Sample return system based on mobile terminal application

Abstract

The invention relates to the technical field of sample return, and discloses a sample return system based on a mobile terminal application, which comprises an information acquisition module, a path generation module, a state monitoring module and the like. The information acquisition module acquires a sample return instruction and user sample basic information (including an acquisition position and a type identifier); the path generation module is used for generating a logistics distribution path according to the basic information and optimizing the path in combination with transportation priority, real-time traffic and node load data; the state monitoring module constructs a period to obtain sample position update and integrity verification data, and marks the data as an overall state value; the abnormity judgment module judges whether abnormity occurs; the two regulation and control modules respectively obtain regulation and control coefficients; the path optimization module calculates a deviation value according to the coefficient and adjusts a path threshold range; and the execution module controls the back-sending process. The system realizes intelligent management, improves information acquisition accuracy, path planning scientificity and transportation monitoring real-time performance, and guarantees sample return efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the field of sample return technology, and specifically to a sample return system based on mobile terminal applications. Background Art

[0002] In fields such as healthcare, biological testing, and scientific research, the sample return process after collection is crucial for ensuring testing accuracy and timeliness. Traditional sample return models rely primarily on manual labor and fixed logistics systems, presenting numerous challenges that require urgent resolution.

[0003] From the perspective of information collection and processing, traditional methods typically use paper documents to record basic sample information, such as the location and type of sample collection. This not only results in low data entry efficiency, but is also prone to information errors or omissions due to handwriting recognition errors. In terms of logistics route planning, traditional systems lack dynamic adjustment capabilities and are often allocated based on fixed routes. They are unable to respond in real time to dynamic factors such as traffic conditions and changes in logistics node loads, resulting in unreasonable sample transportation routes and difficulty in ensuring transportation timeliness. For example, in the event of sudden traffic congestion or temporary overload of logistics nodes, samples may be delayed, affecting the progress of subsequent testing.

[0004] Monitoring sample transport status is another shortcoming of the traditional model. Traditional methods struggle to achieve real-time tracking and status assessment of sample transport, and they lack timely access to updated sample location and integrity verification data. If samples are lost, damaged, or stored in substandard conditions during transport, it's difficult to detect and implement remedial measures, potentially leading to sample failure and compromising test results. This can even necessitate re-collection, increasing costs and time.

[0005] In terms of exception handling mechanisms, traditional systems lack intelligent exception identification and control capabilities. When anomalies occur during sample transportation, they are unable to quickly and accurately identify the abnormal state, nor can they dynamically adjust logistics routes and return shipping processes based on the abnormal situation. This leads to inefficient exception handling and further exacerbates the risks involved in sample return shipping.

[0006] Furthermore, with the increasing prevalence of mobile applications, traditional sample return systems have been insufficiently integrated with mobile technology, failing to fully leverage the convenience and real-time capabilities of mobile devices. This makes it difficult to meet user needs for real-time monitoring and convenient operations of the sample return process. For example, users are unable to view sample shipment status or submit location updates in real time via mobile devices, resulting in a poor user experience. Summary of the Invention

[0007] The purpose of the present invention is to provide a sample return system based on mobile applications to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a sample return system based on a mobile application, the system comprising:

[0009] An information collection module is used to obtain a sample return instruction and input user sample basic information data according to the sample return instruction, wherein the user sample basic information data includes sample collection location data and sample type identification data;

[0010] A path generation module is used to generate a sample logistics distribution path based on the sample collection location data and the sample type identification data;

[0011] The status monitoring module is used to build a status detection cycle, obtain the sample location update data and sample integrity verification data submitted by the user during the status detection cycle, and mark them as the overall status value of the sample;

[0012] An abnormality determination module, used to determine whether the overall state value of the sample meets the first preset condition, and if not, mark the sample as an abnormal state period;

[0013] A first control module, configured to obtain a first control coefficient according to the overall state value of the sample;

[0014] The second control module is used to obtain the user's sample transportation timeliness data and sample handover verification data within the status detection period, and mark them as the second control coefficient;

[0015] A path optimization module is used to calculate a logistics path deviation value according to the first control coefficient and the second control coefficient, and adjust a sample logistics allocation path threshold range according to the logistics path deviation value;

[0016] The execution module is used to control the execution of the sample return process according to the adjusted threshold range.

