Factory traffic management method, device, equipment, storage medium and product

Abstract

The present application discloses a factory traffic management method, device, equipment, storage medium and product, which relates to the field of computer technology. The factory traffic management method, device, equipment, storage medium and product include: when a factory traffic management signal is received, multi-dimensional information related to traffic management in the factory is obtained; based on a preset information fusion model, the multi-dimensional information is subjected to multi-modal fusion processing to obtain factory traffic status information; based on a preset decision model, the factory traffic status information is analyzed and processed to obtain a factory traffic management decision plan. When the factory traffic management signal is received, the present application processes and analyzes the collected multi-dimensional information related to traffic management through an information fusion model and a decision model, thereby realizing the automated analysis and disposal of the factory traffic management scene. Therefore, based on the automated disposal of the factory traffic management scene, the efficiency of factory traffic management can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to factory traffic management methods, devices, equipment, storage media and products. Background Art

[0002] With the continuous expansion of factory scale and the growth of traffic safety management needs, traditional traffic safety management methods can no longer meet the needs of modern enterprises for traffic safety management within factories.

[0003] Existing factory traffic management systems usually use cameras and manual recording of vehicles and personnel entering and exiting, but lack monitoring of restricted areas and safe traffic regulations within the factory, and are unable to monitor vehicle dynamics and personnel behavior in a timely manner, resulting in low efficiency of factory traffic management.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a factory traffic management method, device, equipment, storage medium and product, aiming to solve the technical problem of low efficiency of factory traffic management.

[0006] To achieve the above objectives, the present application proposes a factory traffic management method, wherein the method comprises:

[0007] When receiving the factory traffic management signal, obtain multi-dimensional information related to the traffic management in the factory;

[0008] Based on a preset information fusion model, multi-modal fusion processing is performed on the multi-dimensional information to obtain factory traffic status information;

[0009] Based on a preset decision model, the factory traffic status information is analyzed and processed to obtain a factory traffic management decision plan.

[0010] In one embodiment, the multi-dimensional information includes factory restricted area information, vehicle speed information, vehicle identification information, and pedestrian identification information.

[0011] In one embodiment, before the step of obtaining multi-dimensional information related to traffic management in the factory when the factory traffic management signal is received, the method includes:

[0012] Get a sample of plant traffic information;

[0013] Based on a preset information fusion model, multimodal fusion processing is performed on the factory traffic information sample to obtain a factory traffic status information sample;

[0014] Inputting the factory traffic status information sample into a preset model to be trained to obtain a predicted traffic management decision plan, wherein the model to be trained is a reinforcement learning model;

[0015] After executing the predicted traffic management decision-making scheme, obtaining an updated factory traffic status information sample, and determining a state change score value based on the factory traffic status information sample and the updated factory traffic status information sample;

[0016] Determine whether the state change score value meets the preset loss convergence condition. If the state change score value does not meet the preset loss convergence condition, adjust the model to be trained, return to the step of inputting the factory traffic state information sample into the preset model to be trained, and obtain a predicted traffic management decision plan, until the state change score value meets the preset loss convergence condition, and obtains a decision model that meets the accuracy condition.

[0017] In one embodiment, before the step of performing multimodal fusion processing on the factory traffic information sample based on a preset information fusion model to obtain the factory traffic status information sample, the method includes:

[0018] The factory traffic information samples in the factory traffic information samples are subjected to noise removal, missing value filling and data format normalization processing to obtain the preprocessed factory traffic information samples.

[0019] In one embodiment, when the factory traffic management signal is received, before the step of obtaining multi-dimensional information related to the traffic management in the factory, the step further includes:

[0020] Get a sample of plant traffic information;

[0021] Based on the factory traffic information sample, the preset second model to be trained and the preset first model to be trained are jointly trained to obtain an information fusion model and a decision model that meet the accuracy conditions, wherein the second model to be trained is the initial training model of the information fusion model, and the first model to be trained is the initial training model of the decision model.

[0022] In one embodiment, the step of obtaining a factory traffic management decision solution includes:

[0023] If the factory traffic management decision plan is to expel vehicles and personnel that enter the restricted area, a reminder message is sent to a preset security management device, and a preset warning message is sent through a broadcasting device to obtain and record the processing result;

[0024] If the factory traffic management decision plan is to expel unrecorded vehicles and personnel, a reminder message is sent to the security management device to identify and record the movement tracks of unrecorded vehicles and personnel, and obtain and record the processing results;

[0025] If the factory traffic management decision plan is to record and remind speeding vehicles, the license plate information and model information of the speeding vehicle are identified, the license plate information and the model information are sent to the safety management device, and the processing result is obtained and recorded.

