Intelligent logistics platform management system and method based on multi-source data

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent logistics platform management system and method based on multi-source data. The method comprises the following steps: acquiring cold-chain logistics order data; performing multi-type cargo temperature control demand processing on the cold-chain logistics order data to generate cargo temperature control demand data; performing multi-node temperature control task decomposition on the cold-chain logistics order data through the cargo temperature control demand data to generate multi-node fine temperature control task data; performing cold chain abnormal feature analysis according to the multi-node fine temperature control task data to generate abnormal temperature control processing scheme data; and carrying out transportation-junction point temperature control correction according to the abnormal temperature control processing scheme data, and carrying out cold chain quality weighted scoring to generate logistics quality scoring data. According to the invention, through cold chain task decomposition and cooperation of multiple transportation nodes, intelligent cold chain logistics management is realized, and the temperature control management efficiency and cargo quality guarantee of cold chain logistics are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a smart logistics platform management system and method based on multi-source data. Background Art

[0002] Cold chain logistics refers to a special mode of transportation that must be completed in a low-temperature environment in order to ensure the quality of goods in transportation, warehousing, loading and unloading. It is mainly used for temperature-sensitive goods such as food, medicine, and chemical products. The current cold chain logistics management model still has obvious divisions in warehousing, distribution, and transportation, and the coordination between the various links is insufficient. Especially in the multi-node handover process, temperature control information is often unable to be transmitted and executed in a timely and accurate manner, resulting in temperature out of control and temperature control chain break. This temperature control chain break problem is particularly prone to occur at key nodes such as transportation and warehousing, and the handover points of different transportation links. For example, when goods are transferred from warehousing to distribution vehicles or from one transportation node to another, temperature monitoring data often cannot be transmitted and shared in real time, or due to the imperfect sensor monitoring system, temperature anomalies cannot be discovered and handled in time, resulting in a decline in the quality of goods or even damage. However, traditional cold chain logistics platform management methods usually rely on single-point temperature monitoring and manual inspections, and cannot achieve real-time and global monitoring of the entire logistics chain. Temperature monitoring is mainly concentrated in a single transportation link or storage facility, lacking the ability to interconnect temperature control data throughout the entire supply chain, and unable to effectively deal with temperature control instability problems caused by multi-link and multi-node handovers. Summary of the invention

[0003] Based on this, the present invention provides a smart logistics platform management system and method based on multi-source data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a smart logistics platform management method based on multi-source data includes the following steps:

[0005] Step S1: Acquire cold chain logistics order data; perform order characteristic analysis on the cold chain logistics order data, and process temperature control requirements of multiple types of goods to generate temperature control demand data for goods;

[0006] Step S2: construct a temperature and humidity sensor monitoring network according to the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring according to the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set a processing plan to generate abnormal temperature control processing plan data;

[0007] Step S3: Correct the temperature control at the transport-handover point according to the abnormal temperature control processing plan data, and execute the real-time temperature control adjustment instruction to generate corrected cold chain temperature control data;

[0008] Step S4: Integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data.

[0009] The present invention realizes full-chain coverage of the temperature and humidity monitoring system by constructing a temperature and humidity sensor monitoring network, so that each logistics node and transportation link has real-time monitoring capabilities. This networked monitoring mode significantly improves the timeliness and accuracy of data collection. Through task decomposition and multi-node collaboration, the temperature control demand tasks for each logistics node are accurately formulated, making the temperature monitoring of the entire cold chain logistics process more systematic and coordinated. The multi-source integration of real-time monitoring data, combined with abnormal feature analysis technology, can not only quickly discover temperature control anomalies, but also make targeted corrections according to the generated processing plan, significantly reducing the risk of temperature control chain breakage. The temperature control correction function of the transportation and handover point effectively ensures the temperature control stability of key nodes by adjusting the abnormal temperature control in real time, overcomes the problem that the traditional manual inspection method cannot respond in time, and thus significantly improves the temperature control reliability of the logistics chain. Through the integration of cold chain transportation data and the introduction of the cold chain quality scoring model, the quantification and feedback of the quality of the entire logistics process are realized, which is convenient for the platform to optimize management decisions in real time. This data interconnection capability throughout the entire cold chain logistics supply chain not only improves the efficiency of temperature control management, but also provides reliable technical support for the collaborative optimization between different links, comprehensively improves the overall quality of cold chain logistics services, reduces the risk of damage to goods due to improper temperature control, and ultimately achieves the goal of intelligent, full-chain cold chain logistics management. Therefore, a smart logistics platform management method based on multi-source data of the present invention collects temperature control demand data of goods in real time by deploying temperature and humidity sensors at each node of cold chain logistics, and monitors temperature control of the entire logistics process. For different types of goods, it can automatically analyze their temperature control requirements, generate corresponding temperature control tasks, and adjust temperature control according to environmental changes when multiple nodes are handed over to ensure that temperature control will not be broken in any link. In the event of abnormal temperature, an early warning is issued in time and temperature control correction measures are implemented to maximize the quality of goods.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtain cold chain logistics order data;

[0012] Step S12: performing order characteristic analysis on the cold chain logistics order data to generate cold chain logistics order characteristic data;

[0013] Step S13: searching and matching the cold chain logistics order feature data with the preset cargo information database to generate cold chain cargo matching data;

[0014] Step S14: Evaluate the transportation time according to the cold chain logistics order characteristic data and generate logistics timeliness requirement data;

[0015] Step S15: Extract the temperature and humidity adaptability ranges of multiple types of goods based on the cold chain goods matching data and the logistics timeliness requirement data, perform temperature control demand processing, and generate goods temperature control demand data.

[0016] By acquiring and parsing cold chain logistics order data, the present invention can accurately extract order features and provide efficient data support for further matching of cargo information. Subsequently, through matching and retrieval of the cargo information database, it is ensured that the characteristics of the cold chain cargo accurately correspond to the order information, avoiding the problems of logistics link disorder or cargo temperature control errors caused by data mismatch. At the same time, the evaluation of transportation time provides clear timeliness requirements for logistics planning, effectively avoiding the risk of cargo quality degradation caused by transportation delays. Finally, by integrating cargo characteristics and timeliness requirements, extracting the temperature and humidity adaptation range of the cargo and generating specific temperature control requirements, it not only ensures the environmental adaptability of different types of cargo during transportation, but also improves the scientificity and refined management level of the entire cold chain logistics chain.

[0017] Preferably, step S15 comprises the following steps:

[0018] Step S151: performing cargo clustering analysis on the cold chain cargo matching data to generate cargo classification label data;

[0019] Step S152: extracting cold chain logistics and transportation influencing features from cold chain goods matching data based on goods classification label data to generate goods transportation sensitive feature data;

[0020] Step S153: Calculate the temperature and humidity sensitivity according to the sensitive characteristic data of cargo transportation to generate cargo temperature and humidity sensitivity data;

[0021] Step S154: Processing the cargo transportation sensitive characteristic data according to the temperature and humidity adaptation range of multiple types of cargoes through the cargo temperature and humidity sensitivity data to obtain cargo temperature and humidity range requirement data;

[0022] Step S155: Use the logistics timeliness requirement data to associate the cargo temperature and humidity range requirement data with the transportation distance, and perform temperature and humidity distance control range correction to obtain cargo temperature control requirement data.

[0023] The present invention generates cargo classification label data by clustering analysis of cold chain cargo matching data, so that the system can distinguish different types of cargo, avoid the "one-size-fits-all" extensive management method, extract cargo transportation sensitive features based on cargo classification label data, and calculate temperature and humidity sensitivity, so that the system can more accurately grasp the temperature and humidity sensitivity characteristics of different types of cargo. For example, for biological products that are extremely sensitive to temperature changes, the system will identify them as highly sensitive goods and formulate more stringent temperature control strategies; while for certain daily necessities with strong tolerance, a relatively loose temperature control scheme can be adopted, thereby reducing energy consumption and costs while ensuring the safety of the goods. By associating the logistics timeliness requirement data with the cargo temperature and humidity range requirement data, and correcting the temperature and humidity distance control range, the dynamic adjustment of the temperature control scheme is achieved. This means that even for the same type of cargo, under different transportation distances and timeliness requirements, the system will formulate different temperature control schemes to ensure that the cargo is always in the best temperature and humidity environment throughout the transportation process. For example, for goods transported over long distances, the system will take into account factors such as changes in ambient temperature during transportation and the goods' own respiration, and make more precise adjustments to the temperature control range to prevent the goods from deteriorating or being damaged.

[0024] Preferably, step S2 comprises the following steps:

[0025] Step S21: Screen the vehicle-mounted temperature control equipment according to the cargo temperature control demand data, match the cold chain transportation vehicles with the cold chain logistics order data, and generate order cold chain transportation equipment data;

[0026] Step S22: deploying a warehouse monitoring sensor array based on the order cold chain transportation equipment data, and performing wireless Internet of Things processing to obtain a temperature and humidity sensor monitoring network;

[0027] Step S23: Decomposing the cold chain logistics order data into multi-node temperature control tasks based on the cargo temperature control demand data to generate multi-node fine temperature control task data;

[0028] Step S24: Perform real-time logistics cold chain multi-source monitoring based on the temperature and humidity sensor monitoring network to generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through multi-node fine temperature control task data, set a processing plan, and generate abnormal temperature control processing plan data.

