Enterprise safety production informatization data management method and system

Abstract

The present application relates to warehouse management technical field, specifically to a kind of enterprise safety production informationization data management method and system, based on the actual running speed of the warehousing robot in warehouse area and the number of robots, the aggregation characteristics of warehouse area are obtained, according to the aggregation characteristics of warehouse area, and the proportion of the number of robots of the robot in the loading state in warehouse area, the collision risk degree of warehouse area is obtained, according to the position transfer flow direction of the robot in the loading state in warehouse area, combined with the collision risk degree of warehouse area, the collision risk change degree of warehouse area is obtained, so as to determine the running state of the robot in the loading state, the possibility of collision can be reduced when warehousing robot carries out goods transportation, improve operating efficiency, so as to realize warehousing safety management, realize the informationization management of enterprise safety production.

Description

Technical Field

[0001] This invention relates to the field of warehouse management technology, specifically to an enterprise safety production information data management method and system. Background Technology

[0002] With the development of modern logistics and intelligent manufacturing, warehouse robots have become core equipment for enterprises' information management of safe production. In large, high-density warehouses, the collaborative operation and path planning of multiple robots are key technologies to ensure both production efficiency and safety.

[0003] Currently, intelligent warehouse management systems primarily focus on improving the accuracy of goods storage and sorting efficiency. For example, Chinese patent CN119027031B discloses an intelligent storage method and device based on cloud warehousing. This technology performs omnidirectional scanning and feature recognition of goods, combines RFID tags for initial positioning, and utilizes an encoder-decoder model for AGV path planning and multi-agent task scheduling. Its core advantage lies in achieving real-time precise positioning of goods and AGVs by integrating RFID and sensor data, thereby optimizing storage location allocation and sorting strategies.

[0004] While existing technologies acquire the trajectory and location information of AGVs, they do not utilize the real-time motion characteristics of the AGV group to infer road congestion risks. This results in an inability to dynamically adjust the operating status of loading robots and thus proactively avoid risks. Therefore, there is an urgent need for a proactive collision avoidance management system capable of operating in high-load warehousing environments based on the proportion of cargo carried and the risk of collisions. Summary of the Invention

[0005] To address the technical problem of collisions easily occurring between warehouse robots during cargo transportation, which affects warehouse management safety, the present invention aims to provide an enterprise safety production information data management method and system. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a method for information-based data management of enterprise safety production, comprising: Based on the actual operating speed and number of warehouse robots within the warehouse area, the clustering characteristics of the warehouse area are obtained; the warehouse robots include robots in the loading state. The collision risk level of the warehouse area is determined based on the clustering characteristics of the warehouse area and the proportion of robots in the loading state within the warehouse area; the collision risk level is directly proportional to the clustering characteristics and the proportion of robots. Based on the location transfer direction of the loading robots in the warehouse area, and combined with the collision risk level of the warehouse area, the degree of change in collision risk in the warehouse area is obtained; The operating status of the loading robot is determined based on the changing degree of collision risk in the warehouse area.

[0006] In one exemplary embodiment, the clustering characteristics of the warehouse area are obtained based on the actual operating speed and number of warehouse robots within the warehouse area, including: Obtain the actual operating speed of the warehouse robots within the warehouse area at each moment, as well as the number of warehouse robots within the warehouse area at each moment; Based on the speed difference between the actual operating speed and the rated operating speed, and the number of robots, abnormal state parameters of the warehouse area at various times are obtained; the abnormal state parameters are proportional to the speed difference and the number of robots. The clustering characteristics of the warehouse area are obtained by analyzing the abnormal state parameters of the warehouse area at various times and the differences between the abnormal state parameters of two adjacent times.

[0007] In an exemplary embodiment, the clustering characteristics of the warehouse area are obtained based on the abnormal state parameters of the warehouse area at various times and the differences in the abnormal state parameters between two adjacent times, including: Calculate the average value of the abnormal state parameters of the warehouse area at each time point to obtain the first average value, and calculate the average value of the difference between the abnormal state parameters of two adjacent time points in the warehouse area to obtain the second average value. Based on the first average value and the second average value, the clustering characteristics of the warehouse area are obtained, wherein the clustering characteristics are proportional to the first average value and the second average value.