[0026] In addition, to achieve the above purpose, the present application also proposes a factory traffic management device, the factory traffic management device comprising:

[0027] An acquisition module, used to acquire multi-dimensional information related to traffic management in the factory when receiving a factory traffic management signal;

[0028] A fusion module, used for performing multimodal fusion processing on the multi-dimensional information based on a preset information fusion model to obtain factory traffic status information;

[0029] The execution module is used to analyze and process the factory traffic status information based on a preset decision model to obtain a factory traffic management decision plan.

[0030] In addition, to achieve the above-mentioned objectives, the present application also proposes a factory traffic management device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the factory traffic management method as described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the factory traffic management method described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the factory traffic management method described above are implemented.

[0033] One or more technical solutions proposed in this application have at least the following technical effects:

[0034] Compared with the related art, which uses cameras and manual recording of vehicles and personnel entering and exiting, but lacks monitoring of prohibited areas and safety traffic regulations in the factory, and cannot timely monitor vehicle dynamics and personnel behavior, resulting in low efficiency of factory traffic management, this application obtains multi-dimensional information related to traffic management in the factory when receiving a factory traffic management signal; based on a preset information fusion model, the multi-dimensional information is subjected to multi-modal fusion processing to obtain factory traffic status information; based on a preset decision model, the factory traffic status information is analyzed and processed to obtain a factory traffic management decision plan. It can be understood that this application uses an information fusion model and a decision model to process and analyze the collected multi-dimensional information related to traffic management. When a factory traffic management signal is received, the collected multi-dimensional information related to traffic management is processed and analyzed through the information fusion model and the decision model, so as to realize the automatic analysis and disposal of the factory traffic management scene. Therefore, based on the automatic analysis and disposal of the factory traffic management scene, the efficiency of factory traffic management can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 A flow chart of the first embodiment of the factory traffic management method of the present application;

[0038] Figure 2 This is a schematic diagram of the module structure of the factory traffic management device according to an embodiment of the present application;

[0039] Figure 3 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the factory traffic management method in the embodiment of the present application.

[0040] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0042] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0043] The main solutions of the embodiments of this application are:

[0044] When receiving the factory traffic management signal, obtain multi-dimensional information related to the traffic management in the factory;

[0045] Based on a preset information fusion model, multi-modal fusion processing is performed on the multi-dimensional information to obtain factory traffic status information;

[0046] Based on a preset decision model, the factory traffic status information is analyzed and processed to obtain a factory traffic management decision plan.

[0047] In this embodiment, the present application uses a factory traffic management device as the execution subject. For ease of description, it is specifically described below as "device".

[0048] Because existing technologies usually use cameras and manual recording of vehicles and personnel entering and exiting, but lack monitoring of restricted areas and safe traffic regulations within the factory, it is impossible to monitor vehicle dynamics and personnel behavior in a timely manner, resulting in low efficiency of factory traffic management.

[0049] The present application provides a solution, which uses an information fusion model and a decision-making model to process and analyze the collected multi-dimensional information related to traffic management. When a factory traffic management signal is received, the collected multi-dimensional information related to traffic management is processed and analyzed through the information fusion model and the decision-making model, so as to realize the automated analysis and disposal of the factory traffic management scenario. Therefore, based on the automated analysis and disposal of the factory traffic management scenario, the efficiency of factory traffic management can be improved.

[0050] Based on this, the present application embodiment provides a factory traffic management method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the factory traffic management method of the present application.

[0051] In this embodiment, the factory traffic management method includes steps S10 to S30:

[0052] Step S10, when a factory traffic management signal is received, obtaining multi-dimensional information related to traffic management in the factory;

[0053] It should be noted that the factory traffic management signal refers to the start signal issued by the management device to start the engineering traffic management. The multi-dimensional information refers to the information collected from different sources and angles, which together constitute a comprehensive description of the factory traffic situation.

[0054] For example, after the factory traffic safety management device is started, multiple sensors and data collection modules will be activated to obtain multi-dimensional information related to factory traffic management. This information includes:

[0055] Vehicle position and speed: Vehicle position and speed collected by cameras and sensors.

[0056] Traffic flow: Collected through cameras and sensors installed at key intersections to monitor vehicle flow and density.

[0057] Road conditions: collected through road sensors, including road slipperiness, temperature, and whether there are obstacles.

[0058] Pedestrian identification information: The identity of a person obtained by comparing camera recognition with a pre-stored identity database.

[0059] Weather conditions: collected through weather stations, including wind speed, temperature, precipitation, etc.

[0060] Security incidents: collected through emergency alarm systems and surveillance cameras to identify any potential security risks.

