Forklift management system

Abstract

The application relates to the field of forklift management systems, in particular to a forklift management system; sensors and video acquisition devices and other equipment are arranged as relevant data of a forklift and a driver in a working process, then the collected data are identified through a safety monitoring module, an alarm behavior is identified, the occurrence law of the alarm behavior is analyzed through a statistical analysis module, and the development trend of the monitoring data is counted, so that an enterprise manager can more clearly understand the state change of the driver and the forklift in the working process, the risk points of each driver are calculated through data calculation, the risk degree of each driver can be more objectively and intuitively reflected, and therefore the enterprise manager can more conveniently understand the risk degree of each driver; the problem that a driver has extremely high safety risks in the process of using a forklift and a supervisory unit is difficult to quickly identify a forklift driver with extremely high risks is solved.

Description

Technical Field

[0001] This invention relates to the field of forklift management systems, specifically a forklift management system. Background Technology

[0002] Forklifts are commonly used tools for handling goods in enterprises. In order to ensure the standardization of forklift and driver operation and the safety of enterprise production and life, some enterprises will set up certain supervision units. The supervision units usually supervise the forklift and driver through manual spot checks, which makes it difficult to achieve real-time supervision of each forklift. Therefore, currently, forklifts are mainly used by the driver to supervise themselves during operation, lacking a stable supervision unit to continuously supervise the driver and forklift.

[0003] When using forklifts, drivers often engage in dangerous behaviors such as fatigued driving, reckless driving, speeding, and entering restricted areas due to a lack of necessary supervision. However, the lack of continuous and stable monitoring methods makes these dangerous behaviors difficult to detect, resulting in extremely high safety risks for drivers using forklifts. Regulatory authorities also find it difficult to quickly identify forklift drivers with extremely high risks, posing safety hazards to the normal production and life of enterprises. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a forklift management system that solves the problem of extremely high safety risks for drivers using forklifts and the difficulty for regulatory authorities to quickly identify forklift drivers with extremely high risks.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A forklift management system, the system comprising:

[0007] The forklift monitoring module is used to acquire trajectory data, location data, and status data of the forklift through an electronic map, as well as real-time and historical videos of the forklift during its operation, in order to generate monitoring data and communicate with the forklift driver.

[0008] The safety monitoring module is used to identify alarm behaviors based on the monitoring data from the forklift monitoring module and the alarm rules set by the data management module, and to process and notify the alarm behaviors.

[0009] The statistical analysis module is used to statistically analyze alarm behaviors based on alarm behavior data from the safety monitoring module, and to statistically analyze the online status of the forklift based on historical data from the forklift monitoring module. It also displays the statistical and analytical data of alarm behaviors and forklift online status, and scores the driver based on alarm behaviors.

[0010] The data management module is used to set alarm rules for the safety monitoring module to identify alarm behaviors from monitoring data, and to register and save basic data of forklifts and corresponding drivers.

[0011] Preferably, the forklift monitoring module includes:

[0012] The map monitoring module is used to obtain the status data and location data of the forklift in real time based on the input forklift information and the electronic map and positioning system.

[0013] The trajectory monitoring module is used to obtain the trajectory data of the corresponding forklift on the electronic map based on the input forklift information, and to obtain the status data, location data and video data at any position on the trajectory based on the map monitoring module and the video monitoring module.

[0014] The video monitoring module is used to acquire real-time video of the forklift during its operation, or historical video of the forklift at any time, and to remotely control the forklift's camera to capture images and communicate with the driver.

[0015] Preferably, the security monitoring module includes:

[0016] The driving monitoring module is used to acquire the actual driver data of the forklift based on monitoring data, issue alarm information when the actual driver of the forklift is different from the driver bound to the forklift, monitor the driver's behavior data in real time during the driving process, analyze the behavior data according to the abnormal driving behavior set by the data management module, issue alarm information after identifying abnormal driving behavior, and save the corresponding image data.

