Fork interaction control method and system based on wireless communication

Abstract

The application discloses a forklift interaction control method and system based on wireless communication and relates to the field of forklifts.The method comprises the following steps: accessing an interaction target storage area, calling M pieces of storage information and M pieces of three-dimensional storage modules, matching M initial forklift control schemes based on the M pieces of storage information and the M pieces of three-dimensional storage modules, matching forklift collision information in a historical control process respectively with the M initial forklift control schemes as indexes, obtaining M control risk point sets, determining Q target control risk points and Q risk factors, judging whether the Q risk factors exceed a risk threshold, optimizing a spatial layout to obtain an optimized target storage area, constructing a control optimization space, optimizing the M initial forklift control schemes to obtain a target forklift interaction control scheme, and solving the technical problem that the forklift operation safety is not enough in the prior art, thereby achieving the technical effect of improving forklift operation efficiency and safety.

Description

Technical Field

[0001] This invention relates to the field of forklifts, and more specifically to a fork interaction control method and system based on wireless communication. Background Technology

[0002] In modern logistics and manufacturing, forks (forklifts) are essential material handling equipment, widely used for storing, retrieving, and moving goods. However, in actual operation, the control and operation of forks present certain risks and complexities, including fork collisions and improper stacking of goods, which can lead to production accidents and losses. Currently, fork control is mostly done manually, and due to limitations in human perception and reaction capabilities, it is difficult to avoid operational risks and errors. Furthermore, existing fork control systems lack in-depth analysis of historical control data, making it impossible to effectively identify and control potential fork collision risks. Summary of the Invention

[0003] This application provides a fork interaction control method and system based on wireless communication, which solves the technical problem of insufficient safety in fork operation in the prior art.

[0004] In view of the above problems, embodiments of this application provide a fork interaction control method and system based on wireless communication.

[0005] A first aspect of this application provides a forklift interaction control method based on wireless communication, the method comprising:

[0006] The interactive target access area retrieves M access information items and M three-dimensional access modules, wherein the M access information items and the M three-dimensional access modules correspond one-to-one;

[0007] Based on the M access information and the M three-dimensional access modules, M initial fork control schemes are matched;

[0008] Using the M initial fork control schemes as indexes, fork collision information in the historical control process is matched to obtain M sets of control risk points, where each set of control risk points includes multiple risk location coordinates;

[0009] Based on the set of M control risk points, determine Q target control risk points and Q risk factors;

[0010] Determine whether the Q risk factors exceed the risk threshold. If so, take the corresponding target control risk point as the control risk point to be adjusted, and obtain K control risk points to be adjusted.

[0011] The spatial structure of the target access area is interactively analyzed, and the spatial layout is optimized by combining the K risk location coordinates of the K control risk points to be adjusted, so as to obtain an optimized target access area.

[0012] Interact with the forklift resources in the target access area to construct a control optimization space;

[0013] Based on the spatial structure of the control optimization space and the optimization target access area, the M initial fork control schemes are optimized to obtain the target fork interaction control scheme.

[0014] A second aspect of this application provides a forklift interaction control system based on wireless communication, the system comprising:

[0015] The calling module is used to interact with the target access area and retrieve M access information and M three-dimensional access modules, wherein the M access information and the M three-dimensional access modules correspond one-to-one.

[0016] The first matching module is used to match M initial fork control schemes based on the M access information and the M three-dimensional access modules;

[0017] The second matching module is used to match the fork collision information in the historical control process with the M initial fork control schemes as indexes to obtain M sets of control risk points, wherein each set of control risk points includes multiple risk location coordinates;

[0018] A risk determination module is used to determine Q target control risk points and Q risk factors based on the set of M control risk points;

[0019] The judgment module is used to determine whether the Q risk factors exceed the risk threshold. If so, the corresponding target control risk point is taken as the control risk point to be adjusted, and K control risk points to be adjusted are obtained.

[0020] A spatial optimization module is used to interact with the spatial structure of the target access area and optimize the spatial layout by combining the K risk location coordinates of the K control risk points to be adjusted, so as to obtain an optimized target access area.

[0021] A space construction module is used to interact with the forklift resources of the optimized target access area and construct a control optimization space;

[0022] The scheme optimization module is used to optimize M initial fork control schemes based on the spatial structure of the control optimization space and the optimization target access area to obtain a target fork interaction control scheme.