[0061] Step S20, based on a preset information fusion model, performing multimodal fusion processing on the multi-dimensional information to obtain factory traffic status information;

[0062] It should be noted that the information fusion model refers to a preset algorithm or data processing framework for integrating and processing multi-dimensional information from different sources to produce a comprehensive and more accurate description of the factory traffic status. The multimodal fusion processing refers to the process of using the information fusion model to conduct a comprehensive analysis of different types of data to obtain a comprehensive factory traffic status information. The factory traffic status information refers to the comprehensive information obtained through multimodal fusion processing, which reflects the real-time status of internal factory traffic, including but not limited to vehicle distribution, traffic congestion, potential safety risks, etc.

[0063] Step S30: analyzing and processing the factory traffic status information based on a preset decision model to obtain a factory traffic management decision plan.

[0064] It should be noted that the decision model refers to a preset algorithm or logical framework for analyzing factory traffic status information and making corresponding traffic management decisions based on this information. The factory traffic management decision plan refers to a series of action plans or instructions formulated based on the analysis and processing results, which are used to optimize the traffic flow within the factory and improve safety and efficiency, and may include traffic route adjustments, speed restrictions, implementation of safety measures, etc.

[0065] For example, after the factory traffic safety management device is started, it continuously monitors the signal from the factory traffic management center through the wireless communication module or the wired network interface. When the device receives the traffic management signal, it will activate multiple sensors and data collection modules to obtain multi-dimensional information related to factory traffic management. This information includes:

[0066] Vehicle position and speed data are collected through cameras and speed sensors.

[0067] Traffic flow data is collected through cameras and sensors installed at key intersections.

[0068] Road condition data is collected through road sensors, including road slipperiness, temperature, and whether there are obstacles.

[0069] Weather condition data, collected through weather stations, including wind speed, temperature, precipitation, etc.

[0070] Security incident data, collected through emergency alarm systems and surveillance cameras to identify any potential security risks.

[0071] The collected raw data needs to be preprocessed to ensure the quality and consistency of the data. The preprocessing steps include data cleaning (removing noise and outliers), data standardization (ensuring that data from different sources are on the same scale), data synchronization (ensuring consistent timestamps), etc.

[0072] The preprocessed data is fed into a preset information fusion model for multimodal fusion processing. The model integrates different types of data (such as video streams, sensor readings, etc.) to generate a comprehensive view of the factory traffic status. Key features such as vehicle trajectories, traffic flow patterns, etc. are extracted from various data sources. The extracted features are combined through different fusion strategies (such as early fusion, mid-term fusion, late fusion) to estimate the current traffic status, such as congestion level, accident risk, etc. using the fused features.

[0073] After multimodal fusion processing, the device will generate factory traffic status information. This information includes but is not limited to: real-time traffic flow map, showing vehicle distribution and traffic density; traffic congestion warning, predicting possible congestion areas and times; accident and safety hazard reports, identifying high-risk areas and events; traffic optimization suggestions, providing strategies and measures to improve traffic flow.

[0074] The generated factory traffic status information is fed into a preset decision model for analysis and processing. The decision model may be a rule-based system, a machine learning model, or other advanced data analysis technology. The model will analyze the traffic status information and make corresponding traffic management decisions, such as: adjusting the timing of traffic lights to optimize traffic flow; issuing traffic guidance to guide vehicles to avoid congested areas; implementing speed limits to reduce accident risks; deploying emergency response measures to deal with safety incidents.

[0075] The decisions made are executed through the control system, such as adjusting traffic lights, issuing traffic guidance, implementing speed limits, etc. At the same time, the device monitors the effectiveness of the execution of these decisions and collects feedback to optimize future decision making.

[0076] This embodiment provides a factory traffic management method, which uses an information fusion model and a decision model to process and analyze the collected multi-dimensional information related to traffic management. When a factory traffic management signal is received, the collected multi-dimensional information related to traffic management is processed and analyzed through the information fusion model and the decision model, so as to realize the automatic analysis and disposal of the factory traffic management scene. Therefore, based on the automatic analysis and disposal of the factory traffic management scene, the efficiency of factory traffic management can be improved.

[0077] In a feasible implementation manner, the multi-dimensional information includes factory restricted area information, vehicle speed information, vehicle identification information, and pedestrian identification information.