[0029] The present invention screens on-board temperature control equipment and matches cold chain transport vehicles according to the temperature control demand data of the goods, ensuring the precise matching of the transport equipment with the temperature control demand of the goods, avoiding the risk of temperature control failure caused by improper equipment selection, and ensuring the quality of the goods from the source. Based on the order cold chain transport equipment data, the warehouse monitoring sensor array is deployed, and combined with the wireless Internet of Things technology, a temperature and humidity sensor monitoring network covering the entire warehouse is constructed. This enables the system to collect temperature and humidity data at various locations in the warehouse in real time, realize comprehensive monitoring of the temperature and humidity status of the goods, and can more timely discover potential temperature control risks compared to the traditional single-point monitoring method. By decomposing the multi-node temperature control tasks of the cold chain logistics orders, multi-node fine temperature control task data is generated. This means that the system can not only monitor the temperature and humidity changes of the entire transportation process, but also carry out fine management of the temperature control tasks of each key node, such as loading, unloading, transshipment and other links, thereby effectively avoiding the temperature control chain break problem that is prone to occur in these links. Through the multi-source monitoring data collected in real time by the temperature and humidity sensor monitoring network, combined with the multi-node fine temperature control task data, the abnormal characteristics of the cold chain are analyzed, and the corresponding processing plan is set to achieve rapid response and effective processing of temperature control abnormalities. Once the system detects that the temperature or humidity exceeds the preset range, it will immediately trigger the early warning mechanism and take corresponding measures according to the preset processing plan, such as automatically adjusting the temperature control equipment parameters, notifying relevant personnel for manual intervention, etc., so as to minimize the temperature control risk.

[0030] Preferably, step S23 includes the following steps:

[0031] Step S231: extracting the shipping place and the receiving place from the cold chain logistics order data to generate order logistics address data;

[0032] Step S232: Preliminary order transportation route planning is performed on the order logistics address data through a preset logistics transportation network to generate preliminary order transportation route data;

[0033] Step S233: evaluating the capacity of the node temperature control facilities according to the preliminary route data of the order transportation, screening the available temperature control nodes for transportation, and generating the capacity data of the temperature control facilities at the transportation nodes;

[0034] Step S234: using the capacity data of the temperature control facilities at the transport nodes as path planning constraints, and performing intelligent transport path selection on the preliminary route data for order transport, to generate intelligent cold chain transport path data;

[0035] Step S235: extracting cargo handover tasks from the intelligent cold chain transport route data to generate cargo handover point task data;

[0036] Step S236: Based on the cargo temperature control demand data, the cargo handover point task data is decomposed into multi-node temperature control tasks to generate multi-node fine temperature control task data.

[0037] The present invention realizes the intelligent selection of transportation paths by performing preliminary route planning on the order logistics address data, and combining the preset logistics transportation network and node temperature control facility capacity evaluation. This can not only shorten the transportation time and reduce the transportation cost, but also ensure that the selected transportation path has the ability to meet the temperature control requirements of the goods, avoiding the risk of temperature control failure caused by improper path selection. In the path planning process, the scheme uses the capacity data of the temperature control facilities of the transportation node as a constraint condition, and performs intelligent transportation path selection, effectively avoiding the transportation of goods to nodes that lack temperature control facilities or temperature control facilities that do not meet the standards, thereby ensuring the temperature control safety of the goods during the entire transportation process. The cargo handover task is extracted from the intelligent cold chain transportation path data, and the multi-node temperature control task is decomposed based on the cargo temperature control demand data, and multi-node fine temperature control task data is generated. This enables the system to carry out refined management of the temperature control tasks of each cargo handover point, such as handover time, temperature control requirements, responsible persons, etc., thereby effectively avoiding the problem of temperature control chain disconnection that is easy to occur during the handover process. By evaluating and screening the capacity of node temperature control facilities, the scheme also helps to optimize resource allocation and improve the utilization rate of temperature control facilities. For example, the system can prioritize goods with higher temperature control requirements to transit at nodes with more complete temperature control facilities based on the temperature control facility capabilities of different nodes, thereby ensuring the quality of the goods to the greatest extent.

[0038] Preferably, step S236 includes the following steps:

[0039] Through the cargo temperature control demand data, the cargo handover point task data is accurately analyzed for the upper and lower limits of temperature control demand, and the detailed temperature control demand data of the handover point is obtained;

[0040] Calculate the dynamic thermal attenuation index of cargo based on the precise temperature control demand data of the handover point and generate the thermal attenuation index of cargo;

[0041] The cargo thermal attenuation index is used to calculate the task temperature fluctuation risk value of the cargo handover task data, and the temperature fluctuation risk tasks are divided based on the preset temperature fluctuation risk threshold to obtain high-risk cargo handover task data and low-risk cargo handover task data respectively;

[0042] Determine the temperature control redundancy level of the transport section for high-risk cargo delivery task data, perform multiple redundant temperature control processing, and generate high-risk temperature control task parameters;

[0043] Calculate the optimal energy-saving temperature range for low-risk cargo delivery task data, perform dynamic energy-saving temperature control processing, and generate low-risk temperature control task parameters;

[0044] The intelligent cold chain transport path data is decomposed into temperature control tasks in the transport section through high-risk temperature control task parameters and low-risk temperature control task parameters, and the temperature control parameter execution task is encapsulated to generate multi-node fine temperature control task data.

[0045] The present invention realizes differentiated management of tasks with different risk levels by calculating the dynamic thermal attenuation index of the cargo and the task temperature fluctuation risk value, and dividing the cargo handover tasks into high-risk and low-risk categories. This risk-based management strategy can allocate resources more effectively and concentrate limited resources on temperature control assurance for high-risk tasks, thereby minimizing the risk of cargo damage. For high-risk cargo handover tasks, the solution determines the temperature control redundancy level of the transport section and performs multiple redundant temperature control processes to ensure that the quality of the goods can be guaranteed by redundant temperature control measures even in unexpected situations. For example, the system can simultaneously enable spare refrigeration equipment, increase insulation materials, etc., thereby improving the reliability of temperature control. For low-risk cargo handover tasks, the solution calculates the optimal energy-saving temperature range and performs dynamic energy-saving temperature control processing, thereby reducing energy consumption as much as possible while ensuring the safety of the goods, thereby saving costs and improving economic benefits. For example, the system can dynamically adjust the operating parameters of the refrigeration equipment according to changes in ambient temperature to avoid energy waste caused by excessive refrigeration.

[0046] Preferably, step S24 includes the following steps:

[0047] Step S241: Distribute remote temperature control tasks based on multi-node fine temperature control task data to generate remote temperature control task data;

[0048] Step S242: acquiring real-time transportation equipment communication quality data; using the real-time transportation equipment communication quality data to adaptively adjust the monitoring frequency of the temperature and humidity sensor monitoring network to obtain an adaptive monitoring frequency strategy;

[0049] Step S243: executing the warehouse temperature control task according to the remote temperature control task data, and using the temperature and humidity sensor monitoring network to perform multi-point timing collection of the monitoring array based on the adaptive monitoring frequency strategy to obtain real-time cargo multi-source monitoring data;

[0050] Step S244: performing data analysis preprocessing on the real-time cargo multi-source monitoring data, and performing multi-source monitoring data fusion to generate standard cargo temperature monitoring data;

[0051] Step S245: intelligently predict the temperature control trend of standard cargo temperature monitoring data, extract cold chain abnormal features based on multi-node fine temperature control task data, and generate temperature control abnormal deviation feature data;

[0052] Step S246: Based on the preset transportation-handover cold chain case database, intelligent matching of historical cases is performed through temperature control abnormal deviation feature data to generate historical related case data;

[0053] Step S247: Set a processing plan based on the historical related case data, and perform temperature control adjustment parameter processing to generate abnormal temperature control processing plan data.

[0054] The present invention realizes remote control and management of temperature control equipment through remote temperature control task distribution, and improves the efficiency and convenience of temperature control operation. The monitoring frequency of the temperature and humidity sensor monitoring network is adaptively adjusted according to the real-time transportation equipment communication quality data, and the dynamic allocation of monitoring resources is realized. When the communication quality is good, the system will reduce the monitoring frequency to save energy and bandwidth resources; when the communication quality is poor, the system will increase the monitoring frequency to ensure the real-time and integrity of the data, so as to better guarantee the quality of goods. By intelligently predicting the temperature control trend of standard cargo temperature monitoring data and extracting cold chain abnormal features in combination with multi-node fine temperature control task data, early warning of potential temperature control risks is realized. This enables the system to take preventive measures before the temperature abnormality occurs to avoid temperature out of control, thereby minimizing the risk of cargo damage. Based on the preset transportation-handover cold chain case database, by intelligently matching historical related cases, setting processing schemes and processing temperature control adjustment parameters, rapid response and precise control of abnormal temperature control processing are realized. This avoids the delay and misjudgment of manual processing of abnormal situations, and improves the efficiency and accuracy of abnormal processing.

[0055] Preferably, step S245 includes the following steps:

[0056] Perform temperature and humidity time series processing on standard cargo temperature monitoring data to generate time series cargo temperature monitoring data;

[0057] Perform monitoring fluctuation characteristic statistics based on the time-series cargo temperature monitoring data to obtain cold chain monitoring fluctuation characteristic data;

[0058] The temperature and humidity prediction model is constructed using the preset time series model, and the cold chain monitoring fluctuation characteristic data is used for transfer learning to obtain the temperature and humidity prediction model;

[0059] Through the temperature and humidity prediction model, the intelligent temperature control trend prediction is carried out on the time-series cargo temperature monitoring data to generate intelligent temperature control trend prediction data;

[0060] Use multi-node fine temperature control task data to match the intelligent temperature control trend prediction data with the transportation task nodes, calculate the ideal temperature control deviation value, and generate cold chain temperature control deviation data;

[0061] Perform cold chain temperature control deviation anomaly analysis based on cold chain temperature control deviation data, obtain the operating status of the temperature control equipment, and generate temperature control abnormal deviation characteristic data.

