Cloud-based park intelligent management and control platform

Abstract

The present invention relates to the field of big data processing technology, and in particular to a cloud-based park intelligent management and control platform, including cloud platform integration, park vehicle dispatching module, park cargo tracking module, collaborative workflow construction module and dispatching risk assessment module; by building a data interaction layer based on the cloud platform, the consistency of data between different modules is ensured; by constructing an objective function to obtain the minimized total driving distance, and setting multi-layer constraints, the transportation efficiency is improved, and the energy consumption and cost are reduced; by making intelligent decisions through a dispatching algorithm, the dispatching accuracy is improved; by monitoring the status of the goods, and processing and analyzing, it is conducive to reducing the risk of cargo loss; by assessing the risk of the collaborative work process, and generating different risk instructions, it is conducive to taking measures to reduce the probability and impact of the risk. The present invention is used to solve the technical problem of low transportation efficiency in logistics parks.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and in particular to a cloud-based park intelligent management and control platform. Background Art

[0002] Big data processing technology refers to the technology and methods used when processing massive amounts of data, involving multiple aspects such as data storage, processing, analysis, and data application.

[0003] With the continuous advancement of technology and the continuous expansion of application fields, big data processing technology has played a more important role. However, in the transportation operations of logistics parks, the following problems still exist: unreasonable vehicle scheduling. Under traditional methods, vehicle scheduling often relies on manual experience or simple algorithms, and it is difficult to achieve the global optimal scheduling plan; information island phenomenon, lack of effective information sharing and coordination between various links in the logistics park, resulting in poor information transmission and inefficient connection of transportation operations; inaccurate path planning, lack of real-time road conditions information and intelligent path planning algorithms, vehicles may choose congested or long routes, increasing transportation time and cost; lack of specialized intelligent management, traditional logistics park management is only a simple information recording tool, unable to make intelligent decisions and realize automated processing capabilities. To this end, the present invention proposes a cloud-based park intelligent management and control platform. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose a cloud-based campus intelligent management and control platform.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Cloud-based park intelligent management and control platform, including: cloud platform integration, park vehicle dispatch module, park cargo tracking module, collaborative workflow building module and dispatch risk assessment module;

[0007] Cloud platform integration uses a scalable cloud computing platform as the core of the entire intelligent management and control system, and is used for big data processing and high-concurrency access; a data interaction layer is built based on the cloud platform for data exchange between the park vehicle dispatch module and the park cargo tracking module, including vehicle location information and cargo tracking data;

[0008] The park vehicle dispatch module is used to install positioning equipment for transport vehicles within the park to monitor the location and driving status of the vehicles in real time; construct an objective function to minimize the total driving distance, set multi-layer constraints based on the objective function, and obtain a dispatch plan through a dispatch algorithm;

[0009] The park cargo tracking module is used to initialize the park cargo information, use positioning technology to track and collect data in real time, update the location of the park cargo, monitor the status of the cargo, detect whether there are any abnormal conditions, trigger the notification mechanism according to the abnormal conditions, and respond in time to adjust the cargo status;

[0010] The collaborative workflow building module is used to connect and establish the connection between vehicle scheduling and cargo tracking. When transporting cargo, the park cargo tracking module will trigger the park vehicle scheduling module to arrange the corresponding transport vehicles for the cargo. At the same time, the park vehicle scheduling module will dynamically adjust the transportation plan according to the cargo tracking information.

[0011] The dispatch risk assessment module is used to assess the risks of the collaborative working process between the park vehicle dispatch module and the park cargo tracking module, and to generate first-level risk instructions, second-level risk instructions, and third-level risk instructions.

[0012] It should be noted that the application object of the cloud-based park intelligent management and control platform in the embodiment of the present invention can be the monitoring of the operation process of the logistics park, and can be used to monitor the transportation status of vehicles and goods in real time. Specifically, it can be used to conduct a comprehensive analysis and accurate evaluation of the vehicle scheduling and cargo tracking links through big data processing technology, so as to optimize resource allocation, improve emergency response capabilities, ensure the safe and stable operation of various activities in the park, and promote the transformation of the logistics park towards intelligence and digitalization, thereby enhancing the overall competitiveness and sustainable development capabilities of the logistics park.

[0013] Furthermore, the park vehicle dispatch module monitors the location and driving status of the vehicle in real time, constructs an objective function to minimize the total driving distance, sets multiple layers of constraints according to the objective function, and obtains the dispatch plan through the dispatch algorithm. The process includes:

[0014] Step 1: Define the vehicle set in the logistics park as , then each car Has its place and status , the state is idle or loaded; where i represents the coordinate number of the park vehicle, i=1, 2, ..., n; n is a positive integer; n represents the total number of vehicles in the park vehicle set;

[0015] Step 2: Define the cargo collection in the logistics park as , then each cargo Has its destination and quantity ; Wherein, j represents the number of the park goods, j=1, 2, ..., m; m is a positive integer; m represents the total number of goods in the park goods collection; f represents the coordinate number of the destination of the goods;