[0008] In one exemplary embodiment, based on the location transfer direction of the loading robot in the warehouse area and combined with the collision risk level of the warehouse area, the degree of change in collision risk in the warehouse area is obtained, including: The latest percentage of loading robots in the second warehouse area is obtained based on the number of loading robots that leave the second warehouse area after entering the first warehouse area; the first warehouse area can be any warehouse area, and the second warehouse area is one of the warehouse areas adjacent to the first warehouse area. Based on the latest robot quantity ratio and the clustering characteristics of the second warehouse area, the latest collision risk level of the second warehouse area is obtained; The difference between the initial collision risk level and the latest collision risk level of the second warehouse area is obtained to obtain the collision risk change index of the second warehouse area.

[0009] In one exemplary embodiment, the operating status of the loading robot is determined based on the degree of change in collision risk in the warehouse area, including: The collision risk change level of each second warehouse area adjacent to the first warehouse area is obtained, and the second warehouse area corresponding to the largest collision risk change level is taken as the next target warehouse area of ​​the first warehouse area, so that the loading robot can move from the first warehouse area to the next target warehouse area.

[0010] In one exemplary embodiment, the operating status of the loading robot is determined based on the degree of change in collision risk in the warehouse area, including: The degree of abnormality in the warehouse area is determined based on the runtime of the loading robot in the warehouse area and the degree of change in the collision risk. The operating status of the loading robot is determined based on the degree of abnormality in the warehouse area.

[0011] In an exemplary embodiment, the degree of abnormality in the warehouse area's state is determined based on the runtime of the loading robot in the warehouse area and the degree of change in collision risk, including: The actual running time of the loading robot in the second warehouse area is obtained, and the difference between the actual running time and the preset running time is obtained; the second warehouse area is one of the warehouse areas adjacent to the first warehouse area, and the first warehouse area is any warehouse area; Based on the time difference and the degree of change in collision risk of the second required warehouse area, the degree of abnormality of the state of the second required warehouse area is obtained; the second required warehouse area is the second warehouse area with a positive degree of change in collision risk, and the degree of abnormality of the state is directly proportional to the time difference and inversely proportional to the degree of change in collision risk.

[0012] In one exemplary embodiment, the formula for calculating the degree of abnormality in the state of the second required warehouse area is as follows: ; in, The degree of abnormality in the state of the second required warehouse area. For the preset runtime, The actual runtime of the loading robot in the second warehouse area. The degree of change in collision risk for the second required warehouse area. To assess the varying degrees of collision risk in the second required warehouse area Perform negative correlation mapping, where norm represents the linear normalization function.

[0013] In one exemplary embodiment, determining the operating status of the loading robot based on the degree of abnormality in the warehouse area includes: The state anomaly level of each second required warehouse area adjacent to the first warehouse area is obtained, and the second required warehouse area corresponding to the minimum state anomaly level is taken as the next target warehouse area of ​​the first warehouse area, so that the loading robot can move from the first warehouse area to the next target warehouse area.

[0014] In a second aspect of the present invention, an enterprise safety production information data management system is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-described enterprise safety production information data management method when the program instructions are executed.

[0015] The present invention has the following beneficial effects: By dividing the warehouse into areas, and based on the relevant characteristics of the storage robots within the warehouse area, and the changes in the relevant characteristics of the warehouse area when the robots in the loading state change, the present invention obtains the degree of change in the collision risk of the warehouse area, realizes risk warning, and determines the operating status of the loading robots. This can reduce the possibility of collisions between storage robots and goods during transportation, improve operating efficiency, and thus realize warehouse safety management and information management of enterprise safety production. Attached Figure Description

[0016] Figure 1 This is a simplified schematic diagram of a warehouse provided in one embodiment of the present invention; Figure 2 This is a flowchart of an enterprise safety production information data management method provided in one embodiment of the present invention; Figure 3 This is a flowchart of step 1 provided in one embodiment of the present invention; Figure 4 This is a flowchart of steps 1-3 provided in one embodiment of the present invention; Figure 5 This is a flowchart of step 3 provided in one embodiment of the present invention; Figure 6 This is a flowchart of step 4 provided in one embodiment of the present invention; Figure 7 This is a flowchart of step 4-1 provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] This invention achieves precise positioning by dividing the warehouse into zones, and then monitors the operating status of the storage robots in each warehouse zone to reduce collision risks, improve efficiency, and realize information-based management of safe production.

[0020] In this embodiment, the norm function specifically employs the range transformation method to map the input data to the [0,1] interval. The specific formula is as follows: ,in and These are the historical minimum and maximum values ​​of this parameter within the preset monitoring period, respectively. season .