[0023] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0024] First, retrieve M access information entries and M three-dimensional access modules from the target access area, ensuring a one-to-one correspondence. Next, use this information to match M initial fork control schemes. Then, using the M initial fork control schemes as indexes, match fork collision information in historical data to obtain a set of M control risk points, containing multiple risk location coordinates for assessing potential risks. Based on the control risk point set, determine Q target control risk points and corresponding risk factors. Evaluate the Q risk factors; if any factor exceeds a risk threshold, the corresponding target control risk point is designated as a control risk point to be adjusted, forming K control risk points to be adjusted. Then, combine the spatial structure of the target access area and the location coordinates of the K control risk points to be adjusted, performing spatial layout optimization to obtain an optimized target access area. Next, optimize fork resources and construct a control optimization space to support subsequent control scheme optimization. Finally, based on the control optimization space and the optimized target access area structure, optimize the M initial fork control schemes to obtain the final target fork interaction control scheme. This invention solves the technical problem of insufficient safety in fork operation in existing technologies, and achieves the technical effect of improving the efficiency and safety of fork operation. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic flowchart of a forklift interaction control method based on wireless communication provided in an embodiment of this application;

[0027] Figure 2 This is a schematic diagram of the structure of a forklift interaction control system based on wireless communication provided in an embodiment of this application.

[0028] Explanation of reference numerals in the attached diagram: Calling module 11, First matching module 12, Second matching module 13, Risk determination module 14, Judgment module 15, Space optimization module 16, Space construction module 17, Scheme optimization module 18. Detailed Implementation

[0029] This application provides a fork interaction control method and system based on wireless communication, which solves the technical problem of insufficient safety in fork operation in the prior art.

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0031] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0032] Example 1

[0033] like Figure 1 As shown in the figure, this application provides a forklift interaction control method based on wireless communication, wherein the method includes:

[0034] The interactive target access area retrieves M access information items and M three-dimensional access modules, wherein the M access information items and the M three-dimensional access modules correspond one-to-one;

[0035] When accessing the target storage area, the system retrieves M access information entries and corresponding M three-dimensional access modules. The access information details key data such as the type, quantity, storage location, and access time of the goods. Simultaneously, the three-dimensional access modules provide specific motion parameters of the forks in three-dimensional space, such as lifting height, forward / backward movement distance, and lateral offset. The accuracy and real-time performance of these parameters directly determine whether the forks can accurately and quickly complete the goods access task. By retrieving and analyzing this access information and the three-dimensional access modules, a more comprehensive understanding of the goods distribution and fork movement can be obtained, providing a solid data foundation for subsequent fork control scheme development.

[0036] Based on the M access information and the M three-dimensional access modules, M initial fork control schemes are matched;

[0037] After obtaining M access information and the corresponding M three-dimensional access modules, the next key step is to match M initial fork control schemes based on this information.

[0038] Furthermore, based on the M access information and the M three-dimensional access modules, M initial fork control schemes are matched, the method includes:

[0039] The data for constructing the matching module includes multiple sample access information, multiple sample three-dimensional access modules, and multiple sample initial forklift control schemes.

[0040] The framework of the matching module is constructed with the access information as the x-axis and the stereo access module as the y-axis. The multiple sample access information and the multiple sample stereo access modules are input into the framework to obtain multiple sample points.

[0041] The coordinates of the multiple sample points are marked using the multiple initial fork control schemes for the samples.

[0042] The matching module is constructed based on the multiple sample points and their corresponding coordinate identifiers;

[0043] The M access information and the M three-dimensional access modules are respectively input into the matching module to generate the M initial fork control schemes.

[0044] To ensure more efficient and safer fork operation in the warehouse, a precise matching module is needed to generate initial fork control schemes. This requires first acquiring multiple sample access information, multiple sample three-dimensional access modules, and corresponding initial fork control schemes. This data will serve as the foundation for building the matching module. Next, a two-dimensional matching module framework is constructed, using the access information as the x-axis and the three-dimensional access modules as the y-axis. In this way, each combination of sample access information and sample three-dimensional access modules can find a corresponding sample point within this framework. Then, these sample points are marked with coordinates using the known initial fork control schemes. Thus, each sample point not only represents a specific combination of access information and three-dimensional access modules but also identifies its corresponding fork control scheme. Based on these coordinate-marked sample points, the matching module can be further constructed. The matching module can automatically match the closest sample point based on the input access information and three-dimensional access modules and output the corresponding initial fork control scheme. Finally, the actual M access information sets and M three-dimensional access modules are input into this constructed matching module. In this way, the matching module can generate M initial fork control schemes based on these actual data, providing guidance for subsequent fork operations.