[0062] The present invention performs temperature and humidity time series processing on the temperature monitoring data of standard goods, converts discrete temperature data into continuous time series data, and extracts cold chain monitoring fluctuation characteristic data by monitoring fluctuation characteristic statistics on the time series temperature monitoring data, which provides an important reference for understanding the law and characteristics of temperature fluctuations and helps to more accurately predict future temperature change trends. A temperature and humidity prediction model is constructed by using a preset time series model and combining the cold chain monitoring fluctuation characteristic data for transfer learning. The application of transfer learning enables the model to better adapt to the temperature and humidity characteristics and transportation environment of different goods, and improves the accuracy and reliability of the prediction. By using the temperature and humidity prediction model to perform intelligent temperature control trend prediction on the time series temperature monitoring data of goods, and combining the multi-node fine temperature control task data to calculate the ideal temperature control deviation value, the temperature deviation that will occur in the future can be predicted in advance, and preventive measures can be taken in time. For example, if it is predicted that the temperature of a certain node is about to exceed the preset range, the system can give an early warning and automatically adjust the operating parameters of the temperature control equipment to avoid temperature out of control. By performing abnormal analysis on the cold chain temperature control deviation data and combining the operating status information of the temperature control equipment, the cause of the temperature control abnormality can be more accurately determined, and more accurate guidance can be provided for subsequent abnormal processing. For example, if the temperature deviation is found to be too large and the temperature control equipment is in normal operation, there is a problem with the insulation material and it needs to be replaced or repaired in time.

[0063] Preferably, step S4 comprises the following steps:

[0064] Step S41: When the cold chain logistics order data completes logistics transportation, the corrected cold chain temperature control data and the multi-node fine temperature control task data are integrated into the cold chain transportation data to generate the cargo cold chain transportation process data;

[0065] Step S42: Evaluate the temperature control effect of the transport section according to the cargo cold chain transport process data, and generate temperature control effect data of the transport section;

[0066] Step S43: Evaluate the status of key handover links based on the cargo cold chain transportation process data to generate handover link evaluation data;

[0067] Step S44: Calculate the cargo quality change index based on the cargo cold chain transportation process data to generate cargo transportation quality index data;

[0068] Step S45: Perform a weighted cold chain quality score based on the temperature control effect data of the transportation section, the handover link evaluation data, and the cargo transportation quality index data to generate logistics quality score data.

[0069] After the cold chain logistics order is completed, the present invention integrates the corrected cold chain temperature control data and the multi-node fine temperature control task data to generate complete cargo cold chain transportation process data, evaluates the temperature control effect of the transportation section, the key handover link status and the cargo quality change index, and generates corresponding evaluation data. The evaluation of these three aspects covers the key links of cold chain logistics and can fully reflect the quality level of cold chain logistics services. For example, the evaluation of the temperature control effect of the transportation section can reflect the operating status and temperature control effect of the temperature control equipment; the evaluation of the handover link status can reflect the operational standardization and efficiency during the handover process; the calculation of the cargo quality change index can reflect the impact of the cold chain logistics service on the cargo quality. Based on the evaluation data of these three aspects, the cold chain quality weighted scoring is performed to generate the final logistics quality scoring data. The weighted scoring method can assign different weights according to the importance of different links, so as to more objectively reflect the overall quality of cold chain logistics services. For example, according to the characteristics of the goods and the requirements for temperature control, a higher weight can be assigned to the cargo quality change index, so as to more effectively ensure the quality of the goods. The generated logistics quality scoring data can not only be used as a performance evaluation indicator within cold chain logistics companies, but also as an important reference for customers to select cold chain logistics service providers.

[0070] The present invention also provides a smart logistics platform management system based on multi-source data, which executes the smart logistics platform management method based on multi-source data as described above. The smart logistics platform management system based on multi-source data includes:

[0071] The order demand analysis module is used to obtain cold chain logistics order data; analyze the order characteristics of cold chain logistics order data, process the temperature control requirements of multiple types of goods, and generate temperature control demand data for goods;

[0072] The temperature control anomaly prediction module is used to build a temperature and humidity sensor monitoring network based on the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring based on the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain anomaly feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set the processing plan to generate abnormal temperature control processing plan data;

[0073] The temperature control execution module is used to perform temperature control correction at the transport-handover point according to the abnormal temperature control processing plan data, and to execute real-time temperature control adjustment instructions to generate corrected cold chain temperature control data;

[0074] The logistics quality assessment module is used to integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of the steps of the smart logistics platform management method based on multi-source data of the present invention;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0077] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0078] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3The present invention provides a smart logistics platform management method based on multi-source data, comprising the following steps:

[0083] Step S1: Acquire cold chain logistics order data; perform order characteristic analysis on the cold chain logistics order data, and process temperature control requirements of multiple types of goods to generate temperature control demand data for goods;

[0084] Step S2: construct a temperature and humidity sensor monitoring network according to the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring according to the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set a processing plan to generate abnormal temperature control processing plan data;

[0085] Step S3: Correct the temperature control at the transport-handover point according to the abnormal temperature control processing plan data, and execute the real-time temperature control adjustment instruction to generate corrected cold chain temperature control data;

[0086] Step S4: Integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data.

[0087] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow chart of a smart logistics platform management method based on multi-source data of the present invention. In this embodiment, the smart logistics platform management method based on multi-source data includes the following steps:

[0088] Step S1: Acquire cold chain logistics order data; perform order characteristic analysis on the cold chain logistics order data, and process temperature control requirements of multiple types of goods to generate temperature control demand data for goods;

[0089] In an embodiment of the present invention, for example, a fresh food e-commerce platform needs to deliver a batch of frozen seafood (-18°C), refrigerated fruits (0-4°C) and room temperature vegetables (10-25°C) from Shanghai to Beijing. First, the platform's order management system automatically obtains the order data, including: order number, customer information, type of goods, quantity, specification, origin, destination, expected delivery time, etc. The system performs characteristic analysis on the order data. Using the preset goods database, the system identifies that "frozen seafood", "refrigerated fruits" and "room temperature vegetables" belong to goods of different temperature control types, and automatically extracts their corresponding ideal storage temperature ranges. At the same time, the system also analyzes other information in the order, such as: climatic conditions at the origin and destination, transportation distance, mode of transportation (for example: road transportation, rail transportation or air transportation) and expected transportation time, etc., which will be used for subsequent temperature control strategy formulation. For example: the cargo temperature control demand data of the order includes: frozen seafood: -18±2°C, full temperature and humidity monitoring, and a real-time alarm mechanism is required. Refrigerated fruits: 0-4℃, need to maintain a certain humidity and avoid large temperature fluctuations. Normal temperature vegetables: 10-25℃, need good ventilation and avoid direct sunlight.

[0090] Step S2: construct a temperature and humidity sensor monitoring network according to the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring according to the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set a processing plan to generate abnormal temperature control processing plan data;

[0091] In the embodiment of the present invention, according to the temperature control demand data of the goods generated in the previous step, the system deploys temperature and humidity sensors in places such as refrigerated trucks and cold storages to build a temperature and humidity sensor monitoring network. For example, multiple temperature sensors are installed at different locations in the refrigerated compartment to ensure that the temperature distribution in the compartment can be fully monitored. At the same time, these sensors are connected to the Internet of Things platform to realize real-time data collection and transmission. According to the temperature control demand data and order information of the goods, the system decomposes the entire cold chain logistics process into multiple nodes, such as warehousing, loading, transportation, unloading, distribution, etc., and sets specific temperature control tasks for each node. For example, at the "loading" node, the system will set the temperature range of the loading area, loading time limit, etc. according to the temperature control requirements of different goods; at the "transportation" node, the system will dynamically adjust the temperature setting value of the refrigerated truck according to factors such as the transportation route and weather conditions. These detailed temperature control task data will guide the operation of each link to ensure the accuracy and effectiveness of temperature control. Through the deployed temperature and humidity sensor monitoring network, the system collects multi-source data in the cold chain logistics process in real time, including: temperature, humidity, GPS location, vehicle vibration, number of door openings, etc. These data are transmitted to the cloud platform via wireless networks to generate real-time cargo multi-source monitoring data. The system compares the real-time cargo multi-source monitoring data with the multi-node fine temperature control task data to analyze whether there are abnormal cold chain characteristics such as abnormal temperature, abnormal humidity, position deviation, and excessive vibration. For example, if the system finds that the temperature of the refrigerated truck exceeds the preset threshold during transportation, it will immediately trigger an alarm and push the alarm information to relevant personnel. At the same time, the system will automatically generate abnormal temperature control processing plan data based on the preset processing plan. For example, the system recommends immediately lowering the temperature setting value of the refrigerated truck, or contacting the nearest maintenance station for maintenance.

[0092] Step S3: Correct the temperature control at the transport-handover point according to the abnormal temperature control processing plan data, and execute the real-time temperature control adjustment instruction to generate corrected cold chain temperature control data;

[0093] In the embodiment of the present invention, in step S2, the system has generated temperature control processing scheme data for specific abnormal situations. This step performs actual temperature control correction operations based on these data. For example, the system detects that the temperature of the refrigerated compartment is too high, and the abnormal temperature control processing scheme data recommends lowering the temperature setting value of the refrigerated truck. The system will automatically send instructions to the temperature control system of the refrigerated truck to lower the set temperature. At the same time, the system can also send an alarm message to the driver to remind the driver to pay attention to temperature changes and take necessary measures. At the transportation-handover point, such as the process of unloading goods from the refrigerated truck to the cold storage, the system will control the temperature and humidity of the handover area according to the preset temperature control scheme, and shorten the handover time to minimize the time the goods are exposed to non-ideal temperature environments. For example, the system can control the pre-cooling of the unloading platform of the cold storage to maintain a temperature similar to that in the refrigerated compartment, thereby reducing the impact of the temperature difference on the goods. At the same time, the system will monitor the temperature changes of the goods during the handover process in real time, record the data, and generate corrected cold chain temperature control data. These data include: the corrected temperature setting value, the actual temperature change curve, the handover time, etc.