[0016] Step 3: Obtain the minimum total driving distance according to the objective function; wherein the objective function formula is as follows:

[0017] ,

[0018] In the formula, Represents finding a set of binary decision variables The value of makes the function minimize; represents the sum of the distance traveled by all vehicles; Indicates vehicle To goods The Euclidean distance of Indicates vehicle To goods The path weight is used to reflect the traffic congestion situation. The larger the value, the more serious the congestion situation. Represented as a binary decision variable, representing vehicle Whether service goods are allocated ,when When Service goods ,when When Unserviced goods ;

[0019] Step 4: Set multi-layer constraints based on the objective function:

[0020] ,

[0021] Where, (1) represents the park vehicle status constraint layer, that is, only idle vehicles are allowed to be assigned to serve goods and complete the pickup task; (2) represents the park vehicle capacity constraint layer, where Indicates vehicle The maximum cargo capacity; (3) represents the park cargo demand satisfaction layer, that is, each cargo must have at least one vehicle to complete the pickup task; (4) represents the path accessibility constraint layer, where D represents the maximum driving distance limit of the park vehicle;

[0022] Step 5: Update binary decision variables using iterative formulas The value of; where the iteration formula is as follows:

[0023] ,

[0024] In the formula, t represents the current number of iterations; Indicates that at the tth iteration, the vehicle Service goods The probability of Represents a heuristic rule-based Amount of change; Represents the learning factor, used to control The update speed of ; where the iterative formula uses a heuristic algorithm for binary decision variables Iterative update as part of finding the optimal or near-optimal scheduling solution, and continuously improve the binary decision variables in an iterative way to approach the optimal solution; in practical applications, The calculation of may involve a variety of factors, including but not limited to: predicted traffic conditions, the amount of cargo already assigned to the vehicle, the dispatch status of other vehicles, etc.;

[0025] Step 6: According to the updated The objective function is recalculated and the multi-layer constraints are applied again;

[0026] Step 7. Repeat the operation and check the termination condition: if the predetermined termination condition is met, stop the iteration; otherwise, return to step 3 to step 6 to continue iterating until a satisfactory solution is obtained; the predetermined termination condition means reaching the maximum number of iterations or finding the optimal solution to determine the optimal or approximately optimal scheduling solution; the entire iterative process will be repeated in the algorithm until a certain stopping condition is met. This process simulates the process of natural selection and evolution. Each iteration will evaluate the candidate solution according to a certain function and update the solution according to the evaluation result in the hope of obtaining a better scheduling solution.

[0027] Furthermore, the park cargo tracking module initializes the park cargo information, uses positioning technology to track and collect data in real time, and updates the location of the park cargo in the following process:

[0028] For each item, record its initial location and the initial state ;

[0029] Assign a unique identifier ID to each shipment;

[0030] Use positioning technology to regularly obtain the new location of the goods and status ;in, Indicates a time interval; time interval Can be adjusted according to actual situation;

[0031] When new location information is received, the location of the goods is updated ; When the location of the cargo changes, a linear interpolation method is used to estimate the location of the cargo during the period between two reports.

[0032] Furthermore, the process of linear interpolation method analysis includes:

[0033] Assume that the goods are at time and The locations are and ;

[0034] At the estimated time When the location of the shipment is determined, a linear interpolation formula is used to estimate the shipment's location during the period between the two reports:

[0035] ,

[0036] In the formula, is a constant term, where ; express and A point in time between .

[0037] Furthermore, the park cargo tracking module monitors the status of the cargo, detects whether there are any abnormal conditions, and triggers a notification mechanism based on the abnormal conditions. The process of timely feedback response includes:

[0038] Determine the status of the goods according to the preset standards or thresholds Is it normal? If the status If there is a change, the status record of the goods is updated; wherein, the standard or threshold is set based on the historical performance and expected behavior of the goods. For example, if the focus is on the temperature of the goods, the standard or threshold is the maximum temperature range and the minimum temperature range. When the actual temperature of the goods exceeds the range, it is considered that the goods are in an abnormal state;

[0039] Building anomaly detection functions , where Indicates the current status of the goods. Indicates the expected status of the goods, is the tolerance error term, which indicates the allowable range of cargo status changes; when , it indicates that an abnormality is detected; the abnormality detection function is based on the state or position deviation compared to the expected path;

[0040] If an abnormality is detected, the notification mechanism is triggered using the formula N(ID); where N is the indicator function used for notification. When an abnormality is detected, the indicator function N(ID) value is 1, otherwise it is 0; ID represents the unique identifier of the goods; the notification is sent to the management personnel via SMS or email;

[0041] Collect the manager's response R to the abnormal notification; adjust the status of the goods according to R ; If R indicates that the exception has been confirmed and resolved, the cargo status is updated to ; If R contains the new status information of the goods, then the status of the goods is directly updated to ;

[0042] In summary, the whole process is expressed as:

[0043] ,

[0044] Where T represents the tracking function, which is responsible for updating the real-time location and status of the goods; M represents the management response function, which is used to receive the results of abnormal situation detection and return the management response R; L represents the prediction function, which is used to predict the location and status of the goods at the next moment.

