed: ; In the formula, 、 and respectively represent the weight coefficient of the flammable and explosive index yryb, the humidity sensitivity index Sdmg and the fragility index Crx, represents the first correction constant.

[0013] Preferably, the classification scheduling unit is used to preset a risk threshold P, the risk threshold P includes a first risk threshold P1 and a second risk threshold P2, and the cargo risk management index Hflxs is compared with the first risk threshold P1 and the second risk threshold P2 respectively, and the following evaluation results are obtained, including: When the cargo risk management index Hflxs is greater than the second risk threshold P2, it indicates that the cargo is high-risk, and the cargo is classified into the heavy cargo area and is allocated a high-strength forklift and an automatic crane for transportation; When the first risk threshold P1 is less than or equal to the cargo risk management index Hflxs and less than or equal to the second risk threshold P2, it indicates that the cargo is medium-risk, and the cargo is classified into the light cargo area and is allocated a light carrying robot or a light forklift for transportation; When the cargo risk management index Hflxs is less than the first risk threshold P1 and the fragility index Crx is greater than the breakable threshold, it indicates that the cargo is low-risk, but due to its high fragility, the cargo is classified into the fragile cargo area and is allocated a small carrying robot with protection function for transportation; When the cargo risk management index Hflxs is less than the first risk threshold P1 and the stacking index Dx is greater than the stability threshold, it indicates that the cargo is low-risk and has qualified stability, and is suitable for stacking, so the cargo is classified into the stacking adaptive area and is allocated an automatic equipment capable of efficiently stacking goods for transportation.

[0014] Preferably, the path optimization module includes a region acquisition unit and a load adaptation risk analysis unit; The region acquisition unit is used to acquire the ground path information, the path width and the space limitation features of the heavy cargo area, the light cargo area, the fragile cargo area and the stacking adaptive area in real time, and is used for Dijkstra algorithm calculation to obtain several transportation paths for each cargo, and a region path database is established; The load adaptation risk analysis unit is used to extract the path slope feature, the turning number feature and the turning angle feature in the region path database, and is used for deep analysis and calculation to obtain the path risk index Hzzs of each transportation path by the following formula: ; ; ； wherein m represents the total number of slope sections in each transport path, m represents the total number of turns in each transport path, represents the slope angle of the ith slope section, in radian, represents the load matching coefficient under the ith slope section, represents the load weight of the transport device, represents the maximum traction force that can be borne on the horizontal plane, in Newton, represents the downhill component force of gravity on the transport device when the slope is By comparing the downhill component force with the maximum traction force of the device, the load adaptability under a specific slope is evaluated; if > 1, it means that the traction force of the device is insufficient to cope with the load under the current slope, and there is a risk of losing control; represents the number of turns at the jth turn, which is set to 1, represents the turning angle at the jth turn, represents the balance coefficient during the jth turn, represents the turning radius of the jth turn, represents the load weight of the transport device, and h represents the height of the cargo, represents the turning speed at the jth turn, represents the influence of centrifugal force on the center of gravity, the higher the center of gravity and the faster the speed, the greater the influence of centrifugal force on balance; and represent the weight coefficients.

[0015] Preferably, the path optimization module further comprises a comparison unit and a priority instruction unit; The comparison unit is used to compare the path risk index Hzzs of each transport path with the safety threshold X respectively, to obtain a first comparison result, including: When the path risk index Hzzs ≥ the safety threshold X, it means that the current transport path has a slope and sharp turn inclination or instability risk; When the path risk index Hzzs < the safety threshold X, it means that the current transport path does not have a slope and sharp turn inclination or instability risk; and the priority instruction unit generates a path transport instruction by taking the current path as a first priority optimal path.

[0016] The present application provides a cargo loading and unloading and dispatching management method and system. It has the following beneficial effects: This cargo loading, unloading, and scheduling management method and system significantly improves the efficiency and safety of warehousing and transportation. By accurately measuring the cargo's weight (m), volume (Tj), density (Md), center of gravity position, and shortest distance (zlj) from the edge, and calculating the stacking index (Dx), the system achieves an assessment of the cargo's stacking adaptability. The rational utilization of the stacking adaptability zone maximizes storage space and improves storage efficiency.

[0017] The generated cargo risk management index Hflxs, based on flammability, explosiveness, humidity sensitivity, and vulnerability, helps to issue classification instructions in a timely manner, allocate cargo to appropriate areas, and optimize the loading and unloading process.