[0045] Furthermore, the methods also include:

[0046] The M access information and the M 3D access modules are respectively input into the matching module to obtain M target coordinate points;

[0047] Based on the M target coordinate points, the matching module matches the corresponding M sample point sets, wherein each sample point set includes the k sample points closest to the target coordinate points, where k is an integer greater than or equal to 3;

[0048] Determine whether the sum of the Euclidean distances from the M sample point sets to the M target coordinate points exceeds a preset distance threshold. If so, remove the q sample points that are farthest from the target coordinate points, where q is an integer greater than or equal to 1.

[0049] If not, the coordinates of the M sample points are averaged to obtain M initial fork control schemes.

[0050] M actual access information points and M 3D access modules are used as input data and passed to the previously constructed matching module. The matching module calculates M target coordinate points based on this data, each representing a specific access requirement and forklift action parameters. Next, the matching module needs to find a corresponding set of sample points for each target coordinate point. This set contains the k closest sample points to the target coordinate point, where k is an integer greater than or equal to 3. Then, the sum of the Euclidean distances from each set of sample points to the corresponding target coordinate point needs to be calculated. Euclidean distance is a commonly used distance calculation method that accurately reflects the straight-line distance between two points. If this sum of distances exceeds a preset distance threshold, it indicates that the current set of sample points may not be accurate or dense enough and needs adjustment. If the sum of distances exceeds the preset threshold, the q sample points farthest from the target coordinate point are removed, where q is an integer greater than or equal to 1. If the sum of distances does not exceed the preset threshold, it indicates that the current set of sample points is relatively accurate. Finally, the coordinate labels corresponding to these sample point sets are averaged. Mean averaging is a commonly used data smoothing method that can reduce the impact of random errors and outliers on control schemes. By calculating the mean of the coordinate markers, M initial fork control schemes can be obtained.

[0051] Using the M initial fork control schemes as indexes, fork collision information in the historical control process is matched to obtain M sets of control risk points, where each set of control risk points includes multiple risk location coordinates;

[0052] To ensure the safety and efficiency of these operations, a detailed risk assessment of the fork control scheme is required. Therefore, fork collision information from historical control processes will be used to analyze the risk points of the M initial fork control schemes generated previously. Specifically, each initial fork control scheme is used as an index to search for similar fork operation records in historical control data. These records contain information on fork collision incidents and are crucial for risk assessment. Next, for each initial fork control scheme, it is compared and matched with the fork collision information from historical control processes to identify potential risk points that could lead to fork collisions in actual operation. These risk points include not only key information such as the fork's starting position and movement path but also detailed coordinates of the specific location at the time of collision. Through analysis, a set of control risk points can be generated for each initial fork control scheme. This set contains multiple risk location coordinates, each representing a potential fork collision risk point. These risk points provide crucial data support for subsequent risk control and optimization.

[0053] Based on the set of M control risk points, determine Q target control risk points and Q risk factors;

[0054] After evaluating the risk points of the M initial fork control schemes, the next step is to determine the Q target control risk points and the corresponding Q risk factors in order to carry out targeted optimization and control.

[0055] Furthermore, based on the set of M control risk points, Q target control risk points and Q risk factors are determined, and the method includes:

[0056] Traverse the M sets of control risk points and filter by the same location coordinates to obtain P sets of risk coordinate location data, each set of risk coordinate location data corresponding to one risk coordinate location;

[0057] Risk factor analysis is performed on the P risk coordinate location datasets respectively to obtain P risk factors;

[0058] Determine whether the P risk factors exceed the preset risk factors. If so, add Q target control risk points and obtain the corresponding Q risk factors.