[0045] Furthermore, the process of linking and establishing the connection between vehicle dispatching and cargo tracking by the collaborative workflow building module includes:

[0046] When the goods are ready for transport, the park cargo tracking module sends a request Q to the park vehicle dispatch module, including the current location of the goods , Destination location and the status of the goods ; Among them, the cargo status includes but is not limited to the type, size, weight, temperature, urgency and other information of the cargo;

[0047] The park vehicle dispatch module receives the information in the request Q and calculates the available vehicles of matches; Indicates the number of available vehicles; get each vehicle Current location and vehicle status ;Choose the best vehicle The function expression is:

[0048] ,

[0049] In the formula, Represents the vehicle minimization function, which is used to find vehicles that meet the requested information ; Indicates vehicle The distance from the current location of the vehicle to the current location of the goods; Indicates the maximum load capacity of the vehicle Less than the judgment value of the cargo weight Z; The weight coefficient indicating the priority of cargo transportation;

[0050] During the transportation process, the park cargo tracking module continuously updates the location and status of the cargo; the park vehicle dispatching module adjusts the transportation plan based on the updated information on the location and status of the cargo. , including changing routes or reallocating tasks; where A represents the adjusted transportation plan;

[0051] The park vehicle dispatch module will adjust the transportation plan Sent to the park cargo tracking module; the park cargo tracking module will send the new transportation plan Update the estimated arrival time and status of your shipment ; Where U represents the update function, which refreshes the status and estimated arrival time of the goods based on the adjusted transportation plan.

[0052] Furthermore, the dispatch risk assessment module assesses the risk of the collaborative working process of the park vehicle dispatch module and the park cargo tracking module, and generates a first-level risk instruction, a second-level risk instruction, and a third-level risk instruction, including the following process:

[0053] Obtain technical failure events through monitoring logs, maintenance records or fault reports, and analyze the number of failures in technical failure events in units of one quarter, combined with the total operating time of the park vehicle dispatch module and the park cargo tracking module; including the frequency of events such as hardware failure, software crash, and network interruption;

[0054] Real-time measurement of data transmission delay between the park vehicle dispatch module and the park cargo tracking module, and recording of data;

[0055] Extracting data on the number of errors made by operators when performing tasks from the operation log;

[0056] Risk assessment through risk assessment index:

[0057] ,

[0058] In the formula, RAI represents the risk assessment index; tr represents the technical failure rate; represents the information synchronization efficiency, where the total information synchronization efficiency of the park vehicle dispatch module and the park cargo tracking module is 100%. If there is a delay or error, the information synchronization efficiency will decrease; LI represents the degree of delay in information synchronization, which is Working together on risk assessment of information synchronization, Used to amplify the negative impact of delay on information synchronization efficiency; represents the environmental interference factor; G represents the cargo safety level, which is set according to the specific attributes of the cargo, such as value, vulnerability, and confidentiality; AE represents the effectiveness of the scheduling algorithm, which is obtained based on actual operation data, that is, the ability of the algorithm to optimize scheduling, reduce conflicts, and improve efficiency, expressed as a percentage. When it is close to 100%, it means it is very effective; Indicates the error rate of human operation; Represents the weight coefficient, which is used to adjust the impact of each reference factor and interaction item on the overall risk score;

[0059] Preset the threshold range of risk assessment index (RAI1, RAI2), where RAI1<RAI2;

[0060] The risk assessment index is compared and analyzed with the threshold range. If RAI<RAI1, it means that the collaborative working process of vehicle scheduling and cargo tracking is the most stable, and a third-level risk instruction is generated; if RAI1≤RAI≤RAI2, it means that the risk level of the collaborative working process of vehicle scheduling and cargo tracking is average, and a second-level risk instruction is generated; if RAI>RAI2, it means that there are serious risk factors in the collaborative working process of vehicle scheduling and cargo tracking, and a first-level risk instruction is generated; among them, for the second-level risk instruction, it is necessary to pay close attention to the changes in risk factors and take appropriate preventive measures and management measures to avoid risk escalation; for the third-level risk instruction, it is necessary to immediately activate the emergency plan and take all necessary measures to reduce the risk.

[0061] Compared with the existing technology, the advantages of the cloud-based park intelligent management and control platform and method provided by the present invention are:

[0062] 1. The present invention integrates data exchange through cloud platform integration, and can integrate data from different modules, thereby improving the overall coordination efficiency of the system; by constructing an objective function to obtain the minimum total driving distance, multi-layer constraints are set according to the objective function to improve transportation efficiency and reduce energy consumption and costs; by using scheduling algorithms to make intelligent decisions, human intervention and errors are reduced, and scheduling accuracy and efficiency are improved;

[0063] 2. The present invention can timely discover and deal with potential problems and reduce the risk of cargo loss by monitoring the status of cargo and detecting abnormal situations; by triggering a notification mechanism based on abnormal situations, timely feedback responses and adjustment of cargo status can be made to ensure that the cargo is in a normal state;

[0064] 3. The present invention effectively connects the two modules of vehicle dispatching and cargo tracking to ensure smooth information flow and collaborative work during the transportation process; dynamically adjusts the transportation plan according to the cargo tracking information to adapt to the changing needs during the transportation process; and evaluates the risks of the collaborative work process and generates first-level risk instructions, second-level risk instructions, and third-level risk instructions to provide early warning of potential problems.