[0094] Step S4: Integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data.

[0095] In an embodiment of the present invention, the system integrates the corrected cold chain temperature control data generated in step S3 with the multi-node fine temperature control task data generated in step S2 to generate complete cargo cold chain transportation process data. These data cover all links of the entire cold chain logistics process, including: order information: cargo type, quantity, origin, destination, etc. Temperature control requirements: ideal temperature range and humidity requirements for different cargoes. Multi-node temperature control tasks: specific temperature control targets and operating specifications for each node. Real-time monitoring data: real-time data such as temperature, humidity, location, and vibration. Abnormal processing records: time of occurrence of abnormal situations, processing solutions, and processing results. Corrected temperature control data: corrected temperature setting values, actual temperature change curves, etc. Based on these complete cargo cold chain transportation process data, the system can comprehensively evaluate the quality of cold chain logistics. The system will score the temperature control performance of each link according to the pre-set weighted scoring rules. For example, the temperature fluctuation amplitude, duration, number of abnormalities, etc. will affect the final score. The weights of different links will also be different. For example, the weight of the "transportation" link is usually higher than that of the "loading" link. Finally, the system will generate a comprehensive logistics quality score data, such as: 95 points (excellent), 80 points (good), 60 points (pass), etc. This score can be used to evaluate the service quality of the carrier and can also be used as a basis for improving cold chain logistics management.

[0096] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S1 in the flowchart, in this example, step S1 includes:

[0097] Step S11: Obtain cold chain logistics order data;

[0098] In an embodiment of the present invention, consumers place orders on the platform to purchase frozen seafood, refrigerated fruits and room temperature vegetables. After the order is generated, the order management system of the platform will automatically generate order data and transmit the data to the smart logistics platform in real time through a preset API interface. The acquired order data includes order number, customer information (delivery address, contact information, etc.), goods information (name, quantity, specification, packaging method, etc.), warehouse number of origin, distribution center number of destination, expected delivery time, etc. The system will store these data in a distributed database and perform data cleaning and preprocessing, such as removing duplicate data, processing missing values, verifying data format, etc., to ensure the accuracy and integrity of the data. In order to ensure data security, the system will encrypt the data transmission process, such as using the HTTPS protocol, and perform access control management on the database, such as setting user permissions, data encryption storage, etc. In addition, the system will also record the timestamp of data acquisition to track the entire life cycle of the order and perform data analysis, such as counting order volume and analyzing order trends.

[0099] Step S12: performing order characteristic analysis on the cold chain logistics order data to generate cold chain logistics order characteristic data;

[0100] In an embodiment of the present invention, the system analyzes and resolves the order data obtained in step S11, extracts key feature information, and generates cold chain logistics order feature data. For example, the system will call the map API to calculate the transportation distance according to the warehouse number of the origin and the distribution center number of the destination of the order, and calculate the required average transportation speed according to the expected delivery time. The system will also calculate the required transportation space and load according to the type, quantity and packaging method of the goods, so as to select a suitable transportation vehicle (for example: refrigerated truck, insulated truck) and arrange the best transportation route. In addition, the system will call the weather API to obtain real-time weather data of the origin, destination and route along the way and weather forecasts for the next few days, such as temperature, humidity, rainfall, whether there is extreme weather, etc., so as to provide a reference for the subsequent temperature control strategy formulation. The order feature data also includes some special transportation requirements, such as whether full GPS tracking is required, whether real-time temperature and humidity monitoring is required, whether specific packaging materials are required (for example: insulated box, ice bag), etc. These feature data will be stored in a structured form in the database, such as using JSON format, so that subsequent steps can access and use them efficiently.

[0101] Step S13: searching and matching the cold chain logistics order feature data with the preset cargo information database to generate cold chain cargo matching data;

[0102] In an embodiment of the present invention, the system presets a cargo information database, which contains detailed information of various common fresh products, such as various fish, meat, fruits, vegetables, etc. The information of each cargo includes: name, type, optimal storage temperature range, humidity requirements, shelf life, sensitivity to light, vibration, optimal packaging method, etc. The system matches and searches the cargo name (e.g., salmon, apple, spinach) in the cold chain logistics order feature data generated in step S12 with the cargo information database. For example, if the order contains "Salmon", the system will search for "Salmon" in the database and extract the product's optimal storage temperature range (e.g., -18°C to -20°C), optimal humidity requirements, sensitivity to light (e.g., avoid light), shelf life, etc. The matching result will generate cold chain cargo matching data, which contains detailed information of each cargo in the order. If the order contains multiple cargoes, the system will perform a matching search for each cargo. If the system cannot find matching cargo information in the database, it will issue an alarm to prompt manual intervention, and manually add the unmatched cargo information to the database for subsequent order processing.

[0103] Step S14: Evaluate the transportation time according to the cold chain logistics order characteristic data and generate logistics timeliness requirement data;

[0104] In the embodiment of the present invention, the system evaluates the time required for transportation based on the cold chain logistics order characteristic data generated in step S12, especially the transportation distance, transportation mode (e.g., road transportation), the origin warehouse, the destination distribution center, and the real-time traffic information along the route. The system can call the map API to obtain real-time traffic information, such as traffic congestion, road construction, traffic accidents, etc., and analyze and predict the transportation time in combination with historical traffic data and prediction models. For example, assuming that the transportation distance is 1,300 kilometers and the average transportation speed is 80 kilometers per hour, the initial estimated transportation time is 16.25 hours. Taking into account factors such as rest, loading and unloading, traffic congestion, and weather changes, the system will add a certain buffer time according to the preset algorithms and rules, such as adding 3.75 hours, and the logistics time requirement data finally generated is 20 hours. In addition, the system will also make adjustments according to the customer's expected delivery time. For example, if the customer requires delivery the next morning, the system will reversely calculate the transportation time based on the delivery time, and use this as the logistics time requirement data, and optimize the transportation route and arrange the transportation plan based on the time requirement data.

[0105] Step S15: Extract the temperature and humidity adaptability ranges of multiple types of goods based on the cold chain goods matching data and the logistics timeliness requirement data, perform temperature control demand processing, and generate goods temperature control demand data.

[0106] In the embodiment of the present invention, the system combines the cold chain cargo matching data generated in step S13 (for example, the optimal storage temperature of salmon is -18°C 4°C, the optimal storage temperature of spinach is 10°C-17°C, and requires to maintain low humidity throughout the process to prevent frost and bacterial growth. For apples, the system will set the temperature control range to 1°C 23°C, and require ventilation to prevent rotting and deterioration. The cargo temperature control demand data finally generated will include the specific temperature control range, humidity control requirements, and other special requirements for each cargo, such as light protection, shockproof, ventilation, etc. These data will serve as an important reference in the subsequent cold chain logistics execution process, guide the setting and operation of temperature control equipment, such as setting the temperature of refrigerated trucks, controlling the humidity of cold storage, etc., to ensure that the goods maintain the best quality during transportation, reduce losses, and improve customer satisfaction.

[0107] Preferably, step S15 comprises the following steps:

[0108] Step S151: performing cargo clustering analysis on the cold chain cargo matching data to generate cargo classification label data;

[0109] Step S152: extracting cold chain logistics and transportation influencing features from cold chain goods matching data based on goods classification label data to generate goods transportation sensitive feature data;

[0110] Step S153: Calculate the temperature and humidity sensitivity according to the sensitive characteristic data of cargo transportation to generate cargo temperature and humidity sensitivity data;

[0111] Step S154: Processing the cargo transportation sensitive characteristic data according to the temperature and humidity adaptation range of multiple types of cargoes through the cargo temperature and humidity sensitivity data to obtain cargo temperature and humidity range requirement data;

[0112] Step S155: Use the logistics timeliness requirement data to associate the cargo temperature and humidity range requirement data with the transportation distance, and perform temperature and humidity distance control range correction to obtain cargo temperature control requirement data.

[0113] In an embodiment of the present invention, the order on the fresh food e-commerce platform includes a variety of goods, such as salmon, tuna, beef, apples, bananas, spinach, and potatoes. The system uses the K-Means clustering algorithm to perform cluster analysis on the cold chain goods matching data (including the optimal storage temperature, humidity, shelf life, and other information of the goods). According to the similarity of the goods, these goods are divided into different categories, such as frozen aquatic products, refrigerated meat, refrigerated fruits, and room temperature vegetables. The system assigns a unique label to each category, such as A, B, C, and D, and generates goods classification label data to record the category label to which each goods belongs. According to the goods classification label data generated in step S151, the system extracts different transportation sensitive features for different categories of goods. For example, for frozen aquatic products (label A), the focus is on the impact of temperature fluctuations and storage time on product freshness, and features such as temperature sensitivity coefficient and time sensitivity coefficient are extracted. For refrigerated fruits (label C), the focus is on the impact of humidity changes on product quality, and features such as humidity sensitivity coefficient and ethylene release rate are extracted. Finally, the goods transportation sensitive feature data is generated, including the specific sensitive features and corresponding values ​​of each type of goods. The system calculates the temperature and humidity sensitivity of each type of goods based on the sensitive characteristic data of cargo transportation extracted in step S152. For example, for frozen aquatic products, the quality changes of this type of goods under different temperature fluctuations and storage times are calculated based on the temperature sensitivity coefficient and the time sensitivity coefficient, and quantified as a temperature and humidity sensitivity score. The higher the score, the more sensitive the goods are to temperature and humidity changes. The system processes the temperature and humidity adaptation range of different types of goods based on the cargo temperature and humidity sensitivity data calculated in step S153. For example, for highly sensitive frozen aquatic products, the system will narrow its temperature and humidity adaptation range, such as the optimal storage temperature range from -18°C to 19.5°C, to reduce the impact of temperature fluctuations on product quality. Finally, the cargo temperature and humidity range demand data is obtained, and the system associates the cargo temperature and humidity range demand data obtained in step S154 with the logistics timeliness requirement data (for example: transportation time 20 hours). Considering that the longer the transportation distance, the greater the difficulty of temperature and humidity control, the system will correct the temperature and humidity range demand data according to the transportation distance. For example, for frozen seafood shipped over long distances, the system will further narrow the temperature control range and increase the humidity monitoring frequency to ensure that the product maintains the best quality during long-distance transportation. The final result is the cargo temperature control demand data, which includes the temperature and humidity control requirements for each cargo under specific transportation distance and time.