[0059] By traversing M sets of control risk points and filtering for similar coordinates (i.e., finding identical or similar coordinate positions in all risk point sets and grouping them together), P risk coordinate location datasets are obtained. Each dataset corresponds to a unique risk coordinate location, allowing for a more accurate assessment of the risk level at that location. Next, risk factor analysis is performed on each risk coordinate location dataset. Risk factor analysis is a method for quantitatively assessing risk, considering multiple factors such as the frequency and severity of forklift collisions at that location, and the operational complexity of the location. By comprehensively considering these factors, a risk factor can be calculated for each risk coordinate location dataset to reflect the overall risk level of that location. After calculating the risk factors for the P risk coordinate location datasets, it is necessary to determine whether these risk factors exceed preset risk thresholds. Preset risk factors are set based on historical data, safety standards, and operational experience, representing an acceptable risk level. If the value of a risk factor exceeds the preset threshold, it means that the risk coordinate location is too high and requires close monitoring and management. For risk coordinate location data sets that exceed the preset risk factors, they need to be added to Q target control risk points to obtain the corresponding Q risk factors.

[0060] Furthermore, the methods also include:

[0061] The collision frequencies of P forks are determined by traversing the set of P risk coordinate locations.

[0062] The P collision frequencies of each fork are divided by the sum of the P collision frequencies of each fork to generate P first risk factors;

[0063] The maximum number of forks in a single collision is determined by traversing the P risk coordinate location data sets, and P maximum forks in a single collision are generated.

[0064] P second risk factors are generated based on the P maximum number of forks in a single collision.

[0065] The mean of the P first risk factors and the P second risk factors is used to identify the P risk factors.

[0066] During risk analysis, the number of fork collision events occurring at each of the P risk coordinate locations is counted, i.e., the fork collision frequency. Next, to transform the collision frequency at different risk coordinate locations into a comparable indicator, the collision frequency at each location is divided by the sum of the collision frequencies at all risk coordinate locations. This yields the proportion of collision frequencies at each location, i.e., the first risk factor. The first risk factor reflects the importance of each risk coordinate location in the overall fork collision events. Then, the P risk coordinate locations are traversed again to determine the maximum number of forks involved in each fork collision event. The P maximum fork numbers involved in a single collision are divided by the sum of the P maximum fork numbers involved in a single collision to generate P second risk factors. The second risk factors reflect the potential impact range of each risk coordinate location in fork collision events. Finally, in order to comprehensively consider the frequency of fork collisions and the severity of a single collision, the mean of the first and second risk factors at each risk coordinate position can be used to identify P risk factors. Each risk factor comprehensively considers the frequency of fork collisions and the severity of a single collision.

[0067] Determine whether the Q risk factors exceed the risk threshold. If so, take the corresponding target control risk point as the control risk point to be adjusted, and obtain K control risk points to be adjusted.

[0068] Each of the Q risk factors is compared with a preset risk threshold to determine whether these risk factors exceed the preset risk threshold. If the value of a risk factor is greater than or equal to the risk threshold, then the target control risk point corresponding to that risk factor is marked as a control risk point to be adjusted, thus obtaining K control risk points to be adjusted. Control risk points to be adjusted refer to target control risk points identified in risk management whose risk factors exceed the preset risk threshold. These points require detailed analysis, and appropriate measures must be taken to reduce the risk and ensure the safety and efficiency of forklift control.

[0069] The spatial structure of the target access area is interactively analyzed, and the spatial layout is optimized by combining the K risk location coordinates of the K control risk points to be adjusted, so as to obtain an optimized target access area.

[0070] After identifying K risk points to be adjusted, further analysis of their spatial layout within the warehouse is needed, particularly their relationship with the target access area. The target access area is where forks frequently move, making its spatial layout crucial for reducing risk and improving efficiency. Specifically, by examining the spatial structure of the target access area, details such as rack layout, fork aisle width, and goods storage methods can be understood. Next, the spatial layout of the target access area is optimized using the risk location coordinates of the K risk points. For example, the rack layout can be adjusted, and the width and direction of the fork aisles optimized to reduce the frequency of fork movement in high-risk areas; considering the storage methods, frequently accessed goods can be placed near the fork aisles to reduce fork movement distance and time.

[0071] Interact with the forklift resources in the target access area to construct a control optimization space;

[0072] After obtaining the optimized target access area, further consideration needs to be given to the interaction and management of fork resources to construct a control optimization space. Fork resources are key equipment in automated warehouses that perform goods storage and retrieval tasks; their operational efficiency and safety have a significant impact on the performance of the entire warehouse system. The configuration of fork resources within the optimized target access area needs to be analyzed, including the number, type, and operating status of the forks. Next, by interacting with the fork resources, a control optimization space can be constructed. The control optimization space is a virtual environment used to simulate the actual operation of the forks within the target access area. In the control optimization space, parameters such as the fork's running path, speed, and acceleration can be precisely controlled to achieve optimized configuration and efficient utilization of fork resources.