[0021] In this embodiment, the warehouse is divided into multiple warehouse areas. It should be understood that the number, shape, and size of these warehouse areas are determined based on actual conditions. Each warehouse robot operating within the warehouse has an independent code. Furthermore, each warehouse robot is equipped with a speed sensor to detect its real-time speed, and a locator to obtain its position information within the warehouse. This allows for the acquisition of the warehouse area where the robot is located in real time, thus enabling real-time tracking of the robot's movement trajectory.

[0022] In addition, to facilitate warehouse management, surveillance cameras are installed in each warehouse area to monitor the area 24 hours a day. Each surveillance camera has its own designated location, such as... Figure 1 As shown.

[0023] In one exemplary embodiment, the enterprise safety production information data management system is configured as a background monitoring system to acquire information about each warehouse area and each warehouse robot, and to execute an enterprise safety production information data management method provided in this embodiment for data processing based on the acquired information.

[0024] like Figure 2As shown in the figure, this embodiment provides a method for enterprise safety production information data management, which includes the following steps: Step 1: Based on the actual operating speed and number of warehouse robots within the warehouse area, obtain the clustering characteristics of the warehouse area.

[0025] The following explanation uses any warehouse area as an example.

[0026] When there are a large number of warehouse robots in a warehouse area, and the operating speed of the warehouse robots drops abnormally, the probability of a collision risk in that warehouse area is high; conversely, the probability of a collision risk is low.

[0027] A monitoring period is preset, the length of which is set according to actual needs. To ensure timely control, the length of the monitoring period can be set relatively short. This monitoring period includes multiple sampling times, referred to simply as times.

[0028] In one exemplary embodiment, such as Figure 3 As shown, a specific process for obtaining the clustering characteristics of a warehouse area is presented, including: Step 1-1: Obtain the actual operating speed of the warehouse robots within the warehouse area at each moment, and the number of warehouse robots within the warehouse area at each moment.

[0029] If there are multiple warehouse robots within the warehouse area, the actual operating speed of each robot at each moment within the monitoring period is obtained, and the average actual operating speed at each moment is calculated. This average actual operating speed is taken as the actual operating speed of the warehouse robot at each moment. Since the warehouse robots are constantly running, the number of robots within the warehouse area may change at different times. Therefore, the number of robots within the warehouse area at each moment within the monitoring period is also determined.

[0030] Steps 1-2: Based on the speed difference between the actual operating speed and the rated operating speed, and the number of robots, obtain the abnormal state parameters of the warehouse area at various times.

[0031] The warehouse robot is pre-set to have a rated operating speed, which is the maximum speed the robot can reach within the warehouse; essentially, its speed limit. Normally, the actual operating speed of the warehouse robot is less than or equal to the rated operating speed. Therefore, the smaller the difference between the actual and rated operating speeds, the smoother the robot's operation and the lower the likelihood of malfunctions. Conversely, the larger the difference, the more congested the robot's operation and the greater the likelihood of malfunctions.

[0032] For any given moment, the speed difference between the actual operating speed and the rated operating speed is obtained; specifically, the difference between the rated operating speed and the actual operating speed. Based on the number of robots at that moment, an abnormal state parameter for the warehouse area is obtained. The more warehouse robots present in the warehouse area, the higher the probability of congestion, and vice versa. Therefore, the abnormal state parameter is directly proportional to the speed difference and the number of robots. In an exemplary embodiment, the product of the speed difference in the warehouse area at that moment and the number of robots at that moment is used as the abnormal state parameter for the warehouse area at that moment. The larger the value of the abnormal state parameter, the greater the probability that the warehouse robots will experience an abnormal state.

[0033] Steps 1-3: Based on the abnormal state parameters of the warehouse area at each time point and the differences in abnormal state parameters between two adjacent time points, the clustering characteristics of the warehouse area are obtained.

[0034] The more drastic the changes in abnormal state parameters during the monitoring period, the higher the likelihood of warehouse robot aggregation in that area. Furthermore, the higher the level of the abnormal state parameters, the higher the likelihood of warehouse robot aggregation. Therefore, the aggregation characteristics of the warehouse area are obtained based on the abnormal state parameters of the warehouse area at various times and the differences between the abnormal state parameters of two adjacent times. In an exemplary embodiment, such as... Figure 4 As shown, the process of obtaining the clustering characteristics of the warehouse area includes: Step 1-3-1: Calculate the average value of the abnormal state parameters of the warehouse area at each time point to obtain the first average value, and calculate the average value of the difference between the abnormal state parameters of two adjacent times in the warehouse area to obtain the second average value.