[0017] Preferably, the step of obtaining a sample return instruction and inputting user sample basic information data according to the sample return instruction includes:

[0018] Obtain the return requirement type of the mobile application, and generate multiple sample return instructions based on the return requirement type; input the sample collection location data and the corresponding sample type identification data according to each sample return instruction; summarize the sample collection location data and sample type identification data into user sample basic information data.

[0019] Preferably, the step of generating a sample logistics distribution path according to the sample collection location data and the sample type identification data includes:

[0020] Converting sample collection location data and sample type identification data into logistics node coordinate data;

[0021] Establish regional logistics network topology map based on logistics node coordinate data;

[0022] Extract the sample collection location node and the sample receiving center node according to the logistics network topology map;

[0023] Determine a plurality of initial path key coordinate points from the sample collection location nodes;

[0024] Determine multiple target path key coordinate points from the sample receiving center node;

[0025] A plurality of initial path key coordinate points and a plurality of target path key coordinate points are combined into a sample logistics distribution path.

[0026] Preferably, the step of establishing a status detection cycle and obtaining sample location update data and sample integrity verification data submitted by users within the status detection cycle includes:

[0027] Obtain the number of times the user submits the sample return instruction;

[0028] Get the preset submission threshold number;

[0029] Determine whether the number of submissions exceeds the submission threshold. If the number of submissions exceeds the submission threshold, mark the current time as the detection start time;

[0030] Get the status detection interval duration;

[0031] Determine the detection end time based on the detection start time and status detection interval;

[0032] Delimit the status detection cycle based on the detection start time and detection end time;

[0033] Obtain the sample location update data and sample integrity verification data submitted by the user during the status detection cycle, and mark them as the overall status value of the sample.

[0034] Preferably, the step of determining whether the overall state value of the sample meets the first preset condition, and if not, marking the sample as being in an abnormal state period, includes:

[0035] Get the sample status threshold range;

[0036] Determine whether the overall state value of the sample exceeds the sample state threshold range;

[0037] If the overall status value of the sample exceeds the sample status threshold range, the sample transportation is determined to be abnormal and marked as a sample abnormal status period;

[0038] If the overall status value of the sample does not exceed the sample status threshold range, the sample transportation is determined to be normal.

[0039] Preferably, the step of obtaining the first control coefficient according to the overall state value of the sample includes:

[0040] Obtain the local state value of the sample position update data of each sample return instruction completed by the user within the state detection cycle;

[0041] The first control coefficient is calculated based on the multiple local state values ​​and the overall state value of the sample, wherein the calculation formula is:

[0042]

[0043] In the formula, C represents the first control coefficient, j represents the number of the local state value, m represents the total number of local state values, k represents the overall state value of the sample, and K j Represented as the jth local state value.

[0044] Preferably, the step of obtaining the user's sample transportation timeliness data and sample handover verification data within the status detection period includes:

[0045] Obtain the actual sample transportation time data for each sample return instruction completed by the user during the status detection cycle;

[0046] Obtain the standard sample transportation time data for each sample return instruction completed by the user;

[0047] The transportation time deviation value of each sample return instruction completed by the user within the status detection period is calculated based on multiple actual time data and multiple standard time data, and marked as the second control coefficient. The calculation formula is:

[0048]

[0049] Where D is the second control coefficient, p is the number of the sample return instruction, q is the total number of sample return instructions, and s is the total number of sample return instructions. p It is represented as the actual duration data of the p-th sample return instruction, S p It represents the standard duration data of the p-th sample return instruction.

[0050] Preferably, the step of calculating the logistics path deviation value according to the first control coefficient and the second control coefficient includes:

[0051] Get the standard path deviation benchmark value;

[0052] The logistics path deviation value is calculated according to the first control coefficient and the second control coefficient, wherein the calculation formula is:

[0053] X=Y×C×D

[0054] Where X represents the logistics path deviation value, Y represents the standard path deviation reference value, C represents the first control coefficient, and D represents the second control coefficient;

[0055] Obtaining a path control mapping table, wherein the path control mapping table includes a plurality of logistics path deviation interval values ​​and a logistics allocation path threshold range corresponding to each interval value;

[0056] Match the target deviation interval value according to the logistics path deviation value;

[0057] According to the target deviation interval value, the corresponding logistics allocation path threshold range is extracted from the path control mapping table.