[0018] Real-time collection of ground path information and the use of Dijkstra's algorithm to calculate the optimal transportation route ensure both safety and efficiency. Through in-depth analysis of path gradient, number of turns, and turning angles, a path risk index Hzzs is calculated and compared with a safety threshold X. Low-risk paths are prioritized, effectively reducing the potential risk of overturning during transportation. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the automatic handling and scheduling method for bicycle parts using AGVs based on edge computing, as described in this invention. Figure 2 This is a schematic diagram of the steps in the cargo loading, unloading, and scheduling management method of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 Please see Figure 2 An edge computing-based AGV automated handling and scheduling method for bicycle parts includes the following steps: Step 1: Platform Login: Users enter their account and password through the pre-set login interface of the cargo loading and unloading and dispatch management platform to establish and access the cargo loading and unloading task center and enter the system operation interface. Step 2, Cargo Information Collection: Collect and register the cargo's weight m, volume Tj, density Md, center of gravity position and shortest distance zlj from the edge in real time, and construct the stacking index Dx; Step 3: Collect information on the packaging, flammability and explosiveness, humidity sensitivity, and vulnerability of the goods; generate a goods classification set; and assess flammability, explosiveness, humidity sensitivity, and vulnerability. Step four, associate the cargo classified collection and stacking index Dx to generate a cargo risk management index Hflxs, and issue a classification instruction if the cargo risk management index Hflxs exceeds the risk threshold P; Step five, collect the ground path information of the heavy cargo area, the light cargo area, the fragile cargo area and the stacking adaptive area in real time, including the path width and space limitation characteristics, and calculate the transportation path using the Dijkstra algorithm to establish a regional path database; Step six, extract the path slope characteristics, turning number characteristics and turning angle characteristics in several transportation paths to analyze and obtain the path risk index Hzzs of each transportation path, and compare the path risk index Hzzs of several transportation paths with the safety threshold X respectively, preferentially select the path with a risk index Hzzs lower than the safety threshold X, and generate a path transportation instruction.

[0022] Please refer to Figure 1 , an automatic handling and scheduling system, comprising a platform login module, a first acquisition module, a second acquisition module, a cargo area classification module and a path optimization module; The platform login module is used to pre-set a cargo loading and unloading and scheduling management platform, and a user enters an operation interface through an account and a password to establish a cargo loading and unloading task center; The first acquisition module is used to collect and register the weight m, volume Tj, density Md, center of gravity position and edge shortest distance zlj of the cargo in real time, and construct a stacking index Dx; the weight m of the cargo is collected and obtained through a gravity sensor, the volume Tj is scanned through a three-dimensional scanner to scan the shape of the cargo, and is calculated and obtained through image processing technology; the density Md is measured through ultrasonic reflection to measure the density of the cargo, and is especially suitable for some specific types of cargo, such as liquid or semi-solid materials; the center of gravity position and the edge shortest distance zlj are measured through a laser range finder to measure the center of gravity position and the distance from the edge of the cargo. It is calculated and obtained through the reflection of the laser beam; Through the gravity sensor, the three-dimensional scanner, the ultrasonic reflection measurement and the laser range finder, the weight, the volume, the density, the center of gravity position and the edge shortest distance of the cargo are accurately and timely collected. This automatic acquisition process reduces the error of manual measurement and improves the accuracy and reliability of the data.

[0023] The second acquisition module is configured to acquire the packaging information of the goods, the flammable and explosive information of the goods, the humidity sensitivity information of the goods, and the fragile sensitivity information of the goods, extract relevant features, and perform deep calculation and analysis to obtain a flammable and explosive index yryb, a humidity sensitivity index Sdmg, and a fragility index Crx, so as to generate a goods classification set. The second acquisition module obtains the packaging information, the flammable and explosive information, the humidity sensitivity information, and the fragile sensitivity information of the goods, calculates the flammable and explosive index yryb, the humidity sensitivity index Sdmg, and the fragility index Crx, and thus generates a comprehensive goods classification set. Such detailed classification helps to better arrange the storage positions of the goods and reduce damage and safety hazards of the goods in the stacking process.

[0024] The goods area classification module is configured to associate the goods classification set and the stacking index Dx to generate a goods risk management index Hflxs, and issue a classification instruction if the goods risk management index Hflxs exceeds a risk threshold P. The goods area classification module generates the goods risk management index Hflxs according to the classification set and the stacking index Dx, and issues a classification instruction if the risk management index exceeds the threshold P. Through this mechanism, potential risks can be identified and managed before the goods are stacked, avoiding damage or safety accidents caused by improper stacking.

[0025] The path optimization module uses the Dijkstra algorithm to calculate a plurality of transportation paths of the goods from the storage area to the loading and unloading area after receiving the classification instruction, establishes a regional path database, extracts the path slope feature, the number of turns feature, and the turning angle feature in the plurality of transportation paths, analyzes and obtains the path risk index Hzzs of each transportation path, and compares the path risk index Hzzs of the plurality of transportation paths with a safety threshold X respectively, and preferentially selects the transportation path with a path risk index Hzzs lower than the safety threshold X for transportation. Such optimization helps to reduce the risk of overturning of high-load equipment during transportation, and ensures the safety and stability of the transportation process.