[0114] Preferably, step S2 comprises the following steps:

[0115] Step S21: Screen the vehicle-mounted temperature control equipment according to the cargo temperature control demand data, match the cold chain transportation vehicles with the cold chain logistics order data, and generate order cold chain transportation equipment data;

[0116] Step S22: deploying a warehouse monitoring sensor array based on the order cold chain transportation equipment data, and performing wireless Internet of Things processing to obtain a temperature and humidity sensor monitoring network;

[0117] Step S23: Decomposing the cold chain logistics order data into multi-node temperature control tasks based on the cargo temperature control demand data to generate multi-node fine temperature control task data;

[0118] Step S24: Perform real-time logistics cold chain multi-source monitoring based on the temperature and humidity sensor monitoring network to generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through multi-node fine temperature control task data, set a processing plan, and generate abnormal temperature control processing plan data.

[0119] In an embodiment of the present invention, for example, a fresh food order includes fish that needs to be frozen at -18°C, fruits that need to be refrigerated at 0°C, and vegetables that need to be stored at 10-25°C. Based on the temperature control demand data of the goods, the system selects on-board temperature control equipment with multi-temperature zone control functions, such as a refrigerated truck with an independent temperature control unit. Then, the system matches a suitable refrigerated vehicle from the available vehicle database based on the total amount of goods in the order, the number of temperature control intervals required, and the transportation distance, such as a large refrigerated truck with three independent temperature control units. Finally, the order cold chain transportation equipment data is generated, which contains the information of the selected vehicle and the configuration parameters of the on-board temperature control equipment. According to the refrigerated truck and temperature control equipment determined by S21, a temperature and humidity sensor array is deployed in different temperature zones of the car, for example, multiple sensors are arranged in the freezing zone, the refrigerated zone, and the normal temperature zone to ensure that the temperature and humidity of each temperature zone can be monitored in real time. Each sensor has a unique identifier and uploads data to the cloud platform in real time through a wireless network (e.g., 4G / 5G, NB-IoT). The system integrates the data of all sensors to build a complete temperature and humidity sensor monitoring network to achieve comprehensive coverage and real-time monitoring of temperature and humidity data in the carriage. The transportation process of a fresh food order includes multiple nodes: warehouse loading, transportation, distribution center unloading, and last-mile delivery. The system refines the temperature control tasks of each node according to the temperature control demand data of the goods (for example: fish -18℃, fruit 0℃, vegetables 10-25℃). For example, during the warehouse loading stage, it is required to load the goods within the specified time and pre-cool the carriage to the target temperature; during transportation, it is required to monitor the temperature in real time and dynamically adjust the temperature control parameters according to the real-time road conditions and weather conditions; during the unloading stage of the distribution center, it is required to unload quickly and keep the temperature stable. Finally, multi-node fine temperature control task data is generated, including specific temperature control requirements, operating specifications and time limits for each node. The temperature and humidity sensor monitoring network collects temperature and humidity data of each temperature zone in the carriage in real time, as well as information such as vehicle position, vibration, door switch status, etc., and uploads them to the cloud platform to generate real-time cargo multi-source monitoring data. The system compares and analyzes these data with the multi-node fine temperature control task data generated by S23. For example, if the temperature in the freezing area exceeds -16°C, the system will judge it as a temperature abnormality and generate abnormal temperature control processing plan data according to the preset rules, such as: immediately issuing an alarm to notify relevant personnel, automatically adjusting the refrigeration power of the refrigeration unit, and recording the abnormal situation in the database for subsequent analysis and improvement. The system will also provide different processing plan suggestions based on the type of abnormality, severity, and current node. For example: if the abnormality occurs during transportation and the temperature deviation is large, it is recommended to find a nearby maintenance point for inspection.

[0120] Preferably, step S23 includes the following steps:

[0121] Step S231: extracting the shipping place and the receiving place from the cold chain logistics order data to generate order logistics address data;

[0122] Step S232: Preliminary order transportation route planning is performed on the order logistics address data through a preset logistics transportation network to generate preliminary order transportation route data;

[0123] Step S233: evaluating the capacity of the node temperature control facilities according to the preliminary route data of the order transportation, screening the available temperature control nodes for transportation, and generating the capacity data of the temperature control facilities at the transportation nodes;

[0124] Step S234: using the capacity data of the temperature control facilities at the transport nodes as path planning constraints, and performing intelligent transport path selection on the preliminary route data for order transport, to generate intelligent cold chain transport path data;

[0125] Step S235: extracting cargo handover tasks from the intelligent cold chain transport route data to generate cargo handover point task data;

[0126] Step S236: Based on the cargo temperature control demand data, the cargo handover point task data is decomposed into multi-node temperature control tasks to generate multi-node fine temperature control task data.

[0127] In an embodiment of the present invention, information such as the warehouse address of the place of shipment and the customer address of the place of receipt are extracted from the cold chain logistics order data obtained from the fresh food e-commerce platform. The system performs standardized processing on the address information, such as converting the text address into longitude and latitude coordinates, and generates order logistics address data. The order logistics address data contains information such as order ID, longitude and latitude of the place of shipment, longitude and latitude of the place of receipt, etc. The system presets a logistics transportation network including various modes of transportation (e.g., road, rail, aviation) and nodes (e.g., warehouses, distribution centers, transfer stations). According to the information of the place of shipment and the place of receipt in the order logistics address data, the system uses a path planning algorithm (e.g., Dijkstra algorithm, A* algorithm) to search for transportation routes in the logistics transportation network, and generates multiple preliminary route data for order transportation. Each preliminary route data contains a series of node and section information, such as warehouses, distribution centers, transfer stations passed through, and the distance between them, estimated transportation time, etc. The system evaluates the temperature control facility capacity based on the information of each node in the preliminary route data for order transportation. For example, the database is queried to obtain information such as the cold storage capacity, temperature control equipment type, and temperature range of each warehouse, distribution center, and transfer station. If an order contains goods that need to be frozen at -18°C, and a transfer station only has a refrigeration capacity of 0-4°C, the node will be marked as not meeting the requirements. The system screens out nodes that meet the temperature control requirements of the ordered goods and generates the temperature control facility capacity data of the transport node. The system uses the temperature control facility capacity data of the transport node generated in step S233 as a constraint condition to screen and optimize the preliminary route data for order transportation generated in step S232. For example, the route where the node does not have the required temperature control capacity is located is excluded. The system also considers other factors, such as transportation cost, transportation time, transportation risk, etc., and uses a multi-objective optimization algorithm to select the best intelligent cold chain transportation route from the remaining routes. The intelligent cold chain transportation route data finally generated contains detailed information on the finally selected transportation route, such as the nodes, sections passed, estimated transportation time, transportation cost, etc. According to the intelligent cold chain transportation route data, the handover tasks of goods between different nodes are extracted. For example, the goods need to be transported from the origin warehouse to a transfer station, and then from the transfer station to the destination distribution center. The system will generate corresponding cargo handover point task data for each handover task, which includes the handover location, handover time, handover cargo information, and transportation tools involved in the handover. The system decomposes and refines the temperature control tasks of each handover point based on the cargo temperature control demand data (for example: fish -18℃, fruit 0℃, vegetables 10-25℃) and cargo handover point task data. For example, in the warehouse loading link, different loading areas and loading sequences need to be set according to the temperature control requirements of different goods, and the temperature and humidity of the loading area need to be controlled. In the handover link at the transfer station, the temperature of the handover area needs to be controlled, and the handover time needs to be shortened as much as possible to reduce the risk of goods being exposed to an undesirable environment.Finally, multi-node fine temperature control task data is generated, including specific temperature control requirements, operating specifications, time limits and other information for each handover point.

[0128] Preferably, step S236 includes the following steps:

[0129] Through the cargo temperature control demand data, the cargo handover point task data is accurately analyzed for the upper and lower limits of temperature control demand, and the detailed temperature control demand data of the handover point is obtained;

[0130] Calculate the dynamic thermal attenuation index of cargo based on the precise temperature control demand data of the handover point and generate the thermal attenuation index of cargo;

[0131] The cargo thermal attenuation index is used to calculate the task temperature fluctuation risk value of the cargo handover task data, and the temperature fluctuation risk tasks are divided based on the preset temperature fluctuation risk threshold to obtain high-risk cargo handover task data and low-risk cargo handover task data respectively;

[0132] Determine the temperature control redundancy level of the transport section for high-risk cargo delivery task data, perform multiple redundant temperature control processing, and generate high-risk temperature control task parameters;

[0133] Calculate the optimal energy-saving temperature range for low-risk cargo delivery task data, perform dynamic energy-saving temperature control processing, and generate low-risk temperature control task parameters;

[0134] The intelligent cold chain transport path data is decomposed into temperature control tasks in the transport section through high-risk temperature control task parameters and low-risk temperature control task parameters, and the temperature control parameter execution task is encapsulated to generate multi-node fine temperature control task data.