[0058] Preferably, after the step of generating the sample logistics distribution path, the method further includes:

[0059] Get the transport priority parameter corresponding to the sample type identification data;

[0060] Adjust the weight coefficient of the sample logistics allocation path according to the transportation priority parameter;

[0061] The optimized logistics distribution path is regenerated based on the adjusted weight coefficients.

[0062] Preferably, the step of generating the sample logistics distribution path further includes:

[0063] Obtain real-time traffic status data and logistics node load data;

[0064] Dynamically modify the logistics network topology based on traffic status data and node load data;

[0065] The key coordinate point combination of the sample logistics distribution path is updated based on the revised logistics network topology graph.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] In terms of information collection and processing, the information collection module can obtain sample return instructions through a mobile application and accurately input basic user sample information data, including sample collection location data and sample type identification data, based on the instructions. This process digitizes and automates information collection, avoids the human errors associated with traditional paper-based record keeping, and improves the efficiency and accuracy of information entry. Furthermore, the system can generate multiple sample return instructions based on the type of return request and aggregate this information to form a complete set of basic user sample information data, providing reliable data support for subsequent logistics route planning and status monitoring.

[0068] The path generation and optimization module converts sample collection location data and sample type identification data into logistics node coordinate data to establish a regional logistics network topology map. It can scientifically extract sample collection location nodes and sample receiving center nodes, determine key coordinate points, and generate the initial sample logistics distribution path. On this basis, the system can also adjust the path weight coefficient based on the transportation priority parameters corresponding to the sample type identification data, regenerate the optimized path, and ensure that different types of samples can obtain reasonable path arrangements based on their transportation needs. In addition, the system obtains traffic status data and logistics node load data in real time, dynamically corrects the logistics network topology map, and updates the key coordinate point combination, so that the logistics path can adapt to changes in the external environment in real time, improving the scientific nature and dynamic adaptability of path planning, effectively shortening sample transportation time, and reducing transportation costs.

[0069] The status monitoring and anomaly determination module establishes a status detection cycle to acquire real-time sample location update data and sample integrity verification data, marking them as the overall sample status value. By comparing the overall sample status value with the preset sample status threshold range, it can quickly and accurately determine whether sample transportation is abnormal. Once an anomaly is detected, it is immediately marked as a sample abnormal status period, providing a basis for timely handling of abnormal situations. This real-time monitoring and intelligent determination mechanism effectively improves the safety and reliability of the sample transportation process, reduces the risk of sample loss and damage, and ensures the integrity of the sample and the accuracy of the test results.

[0070] By calculating the first and second control coefficients and combining them with the standard path deviation baseline, the control module and path optimization module can accurately calculate the logistics path deviation value. They then adjust the sample logistics allocation path threshold range based on the path control mapping table, achieving dynamic optimization and precise control of the logistics path. This multi-dimensional data-based control mechanism enables the system to flexibly adjust the return process based on actual conditions such as sample status and transportation timeliness. This improves the system's adaptability and decision-making scientificity, ensures that samples are transported along the optimal path, and further enhances the efficiency and quality of sample returns.

[0071] The deep integration of mobile applications is a major highlight of this invention. The system leverages the convenience and real-time capabilities of mobile devices. Users can trigger sample return instructions and submit updated sample location data through their mobile devices, enabling real-time interaction and monitoring of the sample return process. This not only improves the user experience but also makes the sample return process more transparent, allowing users to keep informed of the sample's transportation status and enhancing their trust in the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a diagram showing the working principle of the sample return system based on mobile application according to the present invention;

[0073] Figure 2 Flowchart for sample return instructions and basic information input;

[0074] Figure 3 Flowchart generated for sample logistics allocation routing;

[0075] Figure 4 Build a flow chart for the condition detection cycle and data acquisition;