[0065] To sum up, the present invention can ensure the safety and reliability of park transportation work through vehicle transportation scheduling, real-time cargo tracking and risk assessment functions according to actual conditions. At the same time, various modules cooperate with each other to form an efficient, intelligent and collaborative logistics park intelligent management and control platform, which provides strong support for the operation and management of the logistics park and ensures the efficient and stable operation of the subsequent cloud-based park intelligent management and control platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a module diagram of the cloud-based park intelligent management and control platform proposed in the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Reference Figure 1 , a cloud-based park intelligent management and control platform, the system includes cloud platform integration, park vehicle dispatching module, park cargo tracking module, collaborative workflow building module and dispatching risk assessment module;

[0069] Cloud platform integration uses a scalable cloud computing platform as the core of the entire intelligent management and control system, and is used for big data processing and high-concurrency access; a data interaction layer is built based on the cloud platform for data exchange between the park vehicle dispatch module and the park cargo tracking module, including vehicle location information and cargo tracking data;

[0070] The park vehicle dispatch module is used to install positioning equipment for transport vehicles within the park to monitor the location and driving status of the vehicles in real time; construct an objective function to minimize the total driving distance, set multi-layer constraints based on the objective function, and obtain a dispatch plan through a dispatch algorithm;

[0071] The park cargo tracking module is used to initialize the park cargo information, use positioning technology to track and collect data in real time, update the location of the park cargo, monitor the status of the cargo, detect whether there are any abnormal conditions, trigger the notification mechanism according to the abnormal conditions, and respond in time to adjust the cargo status;

[0072] The collaborative workflow building module is used to connect and establish the connection between vehicle scheduling and cargo tracking. When transporting cargo, the park cargo tracking module will trigger the park vehicle scheduling module to arrange the corresponding transport vehicles for the cargo. At the same time, the park vehicle scheduling module will dynamically adjust the transportation plan according to the cargo tracking information.

[0073] The dispatch risk assessment module is used to assess the risks of the collaborative working process between the park vehicle dispatch module and the park cargo tracking module, and to generate first-level risk instructions, second-level risk instructions, and third-level risk instructions.

[0074] It should be noted that the application object of the cloud-based park intelligent management and control platform in the embodiment of the present invention can be the monitoring of the operation process of the logistics park, and can be used to monitor the transportation status of vehicles and goods in real time. Specifically, it can be used to conduct a comprehensive analysis and accurate evaluation of the vehicle scheduling and cargo tracking links through big data processing technology, so as to optimize resource allocation, improve emergency response capabilities, ensure the safe and stable operation of various activities in the park, and promote the transformation of the logistics park towards intelligence and digitalization, thereby enhancing the overall competitiveness and sustainable development capabilities of the logistics park.

[0075] The park vehicle dispatch module monitors the location and driving status of the vehicle in real time, constructs an objective function to minimize the total driving distance, sets multiple layers of constraints based on the objective function, and obtains the dispatch plan through the dispatch algorithm. The steps include:

[0076] Step 101: Define the vehicle set in the logistics park as , then each car Has its place and status , the state is idle or loaded; where i represents the coordinate number of the park vehicle, i=1, 2, ..., n; n is a positive integer; n represents the total number of vehicles in the park vehicle set;

[0077] Step 102: Define the cargo collection in the logistics park as , then each cargo Has its destination and quantity ; Wherein, j represents the number of the park goods, j=1, 2, ..., m; m is a positive integer; m represents the total number of goods in the park goods collection; f represents the coordinate number of the destination of the goods;

[0078] Step 103: Obtain the minimum total driving distance according to the objective function; wherein the objective function formula is as follows:

[0079] ,

[0080] In the formula, Represents finding a set of binary decision variables The value of makes the function minimize; represents the sum of the distance traveled by all vehicles; Indicates vehicle To goods The Euclidean distance of Indicates vehicle To goods The path weight is used to reflect the traffic congestion situation. The larger the value, the more serious the congestion situation. Represented as a binary decision variable, representing vehicle Whether service goods are allocated ,when When Service goods ,when When Unserviced goods ;

[0081] Step 104: Set multi-layer constraints based on the objective function:

[0082] ,

[0083] Where, (1) represents the park vehicle status constraint layer, that is, only idle vehicles are allowed to be assigned to serve goods and complete the pickup task; (2) represents the park vehicle capacity constraint layer, where Indicates vehicle The maximum cargo capacity; (3) represents the park cargo demand satisfaction layer, that is, each cargo must have at least one vehicle to complete the pickup task; (4) represents the path accessibility constraint layer, where D represents the maximum driving distance limit of the park vehicle;