[0135] In an embodiment of the present invention, a batch of frozen seafood (-18 ± 2 ° C) and refrigerated fruits (0-4 ° C) are taken as an example, and the goods are handed over at the transfer station. The system analyzes the temperature fluctuation range allowed during the handover process according to the temperature control demand data of the goods. Taking into account the influence of loading and unloading operations and ambient temperature, the system will refine the temperature control requirements, for example, the temperature control range of frozen seafood is set to -19 ° C ~ -16 ° C, and the temperature control range of refrigerated fruits is set to 1-3 ° C, and more stringent handover point fine temperature control demand data is generated to ensure that the temperature of the goods remains within a safe range during the handover process. Based on the fine temperature control demand data of the handover point, as well as the physical properties of the goods (such as specific heat capacity, thermal conductivity, etc.) and the thermal insulation performance of the packaging material, the thermal attenuation index of the goods at different ambient temperatures is calculated. For example, for salmon, according to its specific heat capacity, the thermal insulation performance of the packaging material and the fine temperature control demand data (-1-5 ° C) at the transfer station, its thermal attenuation index at different ambient temperatures (such as 10 ° C, 20 ° C, 30 ° C) is calculated. The thermal decay index reflects the rate at which the temperature of the cargo changes over time and is used to assess the risk of temperature changes in the cargo during the handover process. The thermal decay index of the cargo, the ambient temperature at the handover point, and the handover time are combined to calculate the temperature fluctuation risk value of each handover task. For example, the temperature fluctuation risk value of the handover task is calculated based on the thermal decay index of salmon, the ambient temperature of the transfer station (e.g., 25°C), and the expected handover time (e.g., 30 minutes). A temperature fluctuation risk threshold is preset, such as 0.8. Tasks with risk values ​​higher than the threshold are classified as high-risk cargo handover tasks, and tasks with risk values ​​lower than the threshold are classified as low-risk cargo handover tasks. For high-risk cargo handover tasks, such as the summer transfer station handover of frozen seafood, the system will increase the temperature control redundancy level. For example, enable the backup refrigeration unit, increase the insulation material, shorten the handover time, etc. The system will generate high-risk temperature control task parameters based on the risk level and the preset redundancy strategy, such as: the set temperature of the main and backup refrigeration units, the handover time limit, the type and quantity of insulation materials, etc. For low-risk cargo handover tasks, such as the handover of refrigerated fruits in spring and autumn distribution centers, the system will give priority to energy conservation. The system will calculate the optimal energy-saving temperature range based on the temperature control requirements of the goods and the ambient temperature, and dynamically adjust the operating parameters of the temperature control equipment within this range, such as reducing the power of the refrigeration unit to reduce energy consumption. Finally, low-risk temperature control task parameters are generated, such as: power setting range of refrigeration units, temperature adjustment frequency, etc. According to the intelligent cold chain transportation path data, the entire transportation process is decomposed into multiple transportation segments, such as: warehouse to transfer station, transfer station to distribution center. The system applies the corresponding high-risk temperature control task parameters or low-risk temperature control task parameters according to the risk level of the handover tasks contained in each transportation segment.For example, for high-risk transport sections that include frozen seafood transfer stations in summer, high-risk temperature control task parameters are applied to start the standby refrigeration unit; for low-risk transport sections that only include refrigerated fruit transfer centers in spring and autumn, low-risk temperature control task parameters are applied to perform energy-saving temperature control. The system encapsulates the temperature control parameters and execution tasks of each transport section to generate multi-node fine temperature control task data.

[0136] Preferably, step S24 comprises the following steps:

[0137] Step S241: Distribute remote temperature control tasks based on multi-node fine temperature control task data to generate remote temperature control task data;

[0138] Step S242: acquiring real-time transportation equipment communication quality data; using the real-time transportation equipment communication quality data to adaptively adjust the monitoring frequency of the temperature and humidity sensor monitoring network to obtain an adaptive monitoring frequency strategy;

[0139] Step S243: executing the warehouse temperature control task according to the remote temperature control task data, and using the temperature and humidity sensor monitoring network to perform multi-point timing collection of the monitoring array based on the adaptive monitoring frequency strategy to obtain real-time cargo multi-source monitoring data;

[0140] Step S244: performing data analysis preprocessing on the real-time cargo multi-source monitoring data, and performing multi-source monitoring data fusion to generate standard cargo temperature monitoring data;

[0141] Step S245: intelligently predict the temperature control trend of standard cargo temperature monitoring data, extract cold chain abnormal features based on multi-node fine temperature control task data, and generate temperature control abnormal deviation feature data;

[0142] Step S246: Based on the preset transportation-handover cold chain case database, intelligent matching of historical cases is performed through temperature control abnormal deviation feature data to generate historical related case data;

[0143] Step S247: Set a processing plan based on the historical related case data, and perform temperature control adjustment parameter processing to generate abnormal temperature control processing plan data.

[0144] In an embodiment of the present invention, the system sends multi-node fine temperature control task data, such as temperature setting values, humidity setting values, refrigeration modes, etc. of different transport sections, to the temperature control system of the corresponding cold chain transport vehicle through a wireless communication network to generate remote temperature control task data. For example, the temperature control parameters of the "warehouse-transfer station" transport section: {-18℃ (steak), 0-4℃ (salmon), 5-15℃ (crayfish)} are sent to the on-board temperature control system of the refrigerated truck responsible for the transport section. The remote temperature control task data ensures that the transport vehicle can execute according to the predetermined temperature control scheme at each node. The system obtains the communication signal strength, delay and other data of the transport vehicle in real time to evaluate the communication quality. The monitoring frequency of the temperature and humidity sensor is dynamically adjusted according to the communication quality. For example, when the communication quality is good, the normal monitoring frequency is maintained, such as collecting data once a minute; when the communication quality is poor, the monitoring frequency is reduced, such as collecting data every 5 minutes, to reduce the amount of data transmission and ensure the reliable transmission of key data. Finally, an adaptive monitoring frequency strategy is obtained, for example: {"Signal strength>80%":"Collect once every minute","Signal strength<50%":"Collect once every 5 minutes"}. After receiving the remote temperature control task data, the on-board temperature control terminal performs the corresponding temperature control operation, such as adjusting the temperature of the refrigerated compartment. At the same time, the temperature and humidity sensor collects temperature and humidity data at multiple locations in the compartment according to the adaptive monitoring frequency strategy, and uploads the data to the cloud platform through the wireless network. In addition, the system also collects information such as vehicle position, vibration, door switch status, etc., and integrates all data to generate real-time cargo multi-source monitoring data. The system parses and preprocesses the real-time cargo multi-source monitoring data, such as removing outliers, filling missing values, and converting data formats. Then, the system fuses the temperature data from different sensors, such as using a weighted average algorithm, to generate more accurate and reliable standard cargo temperature monitoring data. The system uses time series analysis and other methods to predict the trend of standard cargo temperature monitoring data, such as predicting cargo temperature changes in the future. At the same time, the system compares the prediction results with the preset temperature range in the multi-node fine temperature control task data. If the predicted temperature exceeds the preset range, it is judged as a potential temperature control anomaly. The system extracts the deviation characteristics of the temperature control anomaly, such as the magnitude and duration of the temperature exceeding the standard, and generates temperature control anomaly deviation characteristic data. The system presets a database containing various transportation-handover cold chain cases, each of which contains abnormal feature descriptions, processing solutions and other information. The system performs intelligent matching in the case database based on the temperature control abnormal deviation characteristic data, such as using a similarity calculation method to find historical cases similar to the current abnormal situation and generate historical associated case data. The system sets the corresponding temperature control adjustment parameters based on the processing solutions recommended in the historical associated case data.For example, if the matched historical case recommends increasing the power of the refrigeration unit, the system will calculate the appropriate power adjustment value based on the current situation and generate abnormal temperature control processing plan data.

[0145] Preferably, step S245 includes the following steps:

[0146] Perform temperature and humidity time series processing on standard cargo temperature monitoring data to generate time series cargo temperature monitoring data;

[0147] Perform monitoring fluctuation characteristic statistics based on the time-series cargo temperature monitoring data to obtain cold chain monitoring fluctuation characteristic data;

[0148] The temperature and humidity prediction model is constructed using the preset time series model, and the cold chain monitoring fluctuation characteristic data is used for transfer learning to obtain the temperature and humidity prediction model;

[0149] Through the temperature and humidity prediction model, the intelligent temperature control trend prediction is carried out on the time-series cargo temperature monitoring data to generate intelligent temperature control trend prediction data;

[0150] Use multi-node fine temperature control task data to match the intelligent temperature control trend prediction data with the transportation task nodes, calculate the ideal temperature control deviation value, and generate cold chain temperature control deviation data;

[0151] Perform cold chain temperature control deviation anomaly analysis based on cold chain temperature control deviation data, obtain the operating status of the temperature control equipment, and generate temperature control abnormal deviation characteristic data.