[0078] It is understood that the factory restricted area information refers to specific areas demarcated within the factory, which may restrict vehicle traffic due to safety, production or other management needs. The factory restricted area information includes but is not limited to the geographical location, restricted time, restricted rules and possible detour routes of these areas. This information is used to guide vehicles to drive safely and comply with traffic rules in the factory. The vehicle speed information refers to the speed data of the vehicle when it is driving in the factory, which can be obtained in real time through on-board speed sensors, radar systems or other speed monitoring equipment. Vehicle speed information is essential for monitoring and ensuring that vehicles comply with speed limits, preventing speeding, and evaluating traffic flow and safety conditions. The vehicle identification information: refers to information used to uniquely identify each vehicle driving in the factory, which may include the vehicle's license plate number, vehicle identification number (VIN), vehicle type, department or company, etc. Vehicle identification information is essential for traffic management and safety monitoring. It helps track vehicle locations, record driving routes, manage vehicle entry and exit, and identify the responsible party in the event of an accident or violation. The pedestrian identification information is information used to uniquely identify pedestrians walking in the factory, which may include the employee's work number, name, department or other personal identifiers. Pedestrian identification information is particularly important for safety management within the factory. It helps monitor pedestrian flow, ensure pedestrian safety, manage employee access to restricted areas, and quickly locate and evacuate personnel in an emergency.

[0079] In a feasible implementation manner, before the step of obtaining multi-dimensional information related to traffic management in the factory when the factory traffic management signal is received, the method includes:

[0080] Get a sample of plant traffic information;

[0081] Based on a preset information fusion model, multimodal fusion processing is performed on the factory traffic information sample to obtain a factory traffic status information sample;

[0082] Inputting the factory traffic status information sample into a preset model to be trained to obtain a predicted traffic management decision plan, wherein the model to be trained is a reinforcement learning model;

[0083] After executing the predicted traffic management decision-making scheme, obtaining an updated factory traffic status information sample, and determining a state change score value based on the factory traffic status information sample and the updated factory traffic status information sample;

[0084] Determine whether the state change score value meets the preset loss convergence condition. If the state change score value does not meet the preset loss convergence condition, adjust the model to be trained, return to the step of inputting the factory traffic state information sample into the preset model to be trained, and obtain a predicted traffic management decision plan, until the state change score value meets the preset loss convergence condition, and obtains a decision model that meets the accuracy condition.

[0085] It is understood that the factory traffic information sample refers to a set of data collected from the factory traffic management system, which represents the status of the factory internal traffic at a specific moment or time period. The factory traffic information sample includes but is not limited to vehicle location, speed, traffic flow, pedestrian flow, weather conditions, road conditions, etc., which are used to analyze and predict traffic conditions. The information fusion model refers to a preset algorithm or data processing framework for integrating and processing factory traffic information samples from different sources and types to produce a comprehensive and more accurate factory traffic status information sample. This model involves steps such as data preprocessing, feature extraction, and feature fusion. The multimodal fusion processing refers to the process of using the information fusion model to comprehensively analyze different types of data (such as video, sensor data, etc.) to obtain a comprehensive factory traffic status information sample. The factory traffic status information sample refers to the comprehensive information obtained through multimodal fusion processing, which reflects the real-time status of the factory internal traffic, including but not limited to vehicle distribution, traffic congestion, potential safety risks, etc. The model to be trained refers to a model that has not yet completed training, and its parameters need to be adjusted through a learning process to improve the accuracy of its prediction of traffic management decision-making solutions. In this example, the model to be trained is a reinforcement learning model. The reinforcement learning model is a machine learning model that learns how to make decisions by interacting with the environment. In the context of factory traffic management, the reinforcement learning model learns how to optimize traffic management by observing traffic state information samples and the results of executing decision plans. The predicted traffic management decision plan refers to a series of action plans or instructions generated by the model to be trained (in this case, the reinforcement learning model) to optimize the traffic flow within the factory and improve safety and efficiency. The updated factory traffic state information sample refers to the factory traffic information sample collected again after the predicted traffic management decision plan is executed, reflecting the new state of factory traffic after the decision plan is executed. The state change score value refers to a quantitative indicator used to evaluate the degree of change in the factory traffic state after the predicted traffic management decision plan is executed. This score value may be calculated based on parameters such as traffic flow, congestion level, accident rate, etc. The loss convergence condition refers to a preset performance standard used to judge whether the prediction performance of the model is good enough, that is, whether the state change score value has been reduced to an acceptable range, indicating that the model's prediction decision plan can effectively improve traffic conditions.