[0084] Step 105: Update binary decision variables using iterative formulas The value of; where the iteration formula is as follows:

[0085] ,

[0086] In the formula, t represents the current number of iterations; Indicates that at the tth iteration, the vehicle Service goods The probability of Represents a heuristic rule-based Amount of change; Represents the learning factor, used to control Update speed;

[0087] In step 105, the iterative formula uses a heuristic algorithm to perform binary decision variables Iterative update as part of finding the optimal or near-optimal scheduling solution, and continuously improve the binary decision variables in an iterative way to approach the optimal solution; in practical applications, The calculation of may involve a variety of factors, including but not limited to: predicted traffic conditions, the amount of cargo already assigned to the vehicle, the dispatch status of other vehicles, etc.;

[0088] Step 106: According to the updated The objective function is recalculated and the multi-layer constraints are applied again;

[0089] Step 107, repeat the operation and check the termination condition: if the predetermined termination condition is met, stop the iteration; otherwise, return to step 3 to step 6 to continue iterating until a satisfactory solution is obtained;

[0090] In step 107, the predetermined termination condition indicates reaching the maximum number of iterations or finding the optimal solution to determine the optimal or approximately optimal scheduling solution; the entire iterative process will be repeated in the algorithm until a certain stopping condition is met. This process simulates the process of natural selection and evolution. Each iteration will evaluate the candidate solution according to a certain function and update the solution according to the evaluation result in the hope of obtaining a better scheduling solution.

[0091] The park cargo tracking module initializes the park cargo information, uses positioning technology to track and collect data in real time, updates the location of the park cargo, monitors the status of the cargo, detects whether there are any abnormalities, and triggers the notification mechanism according to the abnormalities. The steps of timely feedback response include:

[0092] Step 201: For each item, record its initial location and the initial state ;

[0093] Step 202: assign a unique identifier ID to each cargo;

[0094] Step 203: Use positioning technology to periodically obtain the new location of the goods and status ;in, Indicates a time interval; time interval Can be adjusted according to actual situation;

[0095] Step 204: When new location information is received, update the location of the goods ; When the location of the goods changes, the linear interpolation method is used to estimate the location of the goods in the time period between two reports; the linear interpolation method is analyzed as follows:

[0096] Assume that the goods are at time and The locations are and ;

[0097] At the estimated time When the location of the shipment is determined, a linear interpolation formula is used to estimate the shipment's location during the period between the two reports:

[0098] ,

[0099] In the formula, is a constant term, where ; express and A point in time between ;

[0100] Step 205: Determine the cargo status according to the preset standard or threshold Is it normal? If the status If there is a change, the status record of the goods will be updated;

[0101] In step 205, standards or thresholds are set based on the historical performance and expected behavior of the goods. For example, if the focus is on the temperature of the goods, the standards or thresholds are the maximum temperature range and the minimum temperature range. When the actual temperature of the goods exceeds the range, it is considered that the state of the goods is abnormal.

[0102] Step 206: Establish an abnormal situation detection function , where Indicates the current status of the goods. Indicates the expected status of the goods, is the tolerance error term, which indicates the allowable range of cargo status changes; when , it indicates that an abnormality is detected; the abnormality detection function is based on the state or comparison of position deviation to expected path;

[0103] Step 207: If an abnormality is detected, a notification mechanism is triggered using the formula N(ID); wherein N is an indicator function for notification, and when an abnormality is detected, the indicator function N(ID) value is 1, otherwise the value is 0; ID represents a unique identifier of the goods; the notification is sent to the management personnel via SMS or email;

[0104] Step 208: Collect the manager's response R to the abnormal notification; adjust the status of the goods according to R ; If R indicates that the exception has been confirmed and resolved, the cargo status is updated to ; If R contains the new status information of the goods, then the status of the goods is directly updated to ;

[0105] Step 209: In summary, the entire process is expressed as follows:

[0106] ,

[0107] Where T represents the tracking function, which is responsible for updating the real-time location and status of the goods; M represents the management response function, which is used to receive the results of abnormal situation detection and return the management response R; L represents the prediction function, which is used to predict the location and status of the goods at the next moment.