[0017] The traffic restriction monitoring module is used to identify driving behaviors of forklifts that violate traffic restriction rules according to the traffic restriction rules set by the data management module, and issue alarm information, as well as issue warning information when the forklift approaches the boundary of the traffic restriction area;

[0018] The alarm management module is used to send prompts to the backend and the driver based on the alarm and warning information issued by the driving monitoring module and the traffic restriction monitoring module, record the alarm and warning information, and set several query rules and corresponding query terms for users to quickly query alarm and warning information.

[0019] Preferably, the statistical analysis module includes:

[0020] The big data analysis module is used to analyze the distribution, online status, offline status and online percentage of forklifts on the electronic map based on the monitoring data output by the forklift monitoring module, and to statistically analyze and predict the alarm status of each forklift according to the time period based on the alarm behavior output by the safety monitoring module.

[0021] The driving analysis module is used to calculate the forklift driver's arrival and departure times and locations, as well as the forklift's mileage during the corresponding time period, based on the monitoring data output by the forklift monitoring module.

[0022] The enterprise analysis module is used to count the total number and online rate of all forklifts in the enterprise within a certain time period, and to predict the total number and online rate.

[0023] The risk analysis module is used to analyze the driver's risk score within a certain time period based on the alarm behavior issued by the safety monitoring module and the preset alarm behavior and its corresponding importance.

[0024] Preferably, the data management module includes:

[0025] The basic information registration module is used to input, bind, and modify information about forklifts and drivers.

[0026] The fence management module is used to set up restricted areas within a certain range on an electronic map and set corresponding entry and exit rules;

[0027] An alarm rule setting module is used to set alarm rules for the driver while driving the forklift, and to issue an alarm signal when the behavior or state of the driver or forklift violates the alarm rules.

[0028] Preferably, the system also includes a safety education module. This module is used to set up a database containing several learning materials, and to count the alarm behaviors of each driver within a certain period based on the alarm behaviors output by the safety monitoring module. It also pushes corresponding learning materials to the drivers from the database based on the corresponding alarm behaviors of the drivers, and sets the learning period for the learning materials and monitors and alarms for the period.

[0029] Preferably, the big data analysis module includes:

[0030] The operation big data module is used to analyze the online and offline status of forklifts, forklift distribution and online rate based on the monitoring data output by the forklift monitoring module and the alarm behavior output by the safety monitoring module, and to predict the changing trend of alarm behavior after a period of time, and output a heat map based on the regional distribution of alarm behavior on the electronic map.

[0031] The safety big data module is used to classify the driver's alarm behavior based on the alarm behavior output by the safety monitoring module, predict the trend of the driver's alarm behavior after a certain period of time, and generate a heat map of the alarm behavior on the electronic map.

[0032] Preferably, the risk analysis module includes:

[0033] The alarm behavior classification module is used to classify alarm behaviors into several importance levels according to their importance.

[0034] The risk score calculation module is used to calculate the driver's risk score within a certain time period based on the importance level of different alarm behaviors from the behavior grading module and the driver's alarm behaviors issued by the safety monitoring module. The formula for calculating the risk score is as follows:

[0035]

[0036] In the above formula, R represents the driver's risk score over a certain time period. N represents the risk baseline score for the j-th alarm behavior with importance level i. i N represents the number of alarm behaviors belonging to the i-th importance level by the driver within a certain period, and N0 represents the number of basic behaviors.

[0037] Compared with the prior art, the present invention provides a forklift management system, which has the following beneficial effects:

[0038] 1. This invention uses sensors and video acquisition devices to collect relevant data on forklifts and drivers during operation. The safety monitoring module then identifies the collected data and identifies alarm behaviors based on relevant alarm rules defined by the data management module. Simultaneously, the statistical analysis module analyzes the occurrence patterns of alarm behaviors and statistically analyzes the development trends of monitoring data. This allows enterprise managers to better understand the status changes of drivers and forklifts during operation, enabling them to take timely measures to reduce losses.