[0086] Exemplarily, the device first collects traffic information samples from multiple sensors and data sources in the factory. These samples include but are not limited to vehicle location, speed, traffic flow, pedestrian flow, weather conditions, road conditions, etc. These data are transmitted to the central processing unit in real time through a wireless communication module or a wired network interface. The collected multi-dimensional information samples are sent to a preset information fusion model for processing. The model may include steps such as data preprocessing, feature extraction, and feature fusion to integrate different types of data to generate a comprehensive factory traffic status information sample. The factory traffic status information sample is input into a preset model to be trained, which is a reinforcement learning model. The reinforcement learning model learns how to make decisions through interaction with the environment to optimize traffic management. The reinforcement learning model outputs a predicted traffic management decision plan based on the input factory traffic status information sample. These plans may include adjusting the timing of traffic lights, issuing traffic guidance, implementing speed limits, etc. After executing the predicted traffic management decision plan, the device re-collects factory traffic information samples to evaluate the effectiveness of the decision plan. These updated samples reflect the new state of factory traffic after the decision plan is executed.

[0087] The device calculates the state change score value based on the original factory traffic state information sample and the updated factory traffic state information sample. This score value may be calculated based on parameters such as traffic flow, congestion level, accident rate, etc. to quantitatively evaluate the effect of the decision-making scheme. The device determines whether the state change score value meets the preset loss convergence condition. If the state change score value does not meet the loss convergence condition, it indicates that the current decision model has not yet reached the expected accuracy and needs further training and optimization. If the state change score value does not meet the loss convergence condition, the device will return to inputting the factory traffic state information sample into the preset model to be trained, and continue to input the factory traffic state information sample into the model to be trained to obtain a new predicted traffic management decision plan. This process will continue to iterate until the state change score value meets the preset loss convergence condition. Once the state change score value meets the loss convergence condition, the device will stop iterating, and the decision model obtained at this time is considered to meet the accuracy condition and can be used for actual factory traffic management.

[0088] In a feasible implementation manner, before the step of performing multimodal fusion processing on the factory traffic information sample based on a preset information fusion model to obtain the factory traffic status information sample, the method includes:

[0089] The factory traffic information samples in the factory traffic information samples are subjected to noise removal, missing value filling and data format normalization processing to obtain the preprocessed factory traffic information samples.

[0090] It should be noted that the factory traffic information sample refers to a set of data collected from the factory traffic management system, which represents the status of the internal traffic of the factory at a specific time or time period. The factory traffic information sample includes but is not limited to vehicle location, speed, traffic flow, pedestrian flow, weather conditions, road conditions, etc., which are used to analyze and predict traffic conditions. The preprocessing refers to a series of operations performed on the original data before data analysis and processing, aiming to improve data quality, reduce noise, fill missing values, normalize data formats, etc., so that the data can be more effectively used for subsequent analysis and model training. The preprocessing step is crucial to ensure the consistency and reliability of data. The preprocessed factory traffic information sample refers to the factory traffic information sample processed by the preprocessing step. These samples have removed noise, filled missing values, normalized data formats, and have been normalized or standardized, making the data more suitable for training and analysis of machine learning models. The preprocessed factory traffic information sample can provide a more accurate description of traffic conditions, which helps to improve the accuracy and efficiency of traffic management decisions.

[0091] In a feasible implementation manner, when the factory traffic management signal is received, the step of obtaining multi-dimensional information related to the traffic management in the factory further includes:

[0092] Get a sample of plant traffic information;

[0093] Based on the factory traffic information sample, the preset second model to be trained and the preset first model to be trained are jointly trained to obtain an information fusion model and a decision model that meet the accuracy conditions, wherein the second model to be trained is the initial training model of the information fusion model, and the first model to be trained is the initial training model of the decision model.

[0094] It should be noted that the factory traffic information sample refers to a set of data collected from the factory traffic management system, which represents the state of the factory internal traffic at a specific moment or time period. The factory traffic information sample includes but is not limited to vehicle location, speed, traffic flow, pedestrian flow, weather conditions, road conditions, etc., which are used to analyze and predict traffic conditions. The reinforcement learning is a machine learning method that learns how to make decisions by interacting with the environment to maximize a certain cumulative reward. In the context of factory traffic management, reinforcement learning is used to train models so that they can make decisions to optimize traffic flow based on real-time traffic information. The second model to be trained refers to the initial training model of the preset information fusion model, which is used to integrate and process factory traffic information samples from different sources and types to produce a comprehensive and more accurate factory traffic status information sample. The first model to be trained refers to the initial training model of the preset decision model, which is used to make traffic management decisions based on the factory traffic status information sample, such as adjusting the timing of traffic lights, issuing traffic guidance, implementing speed limits, etc. The joint training refers to the process of training two or more models at the same time, so that these models can work together to improve the performance of the overall system. In this embodiment, joint training refers to training the information fusion model and the decision model at the same time so that they can work together better to improve the efficiency and effectiveness of traffic management. The information fusion model refers to a model that can integrate and process factory traffic information samples from different sources and types after training to produce a comprehensive and more accurate factory traffic status information sample. The decision model refers to a model that can make traffic management decisions based on factory traffic status information samples after training, such as adjusting the timing of traffic lights, issuing traffic guidance, implementing speed limits, etc.