[0108] The steps for the collaborative workflow building module to connect and establish the link between vehicle dispatch and cargo tracking include:

[0109] Step 301: When the cargo is ready for transport, the park cargo tracking module sends a request Q to the park vehicle dispatch module, including the current location of the cargo. , Destination location and the status of the goods ;

[0110] In step 301, the cargo status includes but is not limited to the type, size, weight, temperature, urgency and other information of the cargo;

[0111] Step 302: The park vehicle dispatch module receives the information in the request Q and dispatches available vehicles. of matches; Indicates the number of available vehicles; get each vehicle Current location and vehicle status ;Choose the best vehicle The function expression is:

[0112] ,

[0113] In the formula, Represents the vehicle minimization function, which is used to find vehicles that meet the requested information ; Indicates vehicle The distance from the current location of the vehicle to the current location of the goods; Indicates the maximum load capacity of the vehicle Less than the judgment value of the cargo weight Z; The weight coefficient indicating the priority of cargo transportation;

[0114] Step 303: During the transportation process, the park cargo tracking module continuously updates the location and status of the cargo; the park vehicle dispatching module adjusts the transportation plan based on the updated information on the location and status of the cargo. , including changing routes or reallocating tasks; where A represents the adjusted transportation plan;

[0115] Step 304: The park vehicle dispatch module will adjust the transportation plan Sent to the park cargo tracking module; the park cargo tracking module will send the new transportation plan Update the estimated arrival time and status of your shipment ; Where U represents the update function, which refreshes the status and estimated arrival time of the goods based on the adjusted transportation plan.

[0116] The steps of the dispatch risk assessment module assessing the risk of the collaborative working process of the park vehicle dispatch module and the park cargo tracking module and generating the first-level risk instruction, the second-level risk instruction and the third-level risk instruction include:

[0117] Step 401: Obtain technical failure events through monitoring logs, maintenance records or failure reports, and analyze the number of failures in the technical failure events in units of one quarter, combined with the total operating time of the park vehicle scheduling module and the park cargo tracking module;

[0118] In step 401, technical failure events include the frequency of events such as hardware failure, software crash, and network interruption;

[0119] Step 402: measure the delay of data transmission between the park vehicle dispatching module and the park cargo tracking module in real time, and record the data;

[0120] Step 403: extracting data on the number of errors that occurred when the operator performed the task from the operation log;

[0121] Step 404: Perform risk assessment using a risk assessment index:

[0122] ,

[0123] In the formula, RAI represents the risk assessment index; tr represents the technical failure rate; represents the information synchronization efficiency, where the total information synchronization efficiency of the park vehicle dispatch module and the park cargo tracking module is 100%. If there is a delay or error, the information synchronization efficiency will decrease; LI represents the degree of delay in information synchronization, which is Working together on risk assessment of information synchronization, Used to amplify the negative impact of delay on information synchronization efficiency; represents the environmental interference factor; G represents the cargo safety level, which is set according to the specific attributes of the cargo, such as value, vulnerability, and confidentiality; AE represents the effectiveness of the scheduling algorithm, which is obtained based on actual operation data, that is, the ability of the algorithm to optimize scheduling, reduce conflicts, and improve efficiency, expressed as a percentage. When it is close to 100%, it means it is very effective; Indicates the error rate of human operation; Represents the weight coefficient, which is used to adjust the impact of each reference factor and interaction item on the overall risk score;

[0124] Step 405: Preset a threshold range of risk assessment index (RAI1, RAI2), where RAI1 < RAI2;

[0125] Step 406: Compare and analyze the risk assessment index with the threshold range. If RAI<RAI1, it means that the collaborative working process of vehicle dispatching and cargo tracking is the most stable, and a third-level risk instruction is generated; if RAI1≤RAI≤RAI2, it means that the risk level of the collaborative working process of vehicle dispatching and cargo tracking is average, and a second-level risk instruction is generated; if RAI>RAI2, it means that there are serious risk factors in the collaborative working process of vehicle dispatching and cargo tracking, and a first-level risk instruction is generated;

[0126] In step 406, for level 2 risk instructions, it is necessary to closely monitor changes in risk factors and take appropriate preventive and management measures to avoid risk escalation; for level 3 risk instructions, it is necessary to immediately initiate an emergency plan and take all necessary measures to reduce risks.

[0127] In the embodiment of the present invention, big data processing, high-concurrency access and data exchange are performed through cloud platform integration to ensure stable and efficient data analysis; the data interaction layer serves as a bridge between the vehicle scheduling module and the cargo tracking module to achieve real-time and accurate data transmission, ensuring the synchronization and consistency of information. By building a data interaction layer, data from different modules can be integrated to improve the overall coordination efficiency of the system; by installing positioning equipment for transport vehicles, real-time monitoring of vehicle location and driving status is achieved, providing accurate data support for scheduling decisions; by constructing objective functions and setting multi-layer constraints, the scheduling algorithm can generate the optimal scheduling plan, minimize the total driving distance, improve transportation efficiency and reduce costs; by dynamically adjusting the transportation plan according to the tracking information of the goods, the flexibility of the transportation process is ensured. The system can improve the safety and response speed of the goods by improving the safety and response speed of the goods; by using positioning technology to track and collect cargo data in real time, it ensures the accurate grasp of the location and status of the goods; by monitoring the status of the goods, it can promptly detect abnormal situations and trigger notification mechanisms, which helps to quickly respond to and solve problems, thereby reducing losses; by updating the cargo location information in real time, it provides the latest data support for vehicle scheduling and cargo transportation; by effectively connecting the two modules of vehicle scheduling and cargo tracking, it realizes seamless connection from cargo tracking to vehicle scheduling, automates the processing of each link in the cargo transportation process, and reduces manual intervention; by conducting a comprehensive risk assessment of the collaborative working process of vehicle scheduling and cargo tracking, identifying potential risk points and hidden dangers, and generating different risk instructions based on the assessment results, it helps managers to prioritize high-risk issues and ensure the stable operation of the system. In summary, the example of the present invention solves the problem of low transportation efficiency in logistics parks. In actual situations, more data and contextual information may be required to make specific decisions and optimization plans.