[0026] In this embodiment, the present application significantly improves the safety and efficiency of the loading and unloading and transportation process by systematically combining the detailed properties of the goods with the path risk factors. First, through automated data acquisition and analysis, the errors and tediousness of manual operation are reduced, and the accuracy of goods classification and stacking management is improved. Second, the system comprehensively considers the risk factors such as slope and turning in path planning, optimizes the transportation path, and thus reduces the incidence of equipment damage and transportation accidents.

[0027] Embodiment 2, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1Specifically, the method for obtaining the stacking index Dx is extracting the weight m, volume Tj, density Md, and the shortest distance zlj between the center of gravity and the edge of the goods, and then calculating the stacking index Dx through the following formula after non-dimensional processing: ； In the formula, h The higher the density Md and the lower the center of gravity zlj, the higher the stacking index Dx, indicating that the goods are suitable for stacking in the warehouse or transportation process; the larger the volume, the lower the index, indicating that the goods are not suitable for high-level stacking.

[0028] In this embodiment, by comprehensively considering the weight, volume, density, and the shortest distance between the center of gravity and the edge of the goods, the stacking index Dx can scientifically evaluate the stability of the goods during stacking. This helps to determine whether the goods are suitable for high-level stacking, thereby optimizing the stacking design and improving the utilization efficiency of the warehouse space. A higher stacking index Dx indicates that the goods have better stacking stability and can be safely stacked at a high level; a lower stacking index warns that the goods may be unstable during high-level stacking. This helps to avoid goods collapse or damage due to improper stacking. By calculating the stacking index Dx, the stacking position of the goods can be reasonably arranged, with goods suitable for high-level stacking placed on the upper layer and goods not suitable for high-level stacking placed on the lower layer, thereby maximizing the utilization efficiency of the warehouse space. During transportation, the appropriate transportation and stacking methods can be selected according to the stacking index Dx of the goods to ensure the stability of the goods during transportation and reduce the risk of overturning and damage during transportation.

[0029] In this embodiment, the stacking index Dx is calculated by comprehensively considering the weight, volume, density, and the shortest distance between the center of gravity and the edge of the goods. This helps to determine whether the goods are suitable for high-level stacking, thereby optimizing the stacking design and improving the utilization efficiency of the warehouse space. A higher stacking index Dx indicates that the goods have better stacking stability and can be safely stacked at a high level; a lower stacking index warns that the goods may be unstable during high-level stacking. This helps to avoid goods collapse or damage due to improper stacking. By calculating the stacking index Dx, the stacking position of the goods can be reasonably arranged, with goods suitable for high-level stacking placed on the upper layer and goods not suitable for high-level stacking placed on the lower layer, thereby maximizing the utilization efficiency of the warehouse space. During transportation, the appropriate transportation and stacking methods can be selected according to the stacking index Dx of the goods to ensure the stability of the goods during transportation and reduce the risk of overturning and damage during transportation. Figure 1 Specifically, the flammable and explosive index yryb is calculated by the following formula: ； In the formula, is the flammable component content of the goods, which is collected and obtained by a gas sensor, is the known maximum flammable component content, is the storage temperature of the goods, which is collected and obtained by a temperature sensor, is the critical temperature of the goods, is the storage pressure of the goods, which is collected and obtained by a pressure sensor; is the maximum safe pressure threshold of the goods storage; is the flash point of the goods; 、 、 and are represented as weight coefficients, adjusted and set by the user, and , , , , and .

[0030] In this embodiment, by comprehensively considering the flammable component content, storage temperature, critical temperature, storage pressure and other factors of the goods, the flammable and explosive index yryb can accurately evaluate the flammable and explosive risk of the goods. This enables timely identification and handling of high-risk goods during storage and transportation, reducing the likelihood of fire or explosion. Based on the evaluation results of the flammable and explosive index, targeted safety measures can be taken, such as adjusting storage conditions, strengthening protective measures, etc., to effectively prevent and control fire or explosion accidents. The flammable and explosive index yryb can help determine the safe storage location of goods, distribute flammable and explosive goods to specially designed safety areas, avoid mixing with other goods, and improve warehouse safety.