[0035] Step 1-3-2: Based on the first average and the second average, obtain the clustering characteristics of the warehouse area. The clustering characteristics are proportional to the first average and the second average.

[0036] The following is a formula for calculating the clustering characteristics of a warehouse area: ; Where k represents the clustering characteristics of the warehouse area. For the monitoring period of the first m represents the number of moments in the monitoring period. For the first Abnormal state parameters at each time point For the first Abnormal state parameters at each time point This is a linear normalization function. In this embodiment, normalization can be achieved using linear normalization, or it can be achieved using the following normalization method: Where n is the input data, It is an exponential function with the natural constant e as its base.

[0037] In this embodiment, the monitoring time period is set to 5 to 30 seconds, for example, 10 seconds. If the sampling frequency is 1Hz, then the number of time points... .

[0038] This is the absolute value of the difference between the abnormal state parameters at two adjacent time points. It represents the difference between the abnormal state parameters at two adjacent time points. The larger the value, the greater the difference between the abnormal state parameters at two adjacent time points.

[0039] The second average value is the average difference between the abnormal state parameters of two adjacent moments in the warehouse area. The larger the value, the more obvious the change in abnormal state and the higher the probability of clustering. Conversely, the lower the value, the lower the probability of clustering.

[0040] The first average value is the average value of the abnormal state parameters at each time point in the warehouse area. It represents the overall level of the abnormal state parameters. The larger the value, the more obvious the changes in the abnormal state and the higher the probability of clustering. Conversely, the lower the value, the lower the probability of clustering.

[0041] Therefore, the higher the clustering characteristics of a warehouse area, the higher the likelihood of collisions with warehouse robots.

[0042] By using the above process, the clustering characteristics of each warehouse area can be obtained.

[0043] In a preferred embodiment, considering the non-uniformity of the physical spatial layout of the warehouse, the actual risks posed by different areas under the same clustering characteristics vary. For example, intersections, aisle entrances, or charging station entrances are high-risk bottleneck areas, while long straight sections of main roads are low-risk open areas. To further improve the accuracy of the clustering characteristics, after obtaining the clustering characteristics k of the aforementioned warehouse areas, a step of correcting the clustering characteristics based on map topology weights is also included: First, bottleneck areas are pre-marked in the warehouse map database, and regional weight coefficients are assigned to different warehouse areas. Specifically, for warehouse areas marked as bottleneck areas, the following settings are configured: The value is 1.2; for unmarked ordinary areas or open areas, set to... .

[0044] Secondly, using regional weighting coefficients The clustering feature k obtained in step 1-3-2 is weighted and corrected to obtain the corrected clustering feature. The calculation formula is: .

[0045] By introducing a regional weighting coefficient, the system can significantly improve the clustering characteristic value of bottleneck areas under the same speed difference and number of robots. In subsequent steps, even if the number of robots in the bottleneck area is small, as long as a speed decreases, the collision risk calculated by the system will increase, thus prompting the system to identify the congestion risk of key nodes earlier. This allows robots to bypass bottlenecks a year in advance during subsequent loading phases, avoiding the risk of cargo tipping and deadlock caused by heavy-load robots suddenly stopping at narrow intersections. The modified clustering characteristic is used in the calculations of subsequent steps 2 to 4. Replace the original clustering feature k for processing.

[0046] Step 2: Based on the clustering characteristics of the warehouse area and the proportion of robots in the loading state within the warehouse area, the collision risk level of the warehouse area is obtained.

[0047] Warehouse robots operating in warehouses mainly fall into two categories. The first type is a loading robot: a robot that has completed loading and is moving from the loading point to the target unloading point. The second type is an unloading robot: a robot that has unloaded and is heading to the loading point to load more goods. Therefore, the warehouse robots in this embodiment include two types: loading robots and unloading robots.

[0048] During operation, loading robots will encounter unloading robots heading to the loading point in different warehouse areas. Because loading robots are carrying goods, their size is larger than that of unloading robots. Therefore, with the same number of robots, the probability of a collision when unloading robots cluster together is lower than that when loading robots cluster together. Furthermore, the more loading robots there are, the higher the probability of a collision.

[0049] Obtain the total number of warehouse robots within the warehouse area, including both loading and unloading robots. Then, calculate the ratio of loading robots to the total number of warehouse robots in the warehouse area. This ratio represents the percentage of loading robots. A higher percentage of loading robots in the warehouse area indicates a higher probability of robot collisions.