[0095] In a feasible implementation manner, the step S30 includes:

[0096] If the factory traffic management decision plan is to expel vehicles and personnel that enter the restricted area, a reminder message is sent to a preset security management device, and a preset warning message is sent through a broadcasting device to obtain and record the processing result;

[0097] If the factory traffic management decision plan is to expel unrecorded vehicles and personnel, a reminder message is sent to the security management device to identify and record the movement tracks of unrecorded vehicles and personnel, and obtain and record the processing results;

[0098] If the factory traffic management decision plan is to record and remind speeding vehicles, the license plate information and model information of the speeding vehicle are identified, the license plate information and the model information are sent to the safety management device, and the processing result is obtained and recorded.

[0099] It should be noted that the factory traffic management decision plan refers to a series of action plans or instructions formulated by the factory traffic safety management system to optimize the traffic flow within the factory and improve safety and efficiency. The expulsion of vehicles and personnel entering the restricted area refers to a specific factory traffic management decision plan, the purpose of which is to remove vehicles and personnel illegally entering the factory restricted area from the area. This usually involves the implementation of security measures to ensure the safety and order of the factory area. The security management equipment refers to the management equipment used in the factory for the security management center, which is used to remind members of the security management department. The reminder information refers to the notification sent by the factory traffic safety management system to the security management equipment, which is used to prompt the equipment to take specific security measures, such as sounding an alarm, starting the access control system, etc. The broadcasting equipment refers to equipment used to broadcast information within the factory, such as a public broadcasting system (PA system). These devices are used to send warning information to all personnel in the factory or personnel in a specific area. The preset warning information refers to a pre-set warning information for issuing to personnel in the factory under specific circumstances. This information may include emergency evacuation instructions, safety warnings, etc.

[0100] The expulsion of unrecorded vehicles and personnel refers to a specific factory traffic management decision plan, the purpose of which is to identify and remove vehicles and personnel that are not authorized to be recorded. This usually involves the identification and tracking of vehicles and personnel. The movement trajectory refers to the movement path of vehicles or personnel within the factory. By recording the movement trajectory, factory safety management can track the movements of unauthorized vehicles and personnel and take corresponding safety measures. The recording and reminder of speeding vehicles refers to a specific factory traffic management decision plan, the purpose of which is to identify speeding vehicles and remind drivers to slow down. This helps to reduce accidents caused by speeding within the factory.

[0101] The license plate information refers to the official registration number of the vehicle, which is usually displayed on the front and rear license plates of the vehicle. The license plate information is the key data for identifying the identity of the vehicle. The style information refers to the model and appearance characteristics of the vehicle, such as the vehicle brand, model, color, etc. The style information helps to further identify and distinguish the vehicle. The processing result refers to the result of the factory traffic safety management system after executing the traffic management decision plan. These results may include vehicles and personnel being successfully expelled, speeding vehicles being reminded to slow down, etc., as well as the implementation effect of these measures.

[0102] For example, the factory traffic safety management device continuously monitors the traffic conditions in the factory. When the device detects that a vehicle or person violates traffic rules, it will automatically formulate a corresponding traffic management decision plan according to the preset rules.

[0103] If the decision is to expel vehicles and personnel that have entered the restricted area, the device will send a reminder message to the preset security management equipment (such as surveillance cameras, access control systems), instructing the equipment to prepare to take action.

[0104] At the same time, the device sends a preset warning message through a broadcasting device (such as a public broadcasting system) to notify vehicles and personnel that have entered the restricted area to leave immediately.

[0105] The device obtains and records the operation results of the security management equipment and the broadcasting equipment, including whether the vehicles and personnel are successfully expelled, and their response time.

[0106] If the decision is to expel unrecorded vehicles and personnel, the device will send a reminder message to the security management device, instructing the device to prepare to take action.

[0107] The device uses surveillance cameras and other sensors to identify and record the movement of unrecorded vehicles and people for subsequent security analysis and tracking.

[0108] The device obtains and records the operation results of the security management equipment, including whether the unrecorded vehicles and personnel are successfully identified and expelled, and their response time.

[0109] If the decision is to record and alert speeding vehicles, the device will use radar speed measuring equipment or cameras to identify the license plate information and model information of the speeding vehicle.

[0110] The device sends the identified license plate information and model information to a security management device, such as a monitoring center or a traffic control center.