[0076] Figure 5 Flowchart for determining abnormal status of samples. DETAILED DESCRIPTION

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0078] See also Figure 1 - Figure 5 The present invention relates to a sample return system based on a mobile application, and the specific implementation steps are as follows:

[0079] Information Collection Module: The system obtains the user's sample return instructions through the mobile application. The user enters basic sample information data on the mobile interface based on the return requirements, including sample collection location data (such as specific address, latitude and longitude coordinates, etc.) and sample type identification data (such as type labels such as blood, tissue, secretions, etc.). For example, when a user submits a blood sample return request through the mobile app in a medical testing scenario, they need to enter the sampling point address and the "blood sample" type identification.

[0080] Path Generation Module: The system generates an initial sample logistics distribution path based on sample collection location data and sample type identification data. By analyzing geographic location and sample characteristics, it plans the optimal transportation route to ensure that samples are transported to the receiving center in a safe and efficient manner.

[0081] Status monitoring module: The system builds a status detection cycle, and obtains in real time the sample location update data (such as logistics node scanning information, GPS positioning data) and sample integrity verification data (such as temperature monitoring values, packaging sealing test results) submitted by users through mobile terminals during this cycle, and comprehensively marks the above data as the overall status value of the sample to reflect the real-time status of the sample during transportation.

[0082] Abnormality determination module: The system presets a sample status threshold range (such as the normal temperature range, the location update frequency threshold, etc.) and determines whether the overall status value of the sample meets the first preset condition (i.e., whether it is within the threshold range). If it exceeds the range, it is marked as an abnormal state of the sample and the early warning mechanism is triggered; if it does not exceed the range, the transportation is determined to be normal.

[0083] First control module: The system calculates the first control coefficient based on the overall state value of the sample, which is used to quantify the impact of the sample state on the logistics path.

[0084] Second control module: The system obtains sample transportation time data (such as the comparison between actual transportation time and standard time) and sample handover verification data (such as the identity verification results of the signatory and the handover time record) within the status detection cycle, and marks it as the second control coefficient to reflect transportation efficiency and handover safety.

[0085] Path optimization module: The system combines the first control coefficient and the second control coefficient to calculate the logistics path deviation value, and adjusts the threshold range of the sample logistics allocation path according to the deviation value to achieve dynamic optimization of the transportation path.

[0086] Execution module: The system controls the execution of the sample return process based on the adjusted threshold range, including logistics node scheduling, transportation resource allocation and other operations, to ensure that the return process follows the optimized path.

[0087] The present invention will be further described below in conjunction with Examples 1 to 5:

[0088] Example 1:

[0089] During the information collection and status monitoring phase, the system achieves precise data collection and cycle delineation through the interactive logic of the mobile application. The specific process is as follows: The information collection module obtains the return request type through the mobile application's user interface. Return request types include routine testing, emergency testing, and other scenarios. The system generates corresponding sample return instructions based on the different return request types. For example, for emergency testing requests, a return instruction with a priority of "urgent" is generated, triggering a rapid response mechanism for subsequent processes. In the mobile interface, users sequentially enter the sample collection location data and the corresponding sample type identification data for each sample return instruction. The sample collection location data can be manually entered as a specific address or obtained through the map positioning function. For example, in a medical scenario, users can select a nearby community health service center as the sampling point and obtain the coordinates of the location through the positioning function. The sample type identification data is available for users to select through a preset drop-down menu. The menu options include specific type labels such as "blood sample," "tissue sample," and "secretion sample." Users select based on the actual sample attributes. The system summarizes the sample collection location data and sample type identification data entered by the user, stores them as user sample basic information data in a preset data format (such as JSON format), and synchronizes them to the cloud database for subsequent module calls.