[0050] Therefore, the degree of collision risk is directly proportional to the aggregation characteristics and the proportion of the number of robots. Based on the aggregation characteristics of the warehouse area and the proportion of robots in the loading state in the warehouse area, the degree of collision risk of the warehouse area can be obtained. In an exemplary embodiment, the product of the proportion of robots in the loading state in the warehouse area and the aggregation characteristics of the warehouse area is calculated, and this product is used as the degree of collision risk of the warehouse area.

[0051] Step 3: Based on the location transfer direction of the loading robots in the warehouse area, and combined with the collision risk level of the warehouse area, obtain the degree of change in collision risk in the warehouse area.

[0052] For any given warehouse area, when a collision risk is anticipated, timely adjustments are needed to the warehouse robots operating within that area to guide robots in the loading phase away from the warehouse. Therefore, it is necessary to analyze the collision risks of warehouse robots in adjacent warehouse areas, obtain the aggregation status of warehouse robots in different areas, and divide paths based on the operational status of the warehouse robots.

[0053] In one exemplary embodiment, a specific process for obtaining the degree of change in collision risk is given below, such as... Figure 5 As shown: Step 3-1: Based on the number of loading robots that leave the second warehouse area after entering the first warehouse area, obtain the latest percentage of loading robots in the second warehouse area.

[0054] The loading robot's destination is the unloading point, therefore, it has a specific direction of movement. Furthermore, based on the division of warehouse areas, any given warehouse area has at least one adjacent warehouse area, and these adjacent warehouse areas contain multiple potential next warehouse areas that the loading robot may enter. Therefore, for ease of explanation, we define the first warehouse area as any warehouse area, and the second warehouse area as one of the warehouse areas adjacent to the first warehouse area.

[0055] If a loading robot moves from the first warehouse area to the second warehouse area, some loading robots in the second warehouse area may also leave. Therefore, when moving loading robots from the first warehouse area, it is necessary to consider the changes in data of loading robots heading to the target area (i.e., the second warehouse area).

[0056] If a loading robot enters the second warehouse area from the first warehouse area, the number of loading robots that leave the second warehouse area is obtained. Combined with the number of loading robots that enter the second warehouse area from the first warehouse area, the latest number of loading robots in the second warehouse area and the percentage of the latest number of loading robots in the second warehouse area are obtained.

[0057] The number of robots in the original loading state in the second warehouse area is set to... The number of loading robots leaving the second warehouse area is The number of loading robots entering the second warehouse area from the first warehouse area is Therefore, the latest number of robots in the loading status of the second warehouse area is: Then, the latest number of loading robots in the second warehouse area is added to the number of unloading robots in the second warehouse area to obtain the total number of warehouse robots in the second warehouse area. The latest percentage of loading robots in the second warehouse area is calculated based on the latest number of loading robots in the second warehouse area and the total number of storage robots in the second warehouse area.

[0058] Step 3-2: Based on the latest robot quantity ratio and the clustering characteristics of the second warehouse area, obtain the latest collision risk level of the second warehouse area.

[0059] Based on the latest robot quantity ratio and the clustering characteristics of the second warehouse area obtained in step 1, the collision risk level of the second warehouse area is obtained and defined as the latest collision risk level of the second warehouse area.

[0060] Step 3-3: Obtain the difference between the initial collision risk level and the latest collision risk level of the second warehouse area to obtain the collision risk change index of the second warehouse area.

[0061] The collision risk change index for the second warehouse area is obtained by comparing the initial collision risk level (i.e., the collision risk level of the second warehouse area before the change in the proportion of robots in the second warehouse area) with the latest collision risk level of the second warehouse area. In an exemplary embodiment, the calculation formula for the collision risk change index is given below: ; In the formula for calculating the collision risk change index, This serves as an indicator of changes in collision risk in the second warehouse area. The initial collision risk level for the second warehouse area. This represents the clustering characteristics of the second warehouse area.

[0062] The latest percentage of robots in the loading status of the second warehouse area.

[0063] The latest collision risk level for the second warehouse area.

[0064] The collision risk change index for the second warehouse area is obtained by comparing before and after. It should be understood that this value can be positive, 0, or negative. When it is positive, it means that the collision risk level of the second warehouse area is decreasing, and the larger the value, the greater the decrease in the collision risk level of the second warehouse area. The lower the collision risk level of the second warehouse area, the lower the risk of collision with the warehouse robots. When it is 0, it means that the collision risk level of the second warehouse area remains unchanged. When it is negative, it means that the collision risk level of the second warehouse area is increasing.