[0073] Based on the spatial structure of the control optimization space and the optimization target access area, the M initial fork control schemes are optimized to obtain the target fork interaction control scheme.

[0074] After constructing the spatial structure of the control optimization space and the target access area, the initial fork control scheme can be optimized based on this information to find the target fork interaction control scheme.

[0075] Furthermore, the methods include:

[0076] A first constraint is constructed based on the control optimization space, wherein the first constraint is that the number of forks called simultaneously in the target fork interaction control scheme is lower than the number of forks in the control optimization space.

[0077] Using the spatial structure of the coordinates of K risk locations in the optimized target access area, the adjustable positions of M initial fork control schemes are determined to obtain a set of M adjustable positions;

[0078] Based on the set of M adjustable positions and the M initial fork control schemes, the schemes are optimized to obtain multiple initial fork interaction control schemes.

[0079] Determine whether the multiple initial fork interaction control schemes meet the first constraint condition. If so, perform fitness calculation to obtain multiple initial fitnesss.

[0080] The initial fork interaction control scheme corresponding to the maximum value among the multiple initial fitness values ​​is taken as the target fork interaction control scheme.

[0081] The first constraint explicitly states that the number of forks invoked simultaneously must be less than the total number of forks in the control optimization space, ensuring that the target fork interaction control scheme does not exceed the system's resource limits during runtime. Determining the adjustable positions of the M initial fork control schemes using the spatial structure of the K risk location coordinates in the optimization target access area means analyzing the trajectory of each initial scheme near the risk location coordinates and identifying which positions are adjustable to achieve better performance during optimization. This step generates a set of adjustable positions for each initial scheme, which will serve as the basis for subsequent optimization. Based on the M adjustable position sets and the M initial fork control schemes, a neural network model is trained for scheme optimization, obtaining multiple initial fork interaction control schemes. Then, it is determined whether these initial fork interaction control schemes satisfy the first constraint. For those that satisfy the first constraint, fitness calculations are performed to obtain the fitness of each scheme, used to evaluate their performance under the constraint conditions. Finally, the initial fork interaction control scheme with the highest fitness is selected as the target fork interaction control scheme.

[0082] Furthermore, the methods also include:

[0083] The fork idle rate is extracted by traversing the multiple initial fork interaction control schemes to obtain multiple fork idle rates;

[0084] Calculate the completion cycle of the multiple initial fork interaction control schemes to obtain multiple control completion cycles;

[0085] The interaction control port occupancy rate of the target access area is optimized to obtain multiple port occupancy rates;

[0086] Multiple initial fitness values ​​are calculated based on the multiple fork idle rates, multiple control completion cycles, and multiple port occupancy rates.

[0087] In optimizing fork interaction control schemes, multiple dimensions need to be considered to comprehensively evaluate the performance of each scheme. Fork idle rate, control completion cycle, and interaction control port occupancy rate are all important performance indicators. The fork idle rate is extracted by traversing multiple initial fork interaction control schemes. The fork idle rate refers to the proportion of forks that are not used within a certain period, reflecting the utilization rate of fork resources. Therefore, it is necessary to collect fork idle data from each initial scheme, calculate its idle rate, and use these fork idle rates as one of the evaluation indicators. Secondly, the completion cycle of each initial fork interaction control scheme is calculated. The completion cycle refers to the total time required from the start of the task to its completion, reflecting the efficiency of the scheme. A shorter completion cycle usually means higher efficiency. Therefore, it is necessary to record the actual running time of each scheme, calculate its completion cycle, and use these completion cycles as one of the evaluation indicators. In addition, the interaction control port occupancy rate of the target access area needs to be optimized. The interaction control port occupancy rate refers to the proportion of control ports that are occupied within a certain period, reflecting the communication efficiency of the system. A higher port occupancy rate may mean busy communication, but it may also mean high data transmission efficiency. Therefore, it is necessary to collect port occupancy data for each scheme, calculate its occupancy rate, and use these port occupancy rates as one of the evaluation metrics. Finally, based on the aforementioned multiple fork idle rates, multiple control completion cycles, and multiple port occupancy rates, multiple initial fitness scores can be calculated. Weights can be assigned to each metric according to actual needs, and then a weighted summation is used to calculate the fitness of each scheme. This method allows for a comprehensive evaluation of the performance of each initial fork interaction control scheme and provides a basis for subsequent optimization.