[0090] The cycle construction mechanism of the status monitoring module is specifically as follows: the system uses the background program to count in real time the number of times users submit sample return instructions on the mobile terminal, and the preset submission threshold number is a configurable system parameter (for example, the initial value is set to 3 times). When the number of submissions by the user exceeds the preset submission threshold number, the system automatically records the current time and marks it as the detection start time. At the same time, the system obtains the preset status detection interval duration parameter (for example, set to 12 hours), which can be adjusted according to the sample type or transportation requirements. Based on the detection start time and the status detection interval duration, the system determines the detection end time through time calculation. For example, if the detection start time is 10:00 on May 17, 2025 and the interval duration is 12 hours, the detection end time is 10:00 on May 18, 2025, thereby defining a complete status detection cycle. During this cycle, the system continuously obtains sample location update data and sample integrity verification data submitted by users through the real-time data interface of the mobile application: Sample location update data includes logistics node scanning information (such as the entry and exit timestamps of express delivery stations), GPS positioning data (such as the real-time coordinates of the transport vehicle), etc., which are used to record the changes in the geographical location of the sample during transportation; Sample integrity verification data is collected through smart packaging or sensor devices. For example, the built-in temperature sensor monitors the temperature of the sample storage environment in real time, and the pressure sensor detects whether the packaging is subjected to abnormal vibration. The relevant data is transmitted to the system in the form of a numerical value or status identifier (such as "normal" or "abnormal"). The system integrates and processes the above two types of data and generates an overall sample status value through a preset algorithm (such as a weighted summation algorithm). This value is used to comprehensively reflect the transportation status of the sample during the status detection cycle. For example, parameters such as location update frequency, temperature fluctuation amplitude, and number of vibrations are calculated according to different weights (such as location accounts for 40%, temperature accounts for 50%, and vibration accounts for 10%) to obtain a comprehensive value from 0 to 100. The higher the value, the more stable the sample status.

[0091] Example 2:

[0092] In the path generation and optimization phase, the system achieves precise route planning through logistics network topology modeling and priority adjustment. The specific steps are as follows: The path generation module first processes the sample collection location data and sample type identification data entered by the user, and uses the geographic information system (GIS) to convert the specific address into logistics node coordinate data. For example, after the address of "XX District People's Hospital Laboratory Department" submitted by the user is parsed, the corresponding latitude and longitude coordinates (such as 39.9042° north latitude and 116.3903° east longitude) are generated and matched to the grassroots collection nodes in the logistics network. The sample type identification data (such as "urine sample") is used as a reference attribute for subsequent path planning, and is used to call the corresponding transportation rules and priority settings.

[0093] The system constructs a regional logistics network topology based on logistics node coordinate data. This topology abstractly represents the logistics network structure using nodes and edges. Nodes include grassroots collection nodes (such as hospitals and physical examination centers), transit nodes (such as urban sorting centers and regional express delivery stations), and receiving center nodes (such as medical testing laboratories). Edges represent transportation routes between nodes, with each edge associated with parameters such as transportation distance, estimated time, and transportation mode (such as road freight or cold chain transportation). For example, the "XX District People's Hospital" node is connected to the "XX Express Sorting Center" via a road route. The route is 10 kilometers long, with an estimated travel time of 25 minutes, and supports cold chain transportation.

[0094] After extracting the sample collection location node and the receiving center node from the topological map, the system screens the initial path key coordinate points and the target path key coordinate points respectively. The initial key coordinate points are selected from the adjacent nodes of the collection location node, such as express delivery stations and logistics hubs within a radius of 5 kilometers with the collection node as the center, to ensure that the samples can be quickly transferred from the collection point to the trunk transportation network; the target key coordinate points are selected from the adjacent nodes of the receiving center node, such as the sorting centers and distribution stations around the laboratory, to facilitate the last mile delivery of the samples. By combining these key coordinate points, an initial logistics distribution path is generated, such as "XX District People's Hospital → XX Express Sorting Center → XX Cold Chain Transport Trunk → YY Medical Testing Laboratory". This path clarifies the sequence of transportation nodes and the specific parameters of each section of transportation.

[0095] After generating the initial route, the system retrieves the corresponding transport priority parameter based on the sample type identification data. This parameter is preset to the attribute values ​​of different sample types. For example, "pathological samples" have a high priority (corresponding to a weight coefficient of 1.2), "routine physical examination samples" have a medium priority (corresponding to a weight coefficient of 1.0), and "non-biological samples" have a low priority (corresponding to a weight coefficient of 0.8). The priority parameter is used to adjust the path weight coefficient. Specifically, when calculating the comprehensive cost of the path for high-priority samples, the time consumption and timeliness parameters have a higher weight. The system will give priority to routes with shorter transport times.