[0128] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0129] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically based on the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0130] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0131] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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.

[0132] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0136] Finally: The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Cloud-based park intelligent management and control platform, characterized by: It includes cloud platform integration, campus vehicle dispatching module, campus cargo tracking module, collaborative workflow building module and dispatch risk assessment module; Cloud platform integration uses a scalable cloud computing platform as the core of the entire intelligent management and control system, and is used for big data processing and high-concurrency access; a data interaction layer is built based on the cloud platform for data exchange between the park vehicle dispatch module and the park cargo tracking module; The park vehicle dispatch module is used to install positioning equipment for transport vehicles within the park to monitor the location and driving status of the vehicles in real time; construct an objective function to minimize the total driving distance, set multi-layer constraints based on the objective function, and obtain a dispatch plan through a dispatch algorithm; The park vehicle dispatch module monitors the location and driving status of the vehicle in real time, constructs an objective function to minimize the total driving distance, sets multiple constraints based on the objective function, and obtains the dispatch plan through the dispatch algorithm. The process includes: Step 1: Define the vehicle set in the logistics park as , then each car Has its place and status , the state is idle or loaded; where i represents the coordinate number of the park vehicle, i=1, 2, ..., n; n is a positive integer; n represents the total number of vehicles in the park vehicle set; Step 2: Define the cargo collection in the logistics park as , then each cargo Has its destination and quantity ; Wherein, j represents the number of the park goods, j=1, 2, ..., m; m is a positive integer; m represents the total number of goods in the park goods collection; f represents the coordinate number of the destination of the goods; Step 3: Obtain the minimum total driving distance according to the objective function; wherein the objective function formula is as follows: ， In the formula, Represents finding a set of binary decision variables The value of makes the function minimize; represents the sum of the distance traveled by all vehicles; Indicates vehicle To goods The Euclidean distance of Indicates vehicle To goods The path weight is used to reflect the traffic congestion situation. The larger the value, the more serious the congestion situation. Represented as a binary decision variable, representing vehicle Whether service goods are allocated ,when When Service goods ,when When Unserviced goods ; Step 4: Set multi-layer constraints based on the objective function: ， Where, (1) represents the park vehicle status constraint layer, that is, only idle vehicles are allowed to be assigned to serve goods and complete the pickup task; (2) represents the park vehicle capacity constraint layer, where Indicates vehicle The maximum cargo capacity; (3) represents the park cargo demand satisfaction layer, that is, each cargo must have at least one vehicle to complete the pickup task; (4) represents the path accessibility constraint layer, where D represents the maximum driving distance limit of the park vehicle; Step 5: Update binary decision variables using iterative formulas The value of; where the iteration formula is as follows: ， In the formula, t represents the current number of iterations; Indicates that at the tth iteration, the vehicle Service goods The probability of Represents a heuristic rule-based Amount of change; Represents the learning factor, used to control Update speed; Step 6: According to the updated The objective function is recalculated and the multi-layer constraints are applied again; Step 7: Repeat the operation and check the termination condition: if the predetermined termination condition is met, stop the iteration; otherwise, return to step 3 to step 6 and continue iterating until a satisfactory solution is obtained; The park cargo tracking module is used to initialize the park cargo information, use positioning technology to track and collect data in real time, update the location of the park cargo, monitor the status of the cargo, detect whether there are any abnormal conditions, trigger the notification mechanism according to the abnormal conditions, and respond in time to adjust the cargo status; The collaborative workflow building module is used to connect and establish the connection between vehicle scheduling and cargo tracking. When transporting cargo, the park cargo tracking module will trigger the park vehicle scheduling module to arrange the corresponding transport vehicles for the cargo. At the same time, the park vehicle scheduling module will dynamically adjust the transportation plan according to the cargo tracking information. The process of linking the collaborative workflow building module and establishing the connection between vehicle dispatching and cargo tracking includes: When the goods are ready for transport, the park cargo tracking module sends a request Q to the park vehicle dispatch module, including the current location of the goods , Destination location and the status of the goods ; The park vehicle dispatch module receives the information in the request Q and calculates the available vehicles of matches; Indicates the number of available vehicles; get each vehicle Current location and vehicle status ;Choose the best vehicle The function expression is: ， In the formula, Represents the vehicle minimization function, which is used to find vehicles that meet the requested information ; Indicates vehicle The distance from the current location of the vehicle to the current location of the goods; Indicates the maximum load capacity of the vehicle Less than the judgment value of the cargo weight Z; The weight coefficient indicating the priority of cargo transportation; During the transportation process, the park cargo tracking module continuously updates the location and status of the cargo; the park vehicle dispatching module adjusts the transportation plan based on the updated information on the location and status of the cargo. , including changing routes or reallocating tasks; where A represents the adjusted transportation plan; The park vehicle dispatch module will adjust the transportation plan Sent to the park cargo tracking module; the park cargo tracking module will send the new transportation plan Update the estimated arrival time and status of your shipment ; Where U represents the update function, which updates the status and estimated arrival time of the goods based on the adjusted transportation plan; The dispatch risk assessment module is used to assess the risks of the collaborative working process between the park vehicle dispatch module and the park cargo tracking module, and to generate first-level risk instructions, second-level risk instructions, and third-level risk instructions.