[0111] The device obtains and records the operation results of the safety management equipment, including whether the speeding vehicle is successfully reminded to slow down, and the driver's response.

[0112] All the acquired processing results will be stored and analyzed by the device to evaluate the effectiveness of the traffic management decision-making plan and provide data support for future decisions. The device can also adjust the preset rules and response measures based on the processing results to optimize the factory's traffic safety management.

[0113] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the factory traffic management method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0114] This application also provides a factory traffic management device, please refer to Figure 2 , the factory traffic management device comprises:

[0115] An acquisition module 10, for acquiring multi-dimensional information related to traffic management in the factory when a factory traffic management signal is received;

[0116] A fusion module 20 is used to perform multimodal fusion processing on the multi-dimensional information based on a preset information fusion model to obtain factory traffic status information;

[0117] The execution module 30 is used to analyze and process the factory traffic status information based on a preset decision model to obtain a factory traffic management decision plan.

[0118] And / or, the factory traffic management device comprises:

[0119] A first acquisition module is used to acquire a factory traffic information sample;

[0120] A first fusion module is used to perform multimodal fusion processing on the factory traffic information sample based on a preset information fusion model to obtain a factory traffic status information sample;

[0121] A first prediction module, used for inputting the factory traffic status information sample into a preset model to be trained to obtain a predicted traffic management decision plan, wherein the model to be trained is a reinforcement learning model;

[0122] A first determination module, configured to obtain an updated factory traffic status information sample after executing the predicted traffic management decision-making scheme, and determine a state change score value based on the factory traffic status information sample and the updated factory traffic status information sample;

[0123] The first loop module is used to determine whether the state change score value meets the preset loss convergence condition. If the state change score value does not meet the preset loss convergence condition, the model to be trained is adjusted, and the step of returning to input the factory traffic state information sample into the preset model to be trained to obtain a predicted traffic management decision plan is performed until the state change score value meets the preset loss convergence condition, thereby obtaining a decision model that meets the accuracy condition.

[0124] And / or, the factory traffic management device comprises:

[0125] The first processing module is used to perform noise removal, missing value filling and data format normalization on the factory traffic information samples in the factory traffic information samples to obtain pre-processed factory traffic information samples.

[0126] And / or, the factory traffic management device comprises:

[0127] The second acquisition module is used to acquire factory traffic information samples;

[0128] The first training module is used to jointly train a preset second model to be trained and a preset first model to be trained based on the factory traffic information sample to obtain an information fusion model and a decision model that meet the accuracy conditions, wherein the second model to be trained is the initial training model of the information fusion model, and the first model to be trained is the initial training model of the decision model.

[0129] And / or, the execution module 30 includes:

[0130] The first execution module is used to send a reminder message to a preset security management device and send a preset warning message through a broadcasting device if the factory traffic management decision plan is to expel vehicles and personnel that enter the restricted area, and obtain and record the processing result;

[0131] The second execution module is used to send a reminder message to the security management device if the factory traffic management decision plan is to expel unrecorded vehicles and personnel, identify and record the movement tracks of unrecorded vehicles and personnel, and obtain and record the processing results;

[0132] The third execution module is used to identify the license plate information and model information of the speeding vehicle if the factory traffic management decision plan is to record and remind speeding vehicles, send the license plate information and the model information to the safety management device, and obtain and record the processing result.

[0133] The factory traffic management device provided by the present application adopts the factory traffic management method in the above embodiment, which can solve the technical problem of low efficiency of factory traffic management. Compared with the prior art, the beneficial effects of the factory traffic management device provided by the present application are the same as the beneficial effects of the factory traffic management method provided by the above embodiment, and other technical features in the factory traffic management device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0134] The present application provides a factory traffic management device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the factory traffic management method in the above-mentioned embodiment one.

[0135] Reference below Figure 3, which shows a schematic diagram of the structure of a factory traffic management device suitable for implementing the embodiment of the present application. The factory traffic management device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, tablet computers, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The factory traffic management equipment shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0136] like Figure 3 As shown, the plant traffic management device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the plant traffic management device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the plant traffic management device to communicate with other devices wirelessly or wired to exchange data. Although the plant traffic management device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0137] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0138] The factory traffic management device provided by the present application adopts the factory traffic management method in the above embodiment to solve the technical problem of low efficiency of factory traffic management. Compared with the prior art, the beneficial effects of the factory traffic management device provided by the present application are the same as the beneficial effects of the factory traffic management method provided by the above embodiment, and the other technical features in the factory traffic management device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0139] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0140] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0141] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the factory traffic management method in the above-mentioned embodiment.