[0039] 2. This invention uses a statistical analysis module to classify, statistically analyze, and interpret the collected monitoring data. It analyzes the data from the perspectives of big data, driving, enterprise, and risk, thereby guiding the enterprise's relevant behaviors based on the relevant data and providing a reference for the enterprise to formulate relevant measures.

[0040] 3. This invention calculates the risk score of each driver using data, thereby reflecting the risk level of each driver more objectively and intuitively. This allows enterprise managers to more easily understand the risk level of each driver and take more targeted measures for each driver. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0042] Figure 1 This is a structural block diagram of the forklift management system of the present invention;

[0043] Figure 2 This is a structural block diagram of the forklift monitoring module of the present invention;

[0044] Figure 3 This is a structural block diagram of the statistical analysis module of the present invention;

[0045] Figure 4 This is a structural block diagram of the data management module of the present invention.

[0046] The diagram shows: 1. Forklift monitoring module; 11. Map monitoring module; 12. Trajectory monitoring module; 13. Video monitoring module; 2. Security monitoring module; 21. Driving monitoring module; 22. Traffic restriction monitoring module; 23. Alarm management module; 3. Statistical analysis module; 31. Big data analysis module; 311. Operational big data module; 312. Safety big data module; 32. Driving analysis module; 33. Enterprise analysis module; 34. Risk analysis module; 341. Behavior classification module; 342. Risk score calculation module; 4. Data management module; 41. Basic information registration module; 42. Fence management module; 43. Alarm rule setting module; 5. Safety education module. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding and implementation of how the present application uses technical means to solve technical problems and achieve technical effects.

[0048] Those skilled in the art will understand that all or part of the steps in the methods of the following embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] To address the high safety risks posed by the lack of stable monitoring methods for forklift operators, and the difficulty for regulatory authorities to quickly identify high-risk forklift drivers, this embodiment provides a forklift management system. The system includes:

[0050] The forklift monitoring module 1 is used to acquire forklift trajectory, location, and status data via electronic maps, as well as real-time and historical video of the forklift's operation to generate monitoring data and communicate with the forklift driver. A GPS positioning device is installed on the forklift to acquire real-time trajectory and location data via electronic maps. Sensors are also installed on the forklift to obtain status data such as tire pressure and fuel level. Real-time video of the forklift during use is also acquired via video monitoring devices, generating monitoring data that serves as the basis for subsequent alarms. A safety monitoring module 2 is used to identify alarm behaviors based on the monitoring data from the forklift monitoring module 1 and the alarm rules set by the data management module 4, and to process and notify of alarm behaviors. A statistical analysis module 3 is used to statistically analyze alarm behaviors based on the alarm behavior data from the safety monitoring module 2, and to analyze the online status of the forklift based on historical data from the forklift monitoring module 1. The system performs statistical analysis and displays statistical and analytical data on alarm behaviors and forklift online status. It also scores drivers based on alarm behaviors to digitally display the driver's risk level, facilitating management's handling of the corresponding drivers. A data management module 4 is used to set alarm rules for the safety monitoring module 2 to identify alarm behaviors from monitoring data and to register and save basic data on the forklift and corresponding driver. This establishes a binding relationship between the driver and the forklift, designating the responsible person for the forklift and allowing the safety monitoring module 2 to identify unauthorized forklift operation, thereby improving forklift management. Through the cooperation of these modules, the system comprehensively monitors the forklift's status and the personnel's status during use, retaining relevant data for subsequent verification. This achieves continuous and stable supervision of the forklift and driver during operation, easily identifying high-risk behaviors and imposing constraints on the forklift and driver, further ensuring the safety of the company's employees' production and lives.