[0031] Embodiment 4, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically, the humidity sensitivity index Sdmg The acquisition method comprises the following steps: S11, measure the mass of the goods material in the dry state with a precision balance to obtain the initial mass and the first intensity , and measure the weight of the goods material after being exposed to standard humidity conditions for 24 hours and the second intensity , calculate the moisture absorption amount and the intensity change rate by the following formula: ； ; S12, according to the moisture absorption amount and the intensity change rate , calculate the humidity sensitivity coefficient by the following formula: ; S13, measure the surface area of the goods material using the BET method , and measure the pore volume of the material using the gas adsorption method or mercury intrusion method , after normalization, calculate the moisture absorption coefficient of the goods material by the following formula: ； S14, the humidity sensitivity coefficient and the material hygroscopicity coefficient After normalization, the humidity sensitivity index is calculated by the following formula Sdmg : ； where H is the current humidity of the storage environment of the goods, is the material hygroscopicity coefficient of the goods, is the time the goods are exposed to the humidity environment, is the humidity tolerance time of the goods, 、 and are expressed as weight coefficients. The user adjusts the settings, the user adjusts the settings, and , , , and .

[0032] In this embodiment, by measuring the changes in mass and strength of the goods in dry and wet states, the humidity sensitivity index can accurately evaluate the impact of humidity on the performance of the material. This helps to understand the behavior and stability of the material under different humidity conditions. The humidity sensitivity index Sdmg can help monitor and control the quality of the goods, ensuring that the impact of humidity on the product is effectively managed during storage and transportation, reducing quality problems caused by humidity. Based on the humidity sensitivity index, goods with different humidity requirements can be allocated to appropriate storage areas to ensure that the goods are stored in the best environment, improving the operation efficiency of the overall logistics system. By analyzing the humidity sensitivity index, emergency plans can be developed for humidity changes to ensure that adjustments and processing can be made quickly in the event of sudden humidity changes, reducing potential impact.

[0033] Embodiment 5, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically, the fragility index Crx is obtained by the following steps: S21, first, the maximum stress that the goods can withstand is obtained by experiment , and the hardness of the goods material is collected by hardness testing , after normalization, the impact resistance is calculated by the following formula : ; S22, the goods transportation vibration frequency and the frequency at which the goods resonate naturally when vibrating are collected by simulating experiments with a vibration analyzer, and the goods resonance frequency is simulated and calculated by the following formula : ; ; ; wherein, represents the damping coefficient of the goods package, represents the natural logarithm ratio of the amplitude between two consecutive vibration periods, describing the rate of energy decay in the process of goods vibration, collected back by the vibration sensor, and the amplitude of the goods of two consecutive periods; π is the circular constant, and the value is set to 3.14159; is a constant used for normalizing the influence of the logarithmic decrement in the damping coefficient calculation formula; S23, simulating the maximum drop height that the goods can withstand without damage , calculated by the following formula: ; wherein, m is the weight of the goods, is the acceleration of gravity; S24, simulating the ability of the goods package to protect the goods from external impact and vibration, and the packaging protection coefficient is calculated by the following formula : ; wherein, is the strength of the current goods packaging material, indicating the cushioning performance value of the package, determined by experiment; S25, the impact resistance 、 the resonance frequency of the goods 、 the maximum drop height that the goods can withstand without damage and the packaging protection coefficient After dimensionless processing, the fragility index is calculated by the following formula Crx : ； wherein, represents the vertical height of the current placement position of the goods from the ground, meaning the height that the goods can drop, 、 、 and are weight coefficients adjusted and set by the user, and , , , , and .

[0034] In this embodiment, by calculating the impact resistance, resonance frequency, maximum drop height and packaging protection coefficient, the fragility index Crx can comprehensively evaluate different types of physical impact and vibration that the goods may encounter during transportation and storage, thereby providing a comprehensive fragility assessment. Through analysis of the packaging protection coefficient Crx, the packaging design can be optimized to improve its protection ability for goods and reduce damage caused by external impact or vibration. The transportation method can be adjusted according to the fragility index Crx, such as avoiding high-frequency vibration or extreme drop conditions, to ensure the safety of goods during transportation. For goods with different fragility levels, appropriate handling measures such as special packaging, vibration reduction measures, etc. can be taken to meet the actual needs of the goods. According to the fragility index Crx, the warehouse layout can be optimized, such as placing high-fragility goods in safer locations to reduce potential risks.

[0035] Embodiment 6, this embodiment is an interpretation and explanation in embodiment 1, please refer to Figure 1 Specifically, the goods area classification module comprises an association unit and a classification scheduling unit. The association unit is used to generate a goods risk management index Hflxs after dimensionless processing of the flammable and explosive index yryb, humidity sensitivity index Sdmg, fragility index Crx and stacking index Dx through the following association formula: ; In the formula, , and respectively represent the weight coefficients of the flammable and explosive index yryb, humidity sensitivity index Sdmg and fragility index Crx, which are adjusted and set by the user, and , , , and ; represents the first correction constant.