[0096] At the same time, the system obtains traffic status data and logistics node load data through real-time data interfaces: traffic status data includes real-time road conditions (such as congested sections, accident locations), weather warnings (such as the impact of blizzards on highways), road construction information, etc.; logistics node load data includes the package backlog of the sorting center, equipment operating status, personnel work saturation, etc.

[0097] The system dynamically modifies the logistics network topology based on real-time data: for congested or delayed sections, adjust the estimated time parameters (such as increasing by 50%) or mark them as "high risk"; for high-load nodes, reduce their processing efficiency parameters (such as extending the package sorting time) or temporarily block them as unselectable nodes. For example, if the "XX cold chain transport trunk line" in the original path is difficult to pass due to heavy snow, the system automatically searches for the alternative trunk line "XY cold chain transport line" and recalculates the estimated time and cost of the route. Based on the revised topology, the system updates the combination of key coordinate points of the path, such as replacing the "XX express sorting center" in the original path with the "YY express sorting center" with a lower load, and regenerates the optimized path containing the new node.

[0098] The core of this path generation and optimization process lies in transforming geographic information into a computable graph structure through topological modeling, leveraging priority parameters to correlate sample characteristics with path selection, and dynamically adjusting node and edge attributes based on real-time data. This approach optimizes transport timeliness and path reliability while ensuring sample transport safety. The entire process utilizes data-driven automated algorithms to generate and iterate paths, eliminating the need for human intervention and ensuring that path planning consistently adapts to changes in the actual transportation environment.

[0099] It should be noted that the dynamic demarcation mechanism of the status detection cycle is triggered by the user's actual operation frequency, avoiding the problem that fixed cycles may cause data collection to not match the actual status of sample transportation. For example, when users frequently submit return instructions, the system promptly initiates the monitoring cycle to ensure real-time monitoring of high-frequency transportation tasks; when the number of submissions does not reach the threshold, the system maintains the regular data collection mode to reduce resource consumption. This mechanism improves the system's adaptability to different user usage scenarios and sample transportation needs through flexible monitoring strategies, ensuring the timeliness and effectiveness of sample status data collection.

[0100] Example 3:

[0101] In the abnormality determination and first control coefficient calculation stages, the system achieves state quantification through data threshold comparison and statistical models. The specific process is as follows:

[0102] The anomaly determination module pre-configures sample status threshold ranges in the system backend. These ranges are set based on the characteristics of the sample type. For example, for blood samples, the temperature threshold is set at 2-8°C, the location update interval threshold is set at least once every two hours, and the vibration amplitude threshold is set to no more than 5g (gravitational acceleration). For tissue samples, the temperature threshold may be adjusted to 4-10°C, and a humidity monitoring threshold (e.g., 30%-60% RH) may be added. These threshold parameters can be customized through the system management interface to meet the transportation requirements of different sample types.

[0103] After the status monitoring module generates the overall status value of the sample, the abnormality judgment module first obtains the status threshold range corresponding to the sample type, and then compares the overall status value of the sample with the threshold range. Taking blood samples as an example, if the temperature parameter in the overall status value of the sample is 10°C, which exceeds the preset range of 2-8°C, the sample transportation is judged to be abnormal, and the system automatically marks the current time period as the sample abnormal status period, and sends an early warning notification to the user through the mobile application. The notification content includes the abnormality type (such as "temperature abnormality"), the time of occurrence and the possible impact (such as "may affect the sample test results"). If the temperature parameter is 5°C, which is within the threshold range, the sample transportation is judged to be normal and the early warning mechanism is not triggered.