[0065] Using the above method, we obtain the collision risk change index for each adjacent warehouse area after the loading robot in the first warehouse area enters each of its adjacent warehouse areas. The larger the collision risk change index, the higher the safety of the corresponding warehouse area. It should be noted that in this embodiment, the collision risk change value is positive, and the larger the value, the more significant the risk reduction after scheduling, and the safer the system.

[0066] Step 4: Determine the operating status of the loading robot based on the degree of change in collision risk in the warehouse area.

[0067] Since a higher collision risk change index indicates a higher safety level for the corresponding warehouse area, in an exemplary embodiment, the collision risk change level of each second warehouse area adjacent to the first warehouse area is obtained, and the second warehouse area corresponding to the highest collision risk change level is taken as the next target warehouse area of ​​the first warehouse area, so that the loading robot can move from the first warehouse area to the next target warehouse area, thereby realizing path planning for the loading robot.

[0068] It should be understood that the loading robot moves towards the unloading point, and the robots move sequentially. There is no phenomenon of the loading robot turning around. Therefore, when analyzing the degree of change in collision risk between adjacent warehouse areas, the warehouse areas that have already been passed are not considered. That is, from the adjacent warehouse areas that the loading robot has not yet passed, the warehouse area corresponding to the largest degree of change in collision risk is selected as the next target warehouse area.

[0069] In another exemplary embodiment, to further improve the accuracy of path planning for the loading robot, instead of performing subsequent analysis solely based on the collision risk change index, the collision risk change index is combined with the loading robot's runtime to determine the robot's operating status, such as... Figure 6 As shown, it includes the following steps: Step 4-1: Based on the runtime of the loading robot in the warehouse area and the degree of change in collision risk, determine the degree of abnormality in the warehouse area's state.

[0070] First, based on the runtime of the loading robot in the second warehouse area and the degree of change in collision risk in the second warehouse area, the degree of abnormality in the state of the second warehouse area is obtained. In an exemplary embodiment, such as... Figure 7 As shown, the process for obtaining the degree of state abnormality is as follows, including: Step 4-1-1: Obtain the actual running time of the loading robot in the second warehouse area, and obtain the time difference between the actual running time and the preset running time.

[0071] It should be understood that the actual runtime of the loading robot can be estimated from its operating speed. The preset runtime is a set value that can be obtained from the robot's rated operating speed. Therefore, normally, the actual runtime of the loading robot is greater than or equal to the preset runtime. Furthermore, based on the loading robot's movement towards the unloading point, the distance traveled by the loading robot in the second warehouse area can be obtained. Therefore, the average actual operating speed of the loading robot in the second warehouse area during the monitoring period is obtained, and then the ratio of the distance traveled by the loading robot in the second warehouse area to the average actual operating speed is calculated as the actual runtime of the loading robot in the second warehouse area.

[0072] The difference between the actual runtime and the preset runtime is obtained. Specifically, the difference is the absolute value of the difference between the actual runtime and the preset runtime. The larger the difference, the greater the possibility of collision risk for the robot during loading, and the more likely there is an abnormal state in the corresponding warehouse area.

[0073] Step 4-1-2: Based on the time difference and the degree of change in collision risk in the second required warehouse area, obtain the degree of abnormality in the state of the second required warehouse area.

[0074] Since the degree of collision risk change corresponding to each second warehouse area may have positive, 0, or negative values, the warehouse area corresponding to the positive value of the collision risk change is selected and defined as the second required warehouse area. Therefore, the second required warehouse area is the second warehouse area with a positive collision risk change.

[0075] Based on the time difference and the degree of change in collision risk in the second required warehouse area, the degree of state anomaly of the second required warehouse area is obtained. The degree of state anomaly is directly proportional to the time difference and inversely proportional to the degree of change in collision risk. In an exemplary embodiment, the formula for calculating the degree of state anomaly is as follows: ; in, The degree of abnormality in the state of the second required warehouse area is represented by norm, which is a linear normalization function. For the preset runtime, The actual runtime of the loading robot in the second warehouse area. The degree of change in collision risk for the second required warehouse area. To assess the varying degrees of collision risk in the second required warehouse area Perform negative correlation mapping. The negative correlation mapping can be performed in the following ways: Where n is the input data, It is an exponential function with the natural constant e as its base.