[0088] In summary, the embodiments of this application have at least the following technical effects:

[0089] First, retrieve M access information entries and M three-dimensional access modules from the target access area, ensuring a one-to-one correspondence. Next, use this information to match M initial fork control schemes. Then, using the M initial fork control schemes as indexes, match fork collision information in historical data to obtain a set of M control risk points, containing multiple risk location coordinates for assessing potential risks. Based on the control risk point set, determine Q target control risk points and corresponding risk factors. Evaluate the Q risk factors; if any factor exceeds a risk threshold, the corresponding target control risk point is designated as a control risk point to be adjusted, forming K control risk points to be adjusted. Then, combine the spatial structure of the target access area and the location coordinates of the K control risk points to be adjusted, performing spatial layout optimization to obtain an optimized target access area. Next, optimize fork resources and construct a control optimization space to support subsequent control scheme optimization. Finally, based on the control optimization space and the optimized target access area structure, optimize the M initial fork control schemes to obtain the final target fork interaction control scheme. This invention solves the technical problem of insufficient safety in fork operation in existing technologies, and achieves the technical effect of improving the efficiency and safety of fork operation.

[0090] Example 2

[0091] Based on the same inventive concept as the wireless communication-based fork interaction control method in the foregoing embodiments, such as Figure 2 As shown, this application provides a forklift interaction control system based on wireless communication. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0092] Calling module 11, the calling module 11 is used to interact with the target access area, and call M access information and M three-dimensional access modules, wherein the M access information and the M three-dimensional access modules correspond one-to-one;

[0093] The first matching module 12 is used to match M initial fork control schemes based on the M access information and the M three-dimensional access modules;

[0094] The second matching module 13 is used to match the fork collision information in the historical control process with the M initial fork control schemes as indexes to obtain M sets of control risk points, wherein each set of control risk points includes multiple risk location coordinates.

[0095] Risk determination module 14, which is used to determine Q target control risk points and Q risk factors based on the set of M control risk points;

[0096] The judgment module 15 is used to determine whether the Q risk factors exceed the risk threshold. If so, the corresponding target control risk point is taken as the control risk point to be adjusted, and K control risk points to be adjusted are obtained.

[0097] The space optimization module 16 is used to interact with the spatial structure of the target access area, and optimize the spatial layout by combining the K risk position coordinates of the K control risk points to be adjusted, so as to obtain an optimized target access area.

[0098] Space construction module 17, which is used to interact with the forklift resources of the optimized target access area to construct a control optimization space;

[0099] The scheme optimization module 18 is used to optimize M initial fork control schemes based on the spatial structure of the control optimization space and the optimization target access area to obtain a target fork interaction control scheme.

[0100] Furthermore, the first matching module 12 is used to perform the following method:

[0101] The data for constructing the matching module includes multiple sample access information, multiple sample three-dimensional access modules, and multiple sample initial forklift control schemes.

[0102] The framework of the matching module is constructed with the access information as the x-axis and the stereo access module as the y-axis. The multiple sample access information and the multiple sample stereo access modules are input into the framework to obtain multiple sample points.

[0103] The coordinates of the multiple sample points are marked using the multiple initial fork control schemes for the samples.

[0104] The matching module is constructed based on the multiple sample points and their corresponding coordinate identifiers;

[0105] The M access information and the M three-dimensional access modules are respectively input into the matching module to generate the M initial fork control schemes.

[0106] Furthermore, the first matching module 12 is used to perform the following method:

[0107] The M access information and the M 3D access modules are respectively input into the matching module to obtain M target coordinate points;

[0108] Based on the M target coordinate points, the matching module matches the corresponding M sample point sets, wherein each sample point set includes the k sample points closest to the target coordinate points, where k is an integer greater than or equal to 3;

[0109] Determine whether the sum of the Euclidean distances from the M sample point sets to the M target coordinate points exceeds a preset distance threshold. If so, remove the q sample points that are farthest from the target coordinate points, where q is an integer greater than or equal to 1.