[0152] In an embodiment of the present invention, the system receives standard cargo temperature monitoring data, including a timestamp and a temperature value. The system arranges these data in chronological order to form a time series, for example: [(t1, 2°C), (t2, 2.5°C), (t3, 2.2°C)...], where t1, t2, and t3 represent different time points. Considering the different sensitivities of different cargoes to temperature changes, the system sets different time intervals according to the type of cargo, for example, for frozen seafood, the time interval is set to 1 minute; for refrigerated fruits, the time interval is set to 5 minutes. Finally, the time series cargo temperature monitoring data is generated. The system analyzes the time series cargo temperature monitoring data and counts the characteristics of temperature fluctuations, such as the maximum temperature difference, the average temperature difference, the fluctuation frequency, the standard deviation of the temperature difference, etc. For example, for a batch of frozen seafood, the system counts that the maximum temperature difference in the past hour is 1°C, the average temperature difference is 0.5°C, and the fluctuation frequency is once every 10 minutes. These cold chain monitoring fluctuation characteristic data can reflect the stability of the temperature control system and the temperature changes of the cargo. The system presets a time series model, such as an LSTM neural network, to predict the trend of temperature and humidity changes. In order to improve the accuracy of the model, the system uses cold chain monitoring fluctuation feature data for transfer learning. For example, the temperature change trends corresponding to different fluctuation features in historical transportation data are used as training data to fine-tune the preset LSTM model so that it can better adapt to the temperature change pattern of the current goods, thereby obtaining a more accurate temperature and humidity prediction model. The system uses the trained temperature and humidity prediction model to predict the time-series cargo temperature monitoring data, such as predicting the temperature change trend in the next 1 hour, 3 hours, and 6 hours. For example, the model predicts that the temperature of frozen seafood will gradually rise in the next hour, with the highest temperature reaching -17°C. These prediction data will generate intelligent temperature control trend prediction data for early warning of potential temperature control risks. The system matches the intelligent temperature control trend prediction data with multi-node fine temperature control task data. For example, the temperature prediction data for the next hour is compared with the specified temperature control range of the current transportation node. Assuming that the current transportation node requires the temperature of frozen seafood to be maintained at -18±2°C, and the predicted maximum temperature is -17°C, the ideal temperature control deviation value is calculated to be 1°C. The system compares the predicted temperature at each time point with the corresponding node temperature control requirements, and generates cold chain temperature control deviation data for evaluating the temperature control effect. The system analyzes the cold chain temperature control deviation data to determine whether there is a temperature control anomaly. For example, if the cold chain temperature control deviation value continues to exceed the set threshold (for example, 2°C), it is considered that there is a temperature control anomaly. At the same time, the system obtains the operating status of the temperature control equipment, such as the power of the refrigeration unit, the fan speed, etc. Combining the temperature control deviation data and the operating status of the equipment, the system extracts the deviation characteristics of the temperature control anomaly, such as: the amplitude and duration of the temperature exceeding the standard, the operating status of the refrigeration unit, etc., and generates temperature control abnormality deviation feature data.

[0153] As an example of the present invention, refer to Figure 3As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0154] Step S41: When the cold chain logistics order data completes logistics transportation, the corrected cold chain temperature control data and the multi-node fine temperature control task data are integrated into the cold chain transportation data to generate the cargo cold chain transportation process data;

[0155] In an embodiment of the present invention, once the order of the fresh food e-commerce platform is delivered, the system immediately integrates the relevant data of the order in the entire cold chain transportation process. The modified cold chain temperature control data includes the actual temperature and humidity records, as well as any adjustment operations performed to maintain temperature stability, such as power adjustment of the refrigeration unit, door opening and closing records, etc. The multi-node fine temperature control task data includes the preset temperature control targets, operating specifications and time limits for each transportation node (for example: departure warehouse, transfer station, distribution center, terminal distribution). The system integrates these two parts of data with the original order information (cargo type, quantity, transportation route, etc.) to generate a complete set of cargo cold chain transportation process data. This data records in detail the temperature and humidity changes, temperature control operations and execution of each node from the origin to the destination of the goods, such as the actual arrival and departure time of each node, the residence time of the goods at each node, temperature and humidity fluctuations, etc.

[0156] Step S42: Evaluate the temperature control effect of the transport section according to the cargo cold chain transport process data, and generate temperature control effect data of the transport section;

[0157] In an embodiment of the present invention, the system analyzes the temperature and humidity data of each transport section (for example, from the originating warehouse to the transfer station, from the transfer station to the distribution center) in the cargo cold chain transportation process data. Evaluation indicators include: temperature fluctuation range, average temperature deviation, humidity fluctuation range, and degree of compliance with preset temperature control targets. For example, for the transportation section of frozen seafood, the system will focus on whether the temperature is always kept within the range of -18±2°C, and calculate the deviation between the actual temperature and the target temperature. The system also considers external environmental factors, such as the impact of weather changes on temperature. Finally, the system will generate a temperature control effect score for each transportation section, such as a percentage system or a graded system (excellent, good, qualified, unqualified), and generate transportation section temperature control effect data, which contains specific evaluation indicators and scores for each transportation section.

[0158] Step S43: Evaluate the status of key handover links based on the cargo cold chain transportation process data to generate handover link evaluation data;

[0159] In an embodiment of the present invention, the system analyzes the data of each key handover link (for example, loading at the warehouse, reloading at the transfer station, and unloading at the distribution center) in the cold chain transportation process data of the goods to evaluate the efficiency and safety of the handover link. Evaluation indicators include: handover time, temperature fluctuations, cargo integrity, etc. For example, the system will count the residence time of the goods at each handover point, analyze whether the temperature fluctuation exceeds the allowable range, and check whether any goods are damaged or lost. The system will also evaluate the standardization of operators, such as whether temperature control operations are performed in accordance with operating procedures. Finally, the system will generate an evaluation score for each handover link, such as a percentage system or a graded system, and generate handover link evaluation data, which contains specific evaluation indicators and scores for each handover link.

[0160] Step S44: Calculate the cargo quality change index based on the cargo cold chain transportation process data to generate cargo transportation quality index data;

[0161] In an embodiment of the present invention, the system calculates the cargo quality change index based on the cargo cold chain transportation process data and the characteristics of the cargo. For example, for fresh products that are sensitive to temperature, the degree of quality loss of the cargo can be evaluated based on the temperature change curve and the shelf life information of the cargo. For example, based on the temperature change curve of salmon and the shelf life data of salmon at different temperatures, the quality loss rate of salmon during transportation is calculated. Finally, cargo transportation quality index data is generated, for example: {"Salmon":"Quality loss rate: 2%"}.

[0162] Step S45: Perform a weighted cold chain quality score based on the temperature control effect data of the transportation section, the handover link evaluation data, and the cargo transportation quality index data to generate logistics quality score data.

[0163] In an embodiment of the present invention, the system performs a weighted score on the entire cold chain logistics process based on the temperature control effect data of the transportation section, the evaluation data of the handover link, and the cargo transportation quality index data. For example, the weight of the temperature compliance rate is set to 50%, the weight of the average temperature deviation is set to 20%, the weight of the temperature fluctuation in the handover link is set to 15%, and the weight of the cargo quality loss rate is set to 15%. According to the actual value and weight of each indicator, the final logistics quality score is calculated. For example, if the temperature compliance rate of an order is 95%, the average temperature deviation is 0.5°C, the temperature fluctuation in the handover link is 1°C, and the cargo quality loss rate is 1%, then the logistics quality score of the order is: 95% × 50% + (1-0.5 / 2) × 20% + (1-1 / 2) × 15% + (1-1 / 2) × 15% = 92.5 points. Finally, the logistics quality score data is generated.

[0164] The present invention also provides a smart logistics platform management system based on multi-source data, which executes the smart logistics platform management method based on multi-source data as described above. The smart logistics platform management system based on multi-source data includes:

[0165] The order demand analysis module is used to obtain cold chain logistics order data; analyze the order characteristics of cold chain logistics order data, process the temperature control requirements of multiple types of goods, and generate temperature control demand data for goods;

[0166] The temperature control anomaly prediction module is used to build a temperature and humidity sensor monitoring network based on the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring based on the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain anomaly feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set the processing plan to generate abnormal temperature control processing plan data;

[0167] The temperature control execution module is used to perform temperature control correction at the transport-handover point according to the abnormal temperature control processing plan data, and to execute real-time temperature control adjustment instructions to generate corrected cold chain temperature control data;

[0168] The logistics quality assessment module is used to integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data.

[0169] The present application is to ensure the environmental adaptability of different goods during transportation and reduce the risk of goods loss caused by improper temperature control through in-depth analysis of cold chain logistics order data and processing of temperature control requirements of multiple types of goods. The present invention realizes real-time monitoring of the entire process of cold chain logistics by constructing a temperature and humidity sensor monitoring network. This networked monitoring mode significantly improves the timeliness and accuracy of data collection, so that each logistics node and transportation link has real-time monitoring capabilities. Through task decomposition and multi-node collaboration, the temperature control demand tasks for each logistics node are accurately formulated, making the temperature monitoring of the entire cold chain logistics process more systematic and coordinated. Through the multi-source integration of real-time monitoring data, not only can temperature control anomalies be quickly discovered, but also the risk of temperature control chain breakage is significantly reduced. By making real-time adjustments to abnormal temperature control, the temperature control stability of key nodes is effectively guaranteed, overcoming the problem that traditional manual inspection methods cannot respond in time, thereby significantly improving the temperature control reliability of the logistics chain, realizing the quantification and feedback of the quality of the entire logistics process, and facilitating the platform to optimize management decisions in real time. This data interconnection and interoperability throughout the entire cold chain logistics supply chain not only improves the efficiency of temperature control management, but also provides reliable technical support for collaborative optimization between different links, comprehensively improving the overall quality of cold chain logistics services.