[0076] The above method yields the probability of robot collisions occurring in each subsequent warehouse area accessible from the first warehouse area, which is essentially the degree of anomaly in the state of each required second warehouse area. A lower degree of anomaly indicates a lower probability of robot collisions in the corresponding warehouse area.

[0077] Step 4-2: Determine the operating status of the loading robot based on the degree of abnormality in the warehouse area.

[0078] To reduce the collision risk of the loading robot entering the new warehouse area, the path planning of the loading robot is achieved by determining the minimum state anomaly level among the various second required warehouse areas adjacent to the first warehouse area and using the second required warehouse area corresponding to the minimum state anomaly level as the next target warehouse area of ​​the first warehouse area.

[0079] Therefore, in the enterprise safety production information data management method provided in this embodiment, the operating status data of warehouse robots within the warehouse area is obtained, specifically the actual operating speed and the number of robots, thereby obtaining the clustering characteristics of the warehouse area. Then, since the loading robots are carrying goods, collision risks need to be avoided within the warehouse. Therefore, the collision risk level of the warehouse area is obtained when the loading robots enter the warehouse area. Based on the positional transfer direction of the loading robots in the warehouse area and the collision risk level, the collision risk change level of the warehouse area is obtained, and areas with lower collision risk change levels are selected for operation. Furthermore, to improve the accuracy of path planning for the loading robots, the state anomaly level of the warehouse area is obtained by combining the running time of the loading robots in the warehouse area and the collision risk change level, and the area with the lowest state anomaly level is selected as the target area.

[0080] It should be understood that when managing warehouse robots, it is also necessary to constantly monitor the robot's movement direction during loading to prevent reverse movement, which could affect the efficiency of warehouse goods transportation. This is achieved through manual monitoring by back-end staff via surveillance cameras, with timely intervention in case of abnormalities such as reverse movement.

[0081] Furthermore, when determining the operating status of the loading robot in step 4, an extreme high-congestion condition may be encountered, where the collision risk change of all second warehouse areas adjacent to the first warehouse area is negative, or the calculated minimum state anomaly level is still higher than the preset safety threshold. This embodiment also provides a solution strategy based on load priority. When the system detects the above extreme condition, it no longer forcibly schedules the loading robot to the next target warehouse area, but instead triggers a "standby" or "cooperative avoidance" command. The specific execution logic is as follows: First, the system determines that the current first warehouse area is a temporary congestion node and sends a pause and wait command to the loading robot located in that area to keep it stationary and avoid it from forcibly entering the congestion area and causing an accident. Second, the system searches the adjacent areas surrounding the first warehouse area for robots in the unloading state. Because empty robots have low inertia and high flexibility, the system will send forced avoidance or reverse guidance commands to these surrounding unloading robots, requiring them to leave their current path or rotate to clear the passageway. Third, the system continuously monitors the degree of collision risk changes in adjacent areas of the first warehouse area. Once it detects that the indicator of a certain second warehouse area has risen back to a positive value, that is, the risk has decreased, the pause command is immediately lifted, and the loading robot is given priority to be guided into that area.

[0082] By prioritizing heavy-load and yielding to empty-load robots through a collaborative mechanism, the high mobility of empty robots can be effectively used to gain passage space for loading robots in high-density warehousing scenarios, thereby restoring overall logistics efficiency while ensuring safety.

[0083] This embodiment also provides an enterprise safety production information data management system, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-described enterprise safety production information data management method embodiment when the program instructions are executed.

[0084] In one exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the embodiment of the enterprise safety production information data management method.

[0085] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying 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.

[0086] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for information-based data management of enterprise safety production, characterized in that, include: Based on the actual operating speed and number of warehouse robots within the warehouse area, the clustering characteristics of the warehouse area are obtained; the warehouse robots include robots in the loading state. The collision risk level of the warehouse area is determined based on the clustering characteristics of the warehouse area and the proportion of robots in the loading state within the warehouse area; the collision risk level is directly proportional to the clustering characteristics and the proportion of robots. Based on the location transfer direction of the loading robots in the warehouse area, and combined with the collision risk level of the warehouse area, the degree of change in collision risk in the warehouse area is obtained; The operating status of the loading robot is determined based on the changing degree of collision risk in the warehouse area.