[0110] If not, the coordinates of the M sample points are averaged to obtain M initial fork control schemes.

[0111] Furthermore, the risk determination module 14 is used to perform the following method:

[0112] Traverse the M sets of control risk points and filter by the same location coordinates to obtain P sets of risk coordinate location data, each set of risk coordinate location data corresponding to one risk coordinate location;

[0113] Risk factor analysis is performed on the P risk coordinate location datasets respectively to obtain P risk factors;

[0114] Determine whether the P risk factors exceed the preset risk factors. If so, add Q target control risk points and obtain the corresponding Q risk factors.

[0115] Furthermore, the risk determination module 14 is used to perform the following method:

[0116] The collision frequencies of P forks are determined by traversing the set of P risk coordinate locations.

[0117] The P collision frequencies of each fork are divided by the sum of the P collision frequencies of each fork to generate P first risk factors;

[0118] The maximum number of forks in a single collision is determined by traversing the P risk coordinate location data sets, and P maximum forks in a single collision are generated.

[0119] P second risk factors are generated based on the P maximum number of forks in a single collision.

[0120] The mean of the P first risk factors and the P second risk factors is used to identify the P risk factors.

[0121] Furthermore, the scheme optimization module 18 is used to perform the following method:

[0122] A first constraint is constructed based on the control optimization space, wherein the first constraint is that the number of forks called simultaneously in the target fork interaction control scheme is lower than the number of forks in the control optimization space.

[0123] Using the spatial structure of the coordinates of K risk locations in the optimized target access area, the adjustable positions of M initial fork control schemes are determined to obtain a set of M adjustable positions;

[0124] Based on the set of M adjustable positions and the M initial fork control schemes, the schemes are optimized to obtain multiple initial fork interaction control schemes.

[0125] Determine whether the multiple initial fork interaction control schemes meet the first constraint condition. If so, perform fitness calculation to obtain multiple initial fitnesss.

[0126] The initial fork interaction control scheme corresponding to the maximum value among the multiple initial fitness values ​​is taken as the target fork interaction control scheme.

[0127] Furthermore, the scheme optimization module 18 is used to perform the following method:

[0128] The fork idle rate is extracted by traversing the multiple initial fork interaction control schemes to obtain multiple fork idle rates;

[0129] Calculate the completion cycle of the multiple initial fork interaction control schemes to obtain multiple control completion cycles;

[0130] The interaction control port occupancy rate of the target access area is optimized to obtain multiple port occupancy rates;

[0131] Multiple initial fitness values ​​are calculated based on the multiple fork idle rates, multiple control completion cycles, and multiple port occupancy rates.

[0132] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0134] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A forklift interaction control method based on wireless communication, characterized in that, The method includes: The interactive target access area retrieves M access information items and M three-dimensional access modules, wherein the M access information items and the M three-dimensional access modules correspond one-to-one; Based on the M access information and the M three-dimensional access modules, M initial fork control schemes are matched; Using the M initial fork control schemes as indexes, fork collision information in the historical control process is matched to obtain M sets of control risk points, where each set of control risk points includes multiple risk location coordinates; Based on the set of M control risk points, determine Q target control risk points and Q risk factors; Determine whether the Q risk factors exceed the risk threshold. If so, take the corresponding target control risk point as the control risk point to be adjusted, and obtain K control risk points to be adjusted. The spatial structure of the target access area is interactively analyzed, and the spatial layout is optimized by combining the K risk location coordinates of the K control risk points to be adjusted, so as to obtain an optimized target access area. Interact with the forklift resources in the target access area to construct a control optimization space; Based on the spatial structure of the control optimization space and the optimization target access area, the M initial fork control schemes are optimized to obtain the target fork interaction control scheme.

2. The method as described in claim 1, characterized in that, Based on the M access information and the M three-dimensional access modules, M initial fork control schemes are matched, and the method further includes: The data for constructing the matching module includes multiple sample access information, multiple sample three-dimensional access modules, and multiple sample initial forklift control schemes. The framework of the matching module is constructed with the access information as the x-axis and the stereo access module as the y-axis. The multiple sample access information and the multiple sample stereo access modules are input into the framework to obtain multiple sample points. The coordinates of the multiple sample points are marked using the multiple initial fork control schemes for the samples. The matching module is constructed based on the multiple sample points and their corresponding coordinate identifiers; The M access information and the M three-dimensional access modules are respectively input into the matching module to generate the M initial fork control schemes.