[0036] The classification scheduling unit is used to preset a risk threshold P, wherein the risk threshold P comprises a first risk threshold P1 and a second risk threshold P2, and compare the goods risk management index Hflxs with the first risk threshold P1 and the second risk threshold P2 respectively, to obtain the following evaluation results, including: When the goods risk management index Hflxs is greater than the second risk threshold P2, it indicates that the goods are high-risk, and the goods are classified into heavy goods area, and high-strength forklift and automatic crane are allocated for transportation; When the first risk threshold P1≤ the goods risk management index Hflxs≤ the second risk threshold P2, indicating that the goods are medium-risk, the goods are classified into the light goods area, and a light handling robot or a light forklift is assigned for transportation; When the goods risk management index Hflxs< the first risk threshold P1 and the fragility index Crx> the breakable threshold, indicating that the goods are low-risk, but due to high fragility, the goods are classified into the fragile goods area, and a small handling robot with a protection function is assigned for transportation; When the goods risk management index Hflxs< the first risk threshold P1 and the stacking index Dx> the stability threshold, indicating that the goods are low-risk and stable, and are suitable for stacking, the goods are classified into the stacking adaptation area, and an automated device that can efficiently stack goods is assigned for transportation.

[0037] In this embodiment, by combining the flammable and explosive index, humidity sensitivity index, fragility index, and stacking index, the goods risk management index Hflxs can comprehensively analyze the overall risk of the goods and provide a comprehensive evaluation of different risk factors. The automated classification and scheduling system simplifies the goods management process, improves the convenience and efficiency of operation, and enables the operator to focus more on core work. According to the stacking adaptability of the goods, the goods suitable for stacking are placed in the stacking adaptation area, maximizing the use of warehouse space and improving storage efficiency. Precise classification and processing reduce the idle and unnecessary loss of equipment, thereby reducing the overall operating cost.

[0038] Embodiment 7, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1 Specifically, when the high-load equipment is transported in an area with a large slope, the center of gravity position and the inclination angle may cause the stability of the equipment to decrease, thereby increasing the risk of overturning. In addition, the centrifugal force of the equipment during turning may affect the balance, especially in the case of sharp turning, the centrifugal force may cause the equipment to lose stability. Therefore, the traditional path planning method fails to fully consider these complex practical factors, which may cause damage to the equipment and goods, affecting the overall transportation efficiency and safety.

[0039] The path optimization module includes a region acquisition unit and a load adaptation risk analysis unit; The region acquisition unit is used to acquire the ground path information, path width, and space limitation characteristics of the heavy goods area, light goods area, fragile goods area, and stacking adaptation area in real time, and is used for Dijkstra algorithm calculation to obtain several transportation paths for each goods, and establishes a region path database. According to the stacking adaptability of the goods, the goods suitable for stacking are placed in the stacking adaptation area, maximizing the use of warehouse space and improving storage efficiency. Precise classification and processing reduce the idle and unnecessary loss of equipment, thereby reducing the overall operating cost.

[0040] The load adaptation risk analysis unit is used to extract the path slope characteristics, the number of turns, and the turning angle characteristics in the regional path database, and to perform deep analysis and calculation. The path risk index Hzzs of each transportation path is calculated by the following formula: ; ; ； In the formula, m represents the total number of slope sections in each transportation path, m represents the total number of turns in each transportation path, represents the slope angle of the i-th slope section, in radians, represents the load matching coefficient under the i-th slope section, represents the load weight of the transportation equipment, represents the maximum traction force that can be borne on the horizontal plane, in Newton, represents the downward component force of gravity on the transportation equipment when the slope is , by comparing the downward component force with the maximum traction force of the equipment, the load adaptation at a specific slope is evaluated; if >1, it means that the traction force of the equipment is insufficient to cope with the load at the current slope, and there is a risk of losing control; represents the number of turns at the j-th turn, which is set to 1, represents the turning angle at the j-th turn, represents the balance coefficient during the j-th turn, represents the turning radius of the j-th turn, represents the load weight of the transportation equipment, and h represents the height of the goods, represents the turning speed at the j-th turn, represents the influence of centrifugal force on the center of gravity, the higher the center of gravity and the faster the speed, the greater the influence of centrifugal force on balance; and represent weight coefficients, which are adjusted and set by the user, and , , and . The above and lower parameters are obtained by GPS and path analysis tools, or obtained by topographic map and detailed description of transportation route, such as measurement by slope meter, load sensor, traction force meter, industrial weighing sensor, angle meter, and speed sensor; and each of the above calculation formulas is calculated after dimensionless processing of all lower parameters, and the dimensionless processed parameters make the relationship between multiple variables more intuitive. It is used to analyze and evaluate different factors at the same scale.