2. The cloud-based park intelligent management and control platform according to claim 1 is characterized in that: The park cargo tracking module initializes the park cargo information, uses positioning technology to track and collect data in real time, and updates the location of the park cargo in the following process: For each item, record its initial location and the initial state ; Assign a unique identifier ID to each shipment; Use positioning technology to regularly obtain the new location of the goods and status ;in, Indicates a time interval; When new location information is received, the location of the goods is updated ; When the location of the cargo changes, a linear interpolation method is used to estimate the location of the cargo during the period between two reports.

3. The cloud-based park intelligent management and control platform according to claim 2 is characterized in that: The process of linear interpolation method analysis includes: Assume that the goods are at time and The locations are and ; At the estimated time When the location of the shipment is determined, a linear interpolation formula is used to estimate the shipment's location during the period between the two reports: ， In the formula, is a constant term, where ; express and A point in time between .

4. The cloud-based park intelligent management and control platform according to claim 1 is characterized in that: The park cargo tracking module monitors the status of the cargo, detects whether there are any abnormal conditions, and triggers the notification mechanism based on the abnormal conditions. The process of timely feedback response includes: Determine the status of the goods according to the preset standards or thresholds Is it normal? If the status If there is a change, update the status record of the goods; Building anomaly detection functions , where Indicates the current status of the goods. Indicates the expected status of the goods, is the tolerance error term, which indicates the allowable range of cargo status changes; when , it indicates that an abnormality is detected; the abnormality detection function is based on the state or comparison of position deviation to expected path; If an abnormality is detected, the notification mechanism is triggered using the formula N(ID); where N is the indicator function used for notification. When an abnormality is detected, the indicator function N(ID) value is 1, otherwise it is 0; ID represents the unique identifier of the goods; the notification is sent to the management personnel via SMS or email; Collect the manager's response R to the abnormal notification; adjust the status of the goods according to R ; If R indicates that the exception has been confirmed and resolved, the cargo status is updated to ; If R contains the new status information of the goods, then the status of the goods is directly updated to ; In summary, the whole process is expressed as: ， Where T represents the tracking function, which is responsible for updating the real-time location and status of the goods; M represents the management response function, which is used to receive the results of abnormal situation detection and return the management response R; L represents the prediction function, which is used to predict the location and status of the goods at the next moment.

5. The cloud-based park intelligent management and control platform according to claim 1 is characterized in that: The process in which the dispatch risk assessment module assesses the risk of the collaborative working process of the park vehicle dispatch module and the park cargo tracking module and generates the first-level risk instructions, the second-level risk instructions and the third-level risk instructions includes: Obtain technical failure events through monitoring logs, maintenance records or fault reports, and analyze the number of failures in technical failure events in units of one quarter, combined with the total operating time of the park vehicle dispatch module and the park cargo tracking module; Real-time measurement of data transmission delay between the park vehicle dispatch module and the park cargo tracking module, and recording of data; Extracting data on the number of errors made by operators when performing tasks from the operation log; Risk assessment through risk assessment index: ， In the formula, RAI represents the risk assessment index; tr represents the technical failure rate; represents the information synchronization efficiency, where the total information synchronization efficiency of the park vehicle dispatching module and the park cargo tracking module is 100%. If there is a delay or error, the information synchronization efficiency will decrease; LI represents the degree of delay in information synchronization, which is Working together on risk assessment of information synchronization, Used to amplify the negative impact of delay on information synchronization efficiency; represents the environmental interference factor; G represents the cargo safety level; AE represents the effectiveness of the scheduling algorithm, which is obtained based on actual operation data; Indicates the error rate of human operation; Represents the weight coefficient, which is used to adjust the impact of each reference factor and interaction item on the overall risk score; Preset the threshold range of risk assessment index (RAI1, RAI2), where RAI1<RAI2; The risk assessment index is compared and analyzed with the threshold range. If RAI<RAI1, it means that the collaborative working process of vehicle scheduling and cargo tracking is the most stable, and a third-level risk instruction is generated; if RAI1≤RAI≤RAI2, it means that the risk level of the collaborative working process of vehicle scheduling and cargo tracking is average, and a second-level risk instruction is generated; if RAI>RAI2, it means that there are serious risk factors in the collaborative working process of vehicle scheduling and cargo tracking, and a first-level risk instruction is generated.

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