[0051] The forklift monitoring module 1 is used to acquire data in three aspects: the forklift's driving trajectory, relevant video during the driving process, and relevant status. Specifically, this module includes: a map monitoring module 11, which acquires the forklift's status and location data in real time based on the input forklift information and using an electronic map and positioning system. There are various ways to input forklift information, such as inputting the license plate number of a single forklift, inputting a corresponding location to obtain forklifts passing through that location, or inputting a forklift group to obtain the status and location data of all forklifts within that group; and a trajectory monitoring module 12, which acquires the corresponding forklift's trajectory data on the electronic map based on the input forklift information and uses the map monitoring module 11 and video... The monitoring module 13 acquires status data, location data, and video data at any location on the trajectory. Based on the forklift's trajectory, any location on the trajectory can be clicked on the electronic map to obtain data such as the forklift's fuel level and tire pressure, time, and video images when the forklift arrives at that location. The video monitoring module 13 is used to acquire real-time video of the forklift during its driving process, or historical video of the forklift at any time, and to remotely control the forklift's shooting device to capture images and communicate with the driver. Real-time video and historical video are one of the judgment objects of the safety monitoring module 2 to determine whether there is an alarm behavior during the forklift's driving process. Other judgment objects are trajectory data, location data, and status data, etc.

[0052] The safety monitoring module 2 identifies and processes alarm behaviors in the monitoring data by comparing the monitoring data with the alarm rules set by the data management module 4, thereby assisting the driver in correcting non-compliant behaviors. Therefore, the safety monitoring module 2 includes: a driving monitoring module 21 for acquiring the actual driver data of the forklift based on the monitoring data and issuing alarm information when the actual driver and the bound driver of the forklift are different; and a module 21 for real-time monitoring of the driver's behavior data during driving, analyzing the behavior data according to the abnormal driving behaviors set by the data management module 4, issuing alarm information after identifying abnormal driving behaviors, and ensuring... The system stores the corresponding image data; the traffic restriction monitoring module 22 is used to identify driving behaviors of forklifts that violate traffic restriction rules according to the traffic restriction rules set by the data management module 4, and issue alarm information, as well as issue warning information when the forklift approaches the boundary of the traffic restriction area; the alarm management module 23 is used to send prompt information to the backend and the driver according to the alarm and warning information issued by the driving monitoring module 21 and the traffic restriction monitoring module 22, and records the alarm and warning information, and sets several query rules and corresponding query terms for users to quickly query alarm and warning information, such as setting query terms such as forklift weekly online rate.

[0053] To further statistically analyze monitoring data and alarm behaviors, enabling enterprises to identify patterns or gain a better understanding of various data points, the statistical analysis module 3 includes: a big data analysis module 31 for analyzing the distribution, online status, offline status, and online percentage of forklifts on the electronic map based on the monitoring data output by the forklift monitoring module 1; a driving analysis module 32 for statistically analyzing and predicting the alarm status of each forklift according to time periods based on the alarm behaviors output by the safety monitoring module 2; an enterprise analysis module 33 for statistically analyzing the online and offline times and locations of forklift drivers and the mileage of forklifts in the corresponding time periods based on the monitoring data output by the forklift monitoring module 1; an enterprise analysis module 33 for statistically analyzing the total number of online forklifts and the online rate of all forklifts within the enterprise within a certain time period and predicting the total number of online forklifts and the online rate; and a risk analysis module 34 for analyzing the risk score of drivers within a certain time period based on the alarm behaviors issued by the safety monitoring module 2, according to preset alarm behaviors and their corresponding importance.

[0054] To establish rules for various alarm behaviors so that the safety monitoring module 2 can identify the corresponding alarm behaviors, and to link alarm behaviors with forklifts and personnel, it is necessary to bind personnel and forklifts. The data management module 4 includes: a basic information registration module 41 for inputting, binding, and modifying information about forklifts and drivers; a fence management module 42 for setting restricted areas within a certain range on the electronic map and setting corresponding entry and exit rules; and an alarm rule setting module 43 for setting alarm rules for drivers while driving forklifts and issuing alarm signals when the behavior or status of the driver or forklift violates the alarm rules.