[0041] In this embodiment, by analyzing the slope characteristics, the number of turns and the angle characteristics of the path, the risk level of each path can be accurately evaluated, so as to select the safest transportation path. By evaluating the traction of the equipment under different slopes, the path with insufficient traction of the equipment under large slope is avoided, so as to reduce the risk of equipment out of control. Considering the balance coefficient, turning radius and centrifugal force when turning, the inclination or falling of goods caused by improper turning is prevented, and the stability and safety of transportation are enhanced. The path risk index can be matched with the load capacity of the transportation equipment, and the best path suitable for the current goods and equipment conditions is selected, so as to avoid overloading of the equipment.

[0042] Embodiment 8, this embodiment is an explanation in embodiment 7, please refer to Figure 1 , Specifically, the path optimization module further comprises a comparison unit and a priority instruction unit; the comparison unit is used for comparing the path risk index Hzzs of each transportation path with the safety threshold X respectively, obtaining a first comparison result, including: When the path risk index Hzzs is greater than or equal to the safety threshold X, it indicates that the current transportation path has the risk of slope and sharp turning inclination or instability; When the path risk index Hzzs is less than the safety threshold X, it indicates that the current transportation path does not have the risk of slope and sharp turning inclination or instability; and the priority instruction unit generates a path transportation instruction by taking the current path as the first priority optimal path; if the path risk indexes Hzzs of several transportation paths are not lower than the safety threshold X, the following scheduling processing is performed, the slope section in the path is identified and measured, and support materials such as high-strength rubber pads, steel plates or other structural reinforcement materials are laid in the area with large slope to reduce the burden and sliding risk of the transportation equipment on the slope. A clear warning sign is set at the turning place to remind the operator to pay attention to speed reduction and stable turning. The speed of the transportation equipment is limited to ensure that the stability limit of the equipment is not exceeded during turning. The friction layer is increased on the ground in the turning area, such as laying anti-skid materials or increasing the friction coefficient of the road surface, to enhance the stability of the equipment during turning.

[0043] In this embodiment, the automatic generation of path transportation instruction reduces the time of manual judgment and selection, speeds up the path selection and scheduling, and thus improves the overall transportation efficiency. The system can prioritize the most suitable path according to the path risk index Hzzs, avoid transportation delay and equipment damage caused by selecting a path with high risk, and improve the accuracy of logistics scheduling. Prioritizing the path with low safety risk can reduce equipment failure and goods damage caused by path risk, thereby reducing maintenance and replacement costs and overall operating costs. The system allows users to adjust the safety threshold X according to specific transportation requirements and conditions to adapt to different path environments and equipment conditions, enhancing the adaptability and flexibility of the system.

[0044] The setting of the size of the threshold is to facilitate comparison, and the size of the threshold depends on how much sample data and the base number set by the person skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantized values.

[0045] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by the person skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art within the technical range disclosed by the present application, according to the technical scheme and the inventive concept of the present application, should be covered within the protection scope of the present application.

Claims

1. An AGV-based automatic handling and scheduling method for bicycle parts based on edge computing, characterized in that: Includes the following steps: Step 1: Platform Login: Users enter their account and password through the pre-set login interface of the automated handling and dispatching system to establish and access the cargo loading and unloading task center and enter the system operation interface; Step 2, Cargo Information Collection: Collect and register the cargo's weight m, volume Tj, density Md, center of gravity position and shortest distance zlj from the edge in real time, and construct the stacking index Dx; Step 3: Collect information on the packaging, flammability and explosiveness, humidity sensitivity, and vulnerability of the goods; generate a goods classification set; and assess flammability, explosiveness, humidity sensitivity, and vulnerability. Step 4: Associate the cargo classification set with the stacking index Dx to generate a cargo risk management index Hflxs. If the cargo risk management index Hflxs exceeds the risk threshold P, issue a classification instruction. Step 5: Collect ground path information in real time for heavy cargo area, light cargo area, fragile cargo area and stacking adaptation area, including path width and spatial constraint characteristics, and use Dijkstra's algorithm to calculate transportation paths and establish a regional path database; Step 6: Extract the path slope characteristics, number of turns characteristics, and turning angle characteristics from several transportation routes to analyze and obtain the path risk index Hzzs for each transportation route. Then, compare the path risk index Hzzs of several transportation routes with the safety threshold X, and prioritize the routes with risk index Hzzs lower than the safety threshold X to generate route transportation instructions.

2. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 1, characterized in that: The automated handling and scheduling system includes a platform login module, a first data acquisition module, a second data acquisition module, a cargo area classification module, and a route optimization module. The platform login module is used to pre-set the cargo loading and unloading and scheduling management platform. Users enter the operation interface with their account and password to establish a cargo loading and unloading task center. The first acquisition module is used to collect and register the weight m, volume Tj, density Md, center of gravity position and shortest distance zlj from the edge of the goods in real time, and construct the stacking index Dx; The second acquisition module is used to acquire cargo packaging information, cargo flammability and explosiveness information, cargo humidity sensitivity information, and cargo vulnerability sensitivity information, and extract relevant features, and perform deep calculation and analysis to obtain: flammability and explosiveness index yryb, humidity sensitivity index Sdmg, and vulnerability index Crx, so as to generate a cargo classification set; The cargo area classification module is used to associate the cargo classification set with the stacking index Dx to generate a cargo risk management index Hflxs. If the cargo risk management index Hflxs exceeds the risk threshold P, a classification instruction is issued. After receiving the classification instruction, the route optimization module uses the Dijkstra algorithm to calculate and obtain several transportation routes from the storage area to the loading and unloading area, establishes a regional route database, and extracts the route slope characteristics, turning number characteristics, and turning angle characteristics from several transportation routes to analyze and obtain the route risk index Hzzs for each transportation route. The route risk index Hzzs of several transportation routes is then compared with the safety threshold X, and transportation routes with a route risk index Hzzs lower than the safety threshold X are selected for transportation.

3. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 2, characterized in that: The stacking index Dx is obtained by extracting the weight m, volume Tj, density Md, and shortest distance zlj between the center of gravity and the edge of the goods. After dimensionless processing, the stacking index Dx is calculated using the following formula: ； In the formula, h The density Md indicates the height of the goods. The higher the density Md, the lower the center of gravity Zlj, and the higher the stacking index Dx, indicating that the goods are suitable for stacking during storage or transportation. The larger the volume, the lower the index, indicating that the goods are not suitable for high-level stacking.

4. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 2, characterized in that: The flammability and explosiveness index yryb is calculated using the following formula: ； In the formula, It refers to the flammable content of the goods. It is the highest known content of flammable components. It refers to the storage temperature of the goods. It is the critical temperature of the goods. It's the pressure of storing goods. It is the maximum safe pressure threshold for cargo storage; It is the flash point of the goods; 、 、 and It is represented as a weighting coefficient.

5. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 2, characterized in that: The humidity sensitivity index Sdmg The steps to obtain it are as follows: S11. Use a precision balance to measure the mass of the cargo material in a dry state to obtain the initial mass. and first strength After exposing the cargo materials to standard humidity conditions for 24 hours, the weight of the materials after moisture absorption was measured. Second strength The moisture absorption capacity can be calculated using the following formula. and intensity change rate : ； ； S12, Based on moisture absorption capacity and intensity change rate The humidity sensitivity coefficient can be calculated using the following formula. : ； S13. Using the BET method to measure the surface area of ​​cargo materials. The pore volume of the material was measured using either gas adsorption or mercury intrusion method. After normalization, the hygroscopic coefficient of the cargo material is calculated using the following formula. : ； S14, Humidity sensitivity coefficient and the material's hygroscopic coefficient After normalization, the humidity sensitivity index is calculated using the following formula. Sdmg : ； In the formula, H is the humidity of the current storage environment for the goods. It is the hygroscopic coefficient of the cargo material. This refers to the time the goods are exposed to a humid environment. This refers to the humidity tolerance time of the goods. 、 and It is represented as a weighting coefficient.

6. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 2, characterized in that: The vulnerability index Crx The steps to obtain it are as follows: S21. First, the maximum stress that the cargo can withstand is determined through experiments. The hardness of the cargo materials was obtained through hardness testing. After normalization, the impact strength is calculated using the following formula. : ； S22. Obtain the vibration frequency of cargo transportation through a vibration analyzer simulation experiment. And the frequency at which the cargo resonates naturally during vibration. The resonant frequency of the cargo is obtained by simulation calculation using the following formula. : ； ； ； In the formula, This indicates the damping coefficient of the cargo packaging. This represents the ratio of the natural logarithm of the amplitude between two consecutive vibration periods, describing the rate of energy decay during cargo vibration. and The amplitude of cargo movement over two consecutive periods; π is the mathematical constant pi, and its value is set to 3.14159. It is a constant used in the damping coefficient calculation formula to normalize the effect of the logarithmic decrease; S23. Simulate the maximum drop height that the cargo can withstand without damage. It can be obtained by calculating using the following formula: ； In the formula, m is the weight of the goods. It is gravitational acceleration; S24. Simulate the ability of cargo packaging to protect goods from external impacts and vibrations, and calculate the packaging protection coefficient using the following formula. : ； In the formula, It represents the strength of the current cargo packaging material, indicating the cushioning performance value of the packaging, which is determined through experiments; S25, Impact resistance 、 Cargo Resonance Frequency 、 The maximum drop height that the goods can withstand without damage and packaging protection factor After dimensionless processing, the vulnerability index is calculated using the following formula. Crx : ； In the formula, This indicates the vertical height of the current position of the goods from the ground. 、 、 and It is represented as a weighting coefficient.

7. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 6, characterized in that: The cargo area classification module includes an association unit and a classification scheduling unit; The associated unit is used to process the flammability and explosiveness index yryb, humidity sensitivity index Sdmg, vulnerability index Crx, and stacking index Dx into dimensionless values, and then generate the cargo risk management index Hflxs using the following associated formula: ； In the formula, , and These represent the weighting coefficients for the flammability and explosiveness index yryb, the humidity sensitivity index Sdmg, and the vulnerability index Crx, respectively. This represents the first correction constant.

8. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 7, characterized in that: The classification and scheduling unit is used to preset a risk threshold P, which includes a first risk threshold P1 and a second risk threshold P2. The cargo risk management index Hflxs is compared with the first risk threshold P1 and the second risk threshold P2 respectively to obtain the following evaluation results: When the cargo risk management index Hflxs > the second risk threshold P2, it indicates that the cargo is high-risk. The cargo is classified into the heavy cargo area and transported by high-strength forklifts and automated cranes. When the first risk threshold P1 ≤ cargo risk management index Hflxs ≤ the second risk threshold P2, it indicates that the cargo is of medium risk. The cargo is then classified into the light cargo area and assigned to a light handling robot or light forklift for transportation. When the cargo risk management index Hflxs < the first risk threshold P1 and the vulnerability index Crx > the fragile threshold, it indicates that the cargo is of low risk, but due to its high fragility, the cargo is classified into the fragile cargo area and assigned to a small handling robot with protective functions for transportation. When the cargo risk management index Hflxs < the first risk threshold P1 and the stacking index Dx > the stability threshold, it indicates that the cargo is low-risk and has qualified stability, making it suitable for stacking. The cargo is then classified into the stacking adaptation area and assigned to automated equipment that can efficiently stack the cargo for transportation.

9. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 2, characterized in that: The path optimization module includes a regional acquisition unit and a load adaptation risk analysis unit. The regional acquisition unit is used to collect ground path information, path width, and spatial limitation characteristics of heavy cargo area, light cargo area, fragile cargo area, and stacking adaptation area in real time, and to use the Dijkstra algorithm to calculate and obtain several transportation paths for each cargo, and to establish a regional path database. The load adaptation risk analysis unit is used to extract path slope characteristics, number of turns characteristics, and turning angle characteristics from the regional path database, and perform in-depth analysis and calculation to obtain the path risk index Hzzs for each transportation path using the following formula: ； ； ； In the formula, m represents the total number of slopes in each transportation route, and m represents the total number of turns in each transportation route. This represents the slope angle of the i-th slope segment, in radians. This represents the load matching coefficient under the i-th slope segment. Indicates the load weight of the transport equipment. This represents the maximum traction force that can be withstood on a horizontal surface, measured in Newtons. This indicates a slope of At that time, the downward component of gravity on the transport equipment is used to evaluate the load adaptability at a specific slope by comparing the downward component of gravity with the equipment's maximum traction force; if A value greater than 1 means that at the current slope, the equipment's traction force is insufficient to cope with the load, posing a risk of loss of control. This indicates the number of turns at the j-th turn, set to 1. This represents the turning angle at the j-th turn. This represents the balance coefficient during the j-th turn. This represents the turning radius of the j-th turn. The value of h represents the load capacity of the transport equipment, and h represents the height of the cargo. This represents the turning speed at the j-th turn. This indicates the effect of centrifugal force on the center of gravity. The higher the center of gravity and the faster the speed, the greater the effect of centrifugal force on the balance. and This represents the weighting coefficient.

10. The AGV automatic handling and scheduling method for bicycle parts based on edge computing according to claim 9, characterized in that: The path optimization module also includes a comparison unit and a priority instruction unit; The comparison unit is used to compare the path risk index Hzzs of each transportation path with the safety threshold X to obtain a first comparison result, including: When the path risk index Hzzs ≥ the safety threshold X, it indicates that the current transportation path has the risk of slope, sharp turns, tilting or instability. When the path risk index Hzzs < safety threshold X, it means that there is no risk of slope, sharp turn, tilt or instability in the current transportation path; and the priority instruction unit will generate a path transportation instruction by taking the current path as the first priority optimal path.

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