[0104] The calculation of the first control coefficient is based on the statistics of local state values ​​within the state detection period. The specific formula is:

[0105]

[0106] Among them, C represents the first control coefficient, which is a dimensionless value used to quantify the degree of influence of sample state fluctuations on the logistics path. Its value range is usually 0 to 1.5 (adjustable according to the actual business scenario); j represents the number of the local state value, which starts from 1 and increases sequentially, corresponding to the 1st to mth data collection in the state detection cycle; m represents the total number of local state values, that is, the number of data collections in the state detection cycle, which is determined by the detection cycle length and sampling frequency (for example, if the cycle is 12 hours and the sample is taken once per hour, then m = 12); k represents the overall state value of the sample, which is a comprehensive indicator generated by algorithms such as weighted summation. For example, the temperature, position, vibration and other parameters are calculated according to preset weights (such as temperature accounts for 60%, position accounts for 30%, and vibration accounts for 10%) to obtain a value of 0-100; K j Represents the jth local state value, i.e., the normalized value of the raw data collected in a single pass. For example, if the temperature collected in the third pass is 6°C, normalization (e.g., to the range 0-100) yields K3 = 70 (assuming 2°C corresponds to 0 and 8°C corresponds to 100). A location update delay of 10 minutes is normalized to K5 = 85 (assuming a 2-hour threshold interval corresponds to 100, with a 10-minute delay proportionally reduced).

[0107] Taking a blood sample as an example, in a state detection cycle of m=10, the local state value K collected at each time is j They are: 75, 80, 78, 82, 79, 85, 77, 76, 81, 74, and the overall state value of the sample is k = 80. The calculation process of the first control coefficient is:

[0108] Molecular part:

[0109] Denominator: m × k = 10 × 80 = 800

[0110] Calculation results:

[0111] This coefficient indicates that within the detection period, the average level of local state values ​​is close to the overall state value, with minimal fluctuations in sample state, thus requiring less adjustment to the logistics route. The system can decide whether to trigger the route optimization process based on preset coefficient thresholds (e.g., C>1 indicates significant fluctuations, requiring major adjustments; 0.8≤C≤1 indicates minor fluctuations, requiring no adjustments for now).

[0112] It should be noted that the standardization of local state values ​​is a key step to ensure the validity of the formula. For example, different types of detection parameters (temperature, time, vibration, etc.) need to be converted into numerical values ​​of uniform dimension through normalization or interval mapping to facilitate mathematical operations. In addition, the calculation formula of the first control coefficient can adjust the weight distribution method according to actual business needs. For example, a higher calculation weight can be given to the local state value of a key parameter (such as temperature), thereby more accurately reflecting the fluctuation of the core indicators of the sample.

[0113] The entire anomaly determination and coefficient calculation process, through automated comparison and mathematical modeling of preset thresholds, enables a quantitative assessment of the sample's transport status, providing a data basis for subsequent logistics route optimization. This mechanism avoids the subjectivity and lag inherent in manual judgment, improving the system's efficiency in identifying sample anomalies and its accuracy in control.

[0114] Example 4:

[0115] In the second control coefficient calculation and path deviation value optimization phase, the system implements dynamic routing adjustments through time analysis and mapping matching. The specific process is as follows:

[0116] The second control module obtains the actual sample transportation time data and standard time data for each sample return instruction within the status detection cycle. The actual time data is extracted from the logistics tracking system, recording the complete time (unit: hours) from the user submitting the return instruction on the mobile terminal to the sample being delivered to the receiving center; the standard time data is the system's preset industry benchmark value or historical average time, configured according to factors such as sample type and transportation distance. For example, the standard time for regular samples in the same city is 24 hours, and the standard time for cross-city cold chain samples is 48 hours.

[0117] The system calculates the transport time deviation value (i.e. the second control coefficient) using the following formula:

[0118]

[0119] Among them, D represents the second regulation coefficient, which is used to quantify the overall deviation degree of transportation timeliness, and its value range is usually from 0.5 to 1.5 (which can be adjusted according to the business scenario); p represents the number of the sample return instruction, starting from 1 and increasing sequentially, corresponding to the 1st to the qth return tasks within the status detection period; q represents the total number of sample return instructions, that is, the number of return tasks within the status detection period; s p represents the actual duration data of the pth return instruction. For example, the actual duration of a certain instruction is 30 hours; S p represents the standard duration data of the pth return instruction. For example, the standard duration of the corresponding instruction is 25 hours.