[0055] To facilitate training forklift drivers and enable them to understand the reasons for their mistakes based on alarm behavior, this system also includes a safety education module 5. The safety education module 5 is used to set up a database containing several learning materials, and based on the alarm behavior output by the safety monitoring module 2, to count the alarm behavior of each driver within a certain period, and to push corresponding learning materials to the driver from the database according to the driver's corresponding alarm behavior, as well as to set the learning period of the learning materials and monitor and alarm the period.

[0056] To analyze data trends based on current and historical data, enabling enterprises to take timely measures to reduce losses or improve efficiency, the big data analysis module 31 includes: an operational big data module 311 for analyzing the online / offline status, distribution, and availability of forklifts based on monitoring data output from the forklift monitoring module 1 and alarm behaviors output from the safety monitoring module 2, predicting the trend of alarm behaviors over a period of time, and outputting a heat map based on the regional distribution of alarm behaviors on an electronic map; and a safety big data module 312 for classifying driver alarm behaviors based on alarm behaviors output from the safety monitoring module 2, predicting the trend of driver alarm behaviors over a certain period of time, and outputting a heat map of alarm behaviors on an electronic map.

[0057] To further visualize the risk level of each driver and facilitate more objective measures by the company, the risk analysis module 34 includes: a behavior grading module 341 for classifying alarm behaviors into several importance levels based on their severity; and a risk score calculation module 342 for calculating the driver's risk score over a certain time period based on the importance levels of different alarm behaviors identified by the behavior grading module 341 and the alarm behaviors issued by the safety monitoring module 2. The formula for calculating the risk score is as follows:

[0058]

[0059] In the above formula, R represents the driver's risk score over a certain time period. N represents the risk baseline score for the j-th alarm behavior with importance level i. i N represents the number of alarm behaviors belonging to the i-th importance level by the driver within a certain period, and N0 represents the number of basic behaviors.

[0060] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A forklift management system, characterized in that... The system includes: Forklift monitoring module (1), the forklift monitoring module (1) is used to obtain the trajectory data, location data and status data of the forklift driving through the electronic map, as well as the real-time video and historical video during the forklift driving process, in order to generate monitoring data and communicate with the forklift driver. Safety monitoring module (2), the safety monitoring module (2) is used to identify alarm behavior based on the monitoring data of forklift monitoring module (1) and alarm rules set by data management module (4), and to process and notify the alarm behavior; The statistical analysis module (3) is used to perform statistical analysis on alarm behavior based on alarm behavior data from the safety monitoring module (2), and to perform statistical analysis on forklift online status based on historical data from the forklift monitoring module (1), and to display statistical analysis data on alarm behavior and forklift online status, and to score the driver based on alarm behavior. The data management module (4) is used to set alarm rules for the safety monitoring module (2) to identify alarm behaviors from the monitoring data, and to register and save the basic data of the forklift and the corresponding driver.

2. The forklift management system according to claim 1, characterized in that: The forklift monitoring module (1) includes: Map monitoring module (11), the map monitoring module (11) is used to obtain the status data and location data of the forklift in real time based on the input forklift information and the electronic map and positioning system; The trajectory monitoring module (12) is used to obtain the trajectory data of the corresponding forklift on the electronic map according to the input forklift information, and to obtain the status data, location data and video data at any position on the trajectory according to the map monitoring module (11) and the video monitoring module (13); The video monitoring module (13) is used to acquire real-time video of the forklift during its operation, or historical video of the forklift at any time, and to remotely control the forklift shooting device to capture images and communicate with the driver.