2. The enterprise safety production information data management method as described in claim 1, characterized in that, Based on the actual operating speed and number of warehouse robots within the warehouse area, the clustering characteristics of the warehouse area are obtained, including: Obtain the actual operating speed of the warehouse robots within the warehouse area at each moment, as well as the number of warehouse robots within the warehouse area at each moment; Based on the speed difference between the actual operating speed and the rated operating speed, and the number of robots, abnormal state parameters of the warehouse area at various times are obtained; the abnormal state parameters are proportional to the speed difference and the number of robots. The clustering characteristics of the warehouse area are obtained by analyzing the abnormal state parameters of the warehouse area at various times and the differences between the abnormal state parameters of two adjacent times.

3. The enterprise safety production information data management method as described in claim 2, characterized in that, Based on the abnormal state parameters of the warehouse area at various times, and the differences in abnormal state parameters between two adjacent times, the clustering characteristics of the warehouse area are obtained, including: Calculate the arithmetic mean of the abnormal state parameters at each sampling time within the preset monitoring period to obtain the first average value, and calculate the average difference of the abnormal state parameters between two adjacent times in the warehouse area to obtain the second average value; Based on the first average value and the second average value, the clustering characteristics of the warehouse area are obtained, wherein the clustering characteristics are proportional to the first average value and the second average value.

4. The enterprise safety production information data management method as described in claim 1, characterized in that, Based on the location transfer direction of the loading robots in the warehouse area, and combined with the collision risk level of the warehouse area, the degree of change in collision risk in the warehouse area is obtained, including: The latest percentage of loading robots in the second warehouse area is obtained based on the number of loading robots that leave the second warehouse area after entering the first warehouse area; the first warehouse area can be any warehouse area, and the second warehouse area is one of the warehouse areas adjacent to the first warehouse area. Based on the latest robot quantity ratio and the clustering characteristics of the second warehouse area, the latest collision risk level of the second warehouse area is obtained; The difference between the initial collision risk level and the latest collision risk level of the second warehouse area is obtained to obtain the collision risk change index of the second warehouse area.

5. The enterprise safety production information data management method as described in claim 4, characterized in that, The operational status of the loading robot is determined based on the changing level of collision risk in the warehouse area, including: The collision risk change level of each second warehouse area adjacent to the first warehouse area is obtained, and the second warehouse area corresponding to the largest collision risk change level is taken as the next target warehouse area of ​​the first warehouse area, so that the loading robot can move from the first warehouse area to the next target warehouse area.

6. The enterprise safety production information data management method as described in claim 1, characterized in that, The operational status of the loading robot is determined based on the changing level of collision risk in the warehouse area, including: The degree of abnormality in the warehouse area is determined based on the runtime of the loading robot in the warehouse area and the degree of change in the collision risk. The operating status of the loading robot is determined based on the degree of abnormality in the warehouse area.

7. The enterprise safety production information data management method as described in claim 6, characterized in that, Based on the runtime of the loading robot in the warehouse area and the degree of change in collision risk, the degree of abnormality in the warehouse area is determined, including: The actual running time of the loading robot in the second warehouse area is obtained, and the difference between the actual running time and the preset running time is obtained; the second warehouse area is one of the warehouse areas adjacent to the first warehouse area, and the first warehouse area is any warehouse area; Based on the time difference and the degree of change in collision risk of the second required warehouse area, the degree of abnormality of the state of the second required warehouse area is obtained; the second required warehouse area is the second warehouse area with a positive degree of change in collision risk, and the degree of abnormality of the state is directly proportional to the time difference and inversely proportional to the degree of change in collision risk.

8. The enterprise safety production information data management method as described in claim 7, characterized in that, The formula for calculating the degree of abnormality in the second required warehouse area is as follows: ； in, The degree of abnormality in the state of the second required warehouse area. For the preset runtime, The actual runtime of the loading robot in the second warehouse area. The degree of change in collision risk for the second required warehouse area. To assess the varying degrees of collision risk in the second required warehouse area Perform negative correlation mapping, where norm represents the linear normalization function.

9. The enterprise safety production information data management method as described in claim 7, characterized in that, The operational status of the loading robot is determined based on the degree of abnormality in the warehouse area, including: The state anomaly level of each second required warehouse area adjacent to the first warehouse area is obtained, and the second required warehouse area corresponding to the minimum state anomaly level is taken as the next target warehouse area of ​​the first warehouse area, so that the loading robot can move from the first warehouse area to the next target warehouse area.

10. An enterprise safety production information data management system, characterized in that, include: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is used to implement the enterprise safety production information data management method according to any one of claims 1-9 when the program instructions are executed.

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