3. The method as described in claim 2, characterized in that, The method further includes: The M access information and the M 3D access modules are respectively input into the matching module to obtain M target coordinate points; Based on the M target coordinate points, the matching module matches the corresponding M sample point sets, wherein each sample point set includes the k sample points closest to the target coordinate points, where k is an integer greater than or equal to 3; Determine whether the sum of the Euclidean distances from the M sample point sets to the M target coordinate points exceeds a preset distance threshold. If so, remove the q sample points that are farthest from the target coordinate points, where q is an integer greater than or equal to 1. If not, the coordinates of the M sample points are averaged to obtain M initial fork control schemes.

4. The method as described in claim 1, characterized in that, Based on the set of M control risk points, Q target control risk points and Q risk factors are determined, and the method further includes: Traverse the M sets of control risk points and filter by the same location coordinates to obtain P sets of risk coordinate location data, each set of risk coordinate location data corresponding to one risk coordinate location; Risk factor analysis is performed on the P risk coordinate location datasets respectively to obtain P risk factors; Determine whether the P risk factors exceed the preset risk factors. If so, add Q target control risk points and obtain the corresponding Q risk factors.

5. The method as described in claim 4, characterized in that, The method further includes: The collision frequencies of P forks are determined by traversing the set of P risk coordinate locations. The P collision frequencies of each fork are divided by the sum of the P collision frequencies of each fork to generate P first risk factors. The maximum number of forks in a single collision is determined by traversing the P risk coordinate location data sets, and P maximum forks in a single collision are generated. P second risk factors are generated based on the P maximum number of forks in a single collision. The mean of the P first risk factors and the P second risk factors is used to identify the P risk factors.

6. The method as described in claim 1, characterized in that, The method further includes: A first constraint is constructed based on the control optimization space, wherein the first constraint is that the number of forks called simultaneously in the target fork interaction control scheme is lower than the number of forks in the control optimization space. Using the spatial structure of the coordinates of K risk locations in the optimized target access area, the adjustable positions of M initial fork control schemes are determined to obtain a set of M adjustable positions; Based on the set of M adjustable positions and the M initial fork control schemes, the schemes are optimized to obtain multiple initial fork interaction control schemes. Determine whether the multiple initial fork interaction control schemes meet the first constraint condition. If so, perform fitness calculation to obtain multiple initial fitnesss. The initial fork interaction control scheme corresponding to the maximum value among the multiple initial fitness values ​​is taken as the target fork interaction control scheme.

7. The method as described in claim 6, characterized in that, The method further includes: The fork idle rate is extracted by traversing the multiple initial fork interaction control schemes to obtain multiple fork idle rates; Calculate the completion cycle of the multiple initial fork interaction control schemes to obtain multiple control completion cycles; The interaction control port occupancy rate of the target access area is optimized to obtain multiple port occupancy rates. Multiple initial fitness values ​​are calculated based on the multiple fork idle rates, multiple control completion cycles, and multiple port occupancy rates.

8. A forklift interaction control system based on wireless communication, characterized in that, For implementing the wireless communication-based forklift interaction control method according to any one of claims 1-7, the system comprises: The calling module is used to interact with the target access area and retrieve M access information and M three-dimensional access modules, wherein the M access information and the M three-dimensional access modules correspond one-to-one. The first matching module is used to match M initial fork control schemes based on the M access information and the M three-dimensional access modules; The second matching module is used to match the fork collision information in the historical control process with the M initial fork control schemes as indexes to obtain M sets of control risk points, wherein each set of control risk points includes multiple risk location coordinates; A risk determination module is used to determine Q target control risk points and Q risk factors based on the set of M control risk points; The judgment module is used to determine whether the Q risk factors exceed the risk threshold. If so, the corresponding target control risk point is taken as the control risk point to be adjusted, and K control risk points to be adjusted are obtained. A spatial optimization module is used to interact with the spatial structure of the target access area and optimize the spatial layout by combining the K risk location coordinates of the K control risk points to be adjusted, so as to obtain an optimized target access area. A space construction module is used to interact with the forklift resources of the optimized target access area and construct a control optimization space; The scheme optimization module is used to optimize M initial fork control schemes based on the spatial structure of the control optimization space and the optimization target access area to obtain a target fork interaction control scheme.

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