3. The forklift management system according to claim 1, characterized in that: The security monitoring module (2) includes: The driving monitoring module (21) is used to obtain the actual driver data of the forklift based on the monitoring data, and to issue an alarm message when the actual driver of the forklift is different from the driver bound to the forklift, and to monitor the driver's behavior data in real time during the driving process, and to analyze the behavior data according to the abnormal driving behavior set by the data management module (4), and to issue an alarm message after identifying the abnormal driving behavior, and to save the corresponding image data. Traffic restriction monitoring module (22), the traffic restriction monitoring module (22) is used to identify the driving behavior of forklifts that violate the traffic restriction rules according to the traffic restriction rules set by the data management module (4), and issue alarm information, as well as issue warning information when the forklifts approach the boundary of the traffic restriction area; The alarm management module (23) is used to send prompt information to the background and the driver based on the alarm information and warning information issued by the driving monitoring module (21) and the traffic restriction monitoring module (22), and to record the alarm information and warning information. It also sets several query rules and corresponding query terms for users to quickly query alarm information and warning information.

4. The forklift management system according to claim 1, characterized in that: The statistical analysis module (3) includes: The big data analysis module (31) is used to analyze the distribution, online status, offline status and online percentage of forklifts on the electronic map based on the monitoring data output by the forklift monitoring module (1), and to statistically analyze and predict the alarm status of each forklift according to the time period based on the alarm behavior output by the safety monitoring module (2). The driving analysis module (32) is used to calculate the forklift driver's online and offline time and location, as well as the forklift's driving mileage in the corresponding time period, based on the monitoring data output by the forklift monitoring module (1). Enterprise analysis module (33) is used to count the total number of forklifts online and the online rate of all forklifts in the enterprise within a certain time period, and to predict the total number of online forklifts and the online rate. Risk analysis module (34) is used to analyze the risk score of the driver within a certain time period based on the alarm behavior issued by the safety monitoring module (2) and the preset alarm behavior and corresponding importance.

5. The forklift management system according to claim 1, characterized in that: The data management module (4) includes: The basic information registration module (41) is used to input, bind, and modify the information of the forklift and the driver; The fence management module (42) is used to set up a restricted area within a certain range on the electronic map and set up corresponding entry and exit rules; The alarm rule setting module (43) is used to set alarm rules for the driver during the operation of the forklift and to issue an alarm signal when the behavior or state of the driver or forklift violates the alarm rules.

6. The forklift management system according to claim 1, characterized in that: It also includes a safety education module (5), which is used to set up a database containing several learning materials, and to count the alarm behavior of each driver within a certain period based on the alarm behavior output by the safety monitoring module (2), and to push the corresponding learning materials from the database to the driver based on the corresponding alarm behavior of the driver, as well as to set the learning period of the learning materials and to monitor and alarm the period.

7. The forklift management system according to claim 4, characterized in that: The big data analysis module (31) includes: Operational big data module (311) is used to analyze the online and offline status of forklifts, forklift distribution and online rate based on the monitoring data output by forklift monitoring module (1) and alarm behavior output by safety monitoring module (2), and predict the trend of alarm behavior after a period of time, and output heat map based on the regional distribution of alarm behavior on electronic map. The safety big data module (312) is used to classify the alarm behavior of the driver according to the alarm behavior output by the safety monitoring module (2), predict the change trend of the alarm behavior of the driver after a certain period of time, and the heat map of the alarm behavior on the electronic map.

8. The forklift management system according to claim 4, characterized in that: The risk analysis module (34) includes: The alarm behavior classification module (341) is used to classify alarm behaviors into several importance levels according to their importance. The risk score calculation module (342) is used to calculate the driver's risk score within a certain time period based on the importance level of different alarm behaviors of the behavior classification module (341) and the alarm behaviors of the driver issued by the safety monitoring module (2). The formula for calculating the risk score is as follows: In the above formula, R represents the driver's risk score over a certain time period. N represents the risk baseline score for the j-th alarm behavior with importance level i. i N represents the number of alarm behaviors belonging to the i-th importance level by the driver within a certain period, and N0 represents the number of basic behaviors.

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