[0142] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0143] The above-mentioned computer-readable storage medium may be included in the factory traffic management device; or it may exist independently without being assembled into the factory traffic management device.

[0144] The computer-readable storage medium carries one or more programs, and when the one or more programs are executed by the factory traffic management device, the factory traffic management device: when receiving a factory traffic management signal, obtains multi-dimensional information related to traffic management in the factory;

[0145] Based on a preset information fusion model, multi-modal fusion processing is performed on the multi-dimensional information to obtain factory traffic status information;

[0146] Based on a preset decision model, the factory traffic status information is analyzed and processed to obtain a factory traffic management decision plan.

[0147] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0148] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0150] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned factory traffic management method, and can solve the technical problem of low efficiency of factory traffic management. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the factory traffic management method provided by the above-mentioned embodiment, and will not be repeated here.

[0151] The present application also provides a computer program product, including a computer program, which implements the steps of the factory traffic management method as described above when executed by a processor.

[0152] The computer program product provided by this application can solve the technical problem of low efficiency of factory traffic management. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the factory traffic management method provided by the above embodiment, which will not be repeated here.

[0153] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A factory traffic management method, characterized in that: The method includes: When receiving the factory traffic management signal, obtain multi-dimensional information related to the traffic management in the factory; Based on a preset information fusion model, multi-modal fusion processing is performed on the multi-dimensional information to obtain factory traffic status information; Based on a preset decision model, the factory traffic status information is analyzed and processed to obtain a factory traffic management decision plan.

2. The method according to claim 1, characterized in that The multi-dimensional information includes factory restricted area information, vehicle speed information, vehicle identification information and pedestrian identification information.

3. The method according to claim 1, characterized in that Before the step of obtaining multi-dimensional information related to traffic management in the factory when the factory traffic management signal is received, the method includes: Get a sample of factory traffic information; Based on a preset information fusion model, multimodal fusion processing is performed on the factory traffic information sample to obtain a factory traffic status information sample; Inputting the factory traffic status information sample into a preset model to be trained to obtain a predicted traffic management decision plan, wherein the model to be trained is a reinforcement learning model; After executing the predicted traffic management decision-making scheme, obtaining an updated factory traffic status information sample, and determining a state change score value based on the factory traffic status information sample and the updated factory traffic status information sample; Determine whether the state change score value meets the preset loss convergence condition. If the state change score value does not meet the preset loss convergence condition, adjust the model to be trained, return to the step of inputting the factory traffic state information sample into the preset model to be trained, and obtain a predicted traffic management decision plan, until the state change score value meets the preset loss convergence condition, and obtains a decision model that meets the accuracy condition.

4. The method as claimed in claim 3, characterized in that Before the step of performing multimodal fusion processing on the factory traffic information sample based on a preset information fusion model to obtain the factory traffic status information sample, the method includes: The factory traffic information samples in the factory traffic information samples are subjected to noise removal, missing value filling and data format normalization processing to obtain the preprocessed factory traffic information samples.

5. The method according to claim 1, characterized in that When the factory traffic management signal is received, the step of obtaining multi-dimensional information related to the traffic management in the factory also includes: Get a sample of plant traffic information; Based on the factory traffic information sample, the preset second model to be trained and the preset first model to be trained are jointly trained to obtain an information fusion model and a decision model that meet the accuracy conditions, wherein the second model to be trained is the initial training model of the information fusion model, and the first model to be trained is the initial training model of the decision model.

6. The method according to claim 1, characterized in that The step of obtaining the factory traffic management decision-making plan includes: If the factory traffic management decision plan is to expel vehicles and personnel that enter the restricted area, a reminder message is sent to a preset security management device, and a preset warning message is sent through a broadcasting device to obtain and record the processing result; If the factory traffic management decision plan is to expel unrecorded vehicles and personnel, a reminder message is sent to the security management device to identify and record the movement tracks of unrecorded vehicles and personnel, and obtain and record the processing results; If the factory traffic management decision plan is to record and remind speeding vehicles, the license plate information and model information of the speeding vehicle are identified, the license plate information and the model information are sent to the safety management device, and the processing result is obtained and recorded.

7. A factory traffic management device, characterized in that: The device comprises: An acquisition module, used to acquire multi-dimensional information related to traffic management in the factory when receiving a factory traffic management signal; A fusion module, used for performing multimodal fusion processing on the multi-dimensional information based on a preset information fusion model to obtain factory traffic status information; The execution module is used to analyze and process the factory traffic status information based on a preset decision model to obtain a factory traffic management decision plan.

8. A factory traffic management device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the factory traffic management method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the factory traffic management method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the factory traffic management method according to any one of claims 1 to 6 are implemented.

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