[0170] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0171] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A smart logistics platform management method based on multi-source data, characterized in that: The following steps are involved: Step S1: Acquire cold chain logistics order data; perform order characteristic analysis on the cold chain logistics order data, and process temperature control requirements of multiple types of goods to generate temperature control demand data for goods; Step S2: construct a temperature and humidity sensor monitoring network according to the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring according to the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set a processing plan to generate abnormal temperature control processing plan data; Step S3: Correct the temperature control at the transport-handover point according to the abnormal temperature control processing plan data, and execute the real-time temperature control adjustment instruction to generate corrected cold chain temperature control data; Step S4: Integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data.

2. The intelligent logistics platform management method based on multi-source data according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtain cold chain logistics order data; Step S12: performing order characteristic analysis on the cold chain logistics order data to generate cold chain logistics order characteristic data; Step S13: searching and matching the cold chain logistics order feature data with the preset cargo information database to generate cold chain cargo matching data; Step S14: Evaluate the transportation time according to the cold chain logistics order characteristic data and generate logistics timeliness requirement data; Step S15: Extract the temperature and humidity adaptability ranges of multiple types of goods based on the cold chain goods matching data and the logistics timeliness requirement data, perform temperature control demand processing, and generate goods temperature control demand data.

3. The intelligent logistics platform management method based on multi-source data according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: performing cargo clustering analysis on the cold chain cargo matching data to generate cargo classification label data; Step S152: extracting cold chain logistics and transportation influencing features from cold chain goods matching data based on goods classification label data to generate goods transportation sensitive feature data; Step S153: Calculate the temperature and humidity sensitivity according to the sensitive characteristic data of cargo transportation to generate cargo temperature and humidity sensitivity data; Step S154: Processing the cargo transportation sensitive characteristic data according to the temperature and humidity adaptation range of multiple types of cargoes through the cargo temperature and humidity sensitivity data to obtain cargo temperature and humidity range requirement data; Step S155: Use the logistics timeliness requirement data to associate the cargo temperature and humidity range requirement data with the transportation distance, and perform temperature and humidity distance control range correction to obtain cargo temperature control requirement data.

4. The intelligent logistics platform management method based on multi-source data according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Screen the vehicle-mounted temperature control equipment according to the cargo temperature control demand data, match the cold chain transportation vehicles with the cold chain logistics order data, and generate order cold chain transportation equipment data; Step S22: deploying a warehouse monitoring sensor array based on the order cold chain transportation equipment data, and performing wireless Internet of Things processing to obtain a temperature and humidity sensor monitoring network; Step S23: Decomposing the cold chain logistics order data into multi-node temperature control tasks based on the cargo temperature control demand data to generate multi-node fine temperature control task data; Step S24: Perform real-time logistics cold chain multi-source monitoring based on the temperature and humidity sensor monitoring network to generate real-time cargo multi-source monitoring data; perform cold chain abnormality feature analysis on the real-time cargo multi-source monitoring data through multi-node fine temperature control task data, set a processing plan, and generate abnormal temperature control processing plan data.

5. The intelligent logistics platform management method based on multi-source data according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: extracting the shipping place and the receiving place from the cold chain logistics order data to generate order logistics address data; Step S232: Preliminary order transportation route planning is performed on the order logistics address data through a preset logistics transportation network to generate preliminary order transportation route data; Step S233: evaluating the capacity of the node temperature control facilities according to the preliminary route data of the order transportation, screening the available temperature control nodes for transportation, and generating the capacity data of the temperature control facilities at the transportation nodes; Step S234: using the capacity data of the temperature control facilities at the transport nodes as path planning constraints, and performing intelligent transport path selection on the preliminary route data for order transport, to generate intelligent cold chain transport path data; Step S235: extracting cargo handover tasks from the intelligent cold chain transport route data to generate cargo handover point task data; Step S236: Based on the cargo temperature control demand data, the cargo handover point task data is decomposed into multi-node temperature control tasks to generate multi-node fine temperature control task data.

6. The intelligent logistics platform management method based on multi-source data according to claim 5 is characterized in that: Step S236 includes the following steps: Through the cargo temperature control demand data, the cargo handover point task data is accurately analyzed for the upper and lower limits of temperature control demand, and the detailed temperature control demand data of the handover point is obtained; Calculate the dynamic thermal attenuation index of cargo based on the precise temperature control demand data of the handover point and generate the thermal attenuation index of cargo; The cargo thermal attenuation index is used to calculate the task temperature fluctuation risk value of the cargo handover task data, and the temperature fluctuation risk tasks are divided based on the preset temperature fluctuation risk threshold to obtain high-risk cargo handover task data and low-risk cargo handover task data respectively; Determine the temperature control redundancy level of the transport section for high-risk cargo delivery task data, perform multiple redundant temperature control processing, and generate high-risk temperature control task parameters; Calculate the optimal energy-saving temperature range for low-risk cargo delivery task data, perform dynamic energy-saving temperature control processing, and generate low-risk temperature control task parameters; The intelligent cold chain transport path data is decomposed into temperature control tasks in the transport section through high-risk temperature control task parameters and low-risk temperature control task parameters, and the temperature control parameter execution task is encapsulated to generate multi-node fine temperature control task data.

7. The intelligent logistics platform management method based on multi-source data according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: Distribute remote temperature control tasks based on multi-node fine temperature control task data to generate remote temperature control task data; Step S242: acquiring real-time transportation equipment communication quality data; using the real-time transportation equipment communication quality data to adaptively adjust the monitoring frequency of the temperature and humidity sensor monitoring network to obtain an adaptive monitoring frequency strategy; Step S243: executing the warehouse temperature control task according to the remote temperature control task data, and using the temperature and humidity sensor monitoring network to perform multi-point timing collection of the monitoring array based on the adaptive monitoring frequency strategy to obtain real-time cargo multi-source monitoring data; Step S244: performing data analysis preprocessing on the real-time cargo multi-source monitoring data, and performing multi-source monitoring data fusion to generate standard cargo temperature monitoring data; Step S245: intelligently predict the temperature control trend of standard cargo temperature monitoring data, extract cold chain abnormal features based on multi-node fine temperature control task data, and generate temperature control abnormal deviation feature data; Step S246: Based on the preset transportation-handover cold chain case database, intelligent matching of historical cases is performed through temperature control abnormal deviation feature data to generate historical related case data; Step S247: Set a processing plan based on the historical related case data, and perform temperature control adjustment parameter processing to generate abnormal temperature control processing plan data.

8. The intelligent logistics platform management method based on multi-source data according to claim 7 is characterized in that: Step S245 includes the following steps: Perform temperature and humidity time series processing on standard cargo temperature monitoring data to generate time series cargo temperature monitoring data; Perform monitoring fluctuation characteristic statistics based on the time-series cargo temperature monitoring data to obtain cold chain monitoring fluctuation characteristic data; The temperature and humidity prediction model is constructed using the preset time series model, and the cold chain monitoring fluctuation characteristic data is used for transfer learning to obtain the temperature and humidity prediction model; Through the temperature and humidity prediction model, the intelligent temperature control trend prediction is carried out on the time-series cargo temperature monitoring data to generate intelligent temperature control trend prediction data; Use multi-node fine temperature control task data to match the intelligent temperature control trend prediction data with the transportation task nodes, calculate the ideal temperature control deviation value, and generate cold chain temperature control deviation data; Perform cold chain temperature control deviation anomaly analysis based on cold chain temperature control deviation data, obtain the operating status of the temperature control equipment, and generate temperature control abnormal deviation characteristic data.

9. The intelligent logistics platform management method based on multi-source data according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: When the cold chain logistics order data completes logistics transportation, the corrected cold chain temperature control data and the multi-node fine temperature control task data are integrated into the cold chain transportation data to generate the cargo cold chain transportation process data; Step S42: Evaluate the temperature control effect of the transport section according to the cargo cold chain transport process data, and generate temperature control effect data of the transport section; Step S43: Evaluate the status of key handover links based on the cargo cold chain transportation process data to generate handover link evaluation data; Step S44: Calculate the cargo quality change index based on the cargo cold chain transportation process data to generate cargo transportation quality index data; Step S45: Perform a weighted cold chain quality score based on the temperature control effect data of the transportation section, the handover link evaluation data, and the cargo transportation quality index data to generate logistics quality score data.

10. A smart logistics platform management system based on multi-source data, characterized in that: Used to execute the intelligent logistics platform management method based on multi-source data as claimed in claim 1, the intelligent logistics platform management system based on multi-source data includes: The order demand analysis module is used to obtain cold chain logistics order data; analyze the order characteristics of cold chain logistics order data, process the temperature control requirements of multiple types of goods, and generate temperature control demand data for goods; The temperature control anomaly prediction module is used to build a temperature and humidity sensor monitoring network based on the temperature control demand data of the goods; perform multi-node temperature control task decomposition on the cold chain logistics order data through the temperature control demand data of the goods, and generate multi-node fine temperature control task data; perform real-time logistics cold chain multi-source monitoring based on the temperature and humidity sensor monitoring network, and generate real-time cargo multi-source monitoring data; perform cold chain anomaly feature analysis on the real-time cargo multi-source monitoring data through the multi-node fine temperature control task data, and set the processing plan to generate abnormal temperature control processing plan data; The temperature control execution module is used to perform temperature control correction at the transport-handover point according to the abnormal temperature control processing plan data, and to execute real-time temperature control adjustment instructions to generate corrected cold chain temperature control data; The logistics quality assessment module is used to integrate the corrected cold chain temperature control data and multi-node fine temperature control task data into cold chain transportation data to generate cargo cold chain transportation process data; perform cold chain quality weighted scoring based on the cargo cold chain transportation process data to generate logistics quality scoring data.

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