[0120] Taking the example of 2 return instructions submitted by a certain user within the status detection period: The first actual duration s1 = 24 hours, and the standard duration S1 = 20 hours; the second actual duration s2 = 30 hours, and the standard duration S2 = 25 hours. Then the calculation process is as follows:

[0121] The timeliness ratio for the first time:

[0122] The timeliness ratio for the second time:

[0123] Sum and average:

[0124] This coefficient indicates that the average timeliness deviation of the two return tasks is 20%, and the proportion of the actual duration exceeding the standard duration is consistent.

[0125] The path optimization module calculates the logistics path deviation value according to the first regulation coefficient C and the second regulation coefficient D. The formula is: X = Y × C × D

[0126] Among them, X represents the logistics path deviation value, which is used to measure the deviation degree of the current path planning from the ideal state; Y represents the standard path deviation reference value, which is a dimensionless constant preset by the system (usually taking 1) and is used to unify the calculation dimension; C and D are the first regulation coefficient and the second regulation coefficient respectively, reflecting the sample state stability and the transportation timeliness deviation.

[0127] After calculating X, the system queries the preset path regulation mapping table. This mapping table contains multiple logistics path deviation interval values and the corresponding logistics distribution path threshold ranges. For example: when X ≤ 0.5, the threshold range is ±5% (that is, the path adjustment amplitude is small); when 0.5 < X ≤ 1, the threshold range is ±10% (that is, the path needs medium adjustment); when X > 1, the threshold range is ±15% (that is, the path needs large adjustment).

[0128] The system matches the calculated result of X to the corresponding target deviation interval value. For example, if X = 1.2, it matches the "X>1" interval, with a corresponding threshold range of ±15%. The system then extracts the logistics allocation path threshold range corresponding to this interval from the path control mapping table and adjusts the parameters of the path planning algorithm based on this range, such as expanding the search radius of optional paths and increasing the weight of real-time traffic data. It then recalculates and generates an optimized logistics allocation path.

[0129] It's important to note that the interval divisions and threshold ranges within the routing control mapping table can be customized through the system management interface to accommodate the specific needs of different logistics networks. For example, for samples with extremely high timeliness requirements, the threshold range corresponding to "X>1" can be narrowed to ±10% to trigger a more sensitive routing adjustment mechanism. The entire process, through a combination of mathematical modeling and pre-set rules, automates the derivation from timeliness data to route optimization, ensuring that the system can dynamically adjust routing strategies based on actual transportation performance, improving the efficiency and reliability of the sample return process.

[0130] Example 5:

[0131] In advanced scenarios of path generation and optimization, the system combines real-time dynamic data to implement topology correction and path updates. The specific process is as follows:

[0132] After generating a sample logistics distribution route, the system continuously obtains real-time traffic status data and logistics node load data through a preset data interface. Real-time traffic status data covers road traffic conditions (such as unimpeded, slow traffic, congestion, and temporary closures), weather conditions (such as the impact of heavy rain, strong winds, and high temperatures on road traffic), and traffic control information (such as detours during construction and accident site diversion plans). These data are transmitted to the system in real time via a traffic information service platform or IoT devices. Logistics node load data includes the package handling volume, remaining processing capacity, and equipment operating status (such as conveyor belt utilization and storage space occupancy) of logistics nodes at all levels (such as sorting centers and express delivery stations). These data are updated in real time by the logistics company's internal management system.

[0133] The system dynamically corrects the logistics network topology map based on the above real-time data. For traffic status data, if a road section is detected to be in a congested state, the system will adjust the attribute parameters of the road section in the topology map, such as adjusting its speed from 60 km / h to 20 km / h, or marking it as "need to detour" and temporarily disconnecting the road section from the adjacent nodes; if a road closure is encountered, the road section will be directly removed from the topology map. For logistics node load data, when the processing volume of a sorting center exceeds the preset threshold (such as 80% of the maximum production capacity), the system will reduce the priority of the node in path planning, such as reducing its probability of being a transit node, and increasing the connection weight of adjacent low-load nodes; if a courier station equipment failure causes a decrease in processing efficiency, the system will correspondingly extend the parcel sorting time parameters of the node.

[0134] Based on the revised logistics network topology, the system re-executes the path generation logic, specifically including: starting from the sample collection location node, combined with the real-time revised topology structure, traversing all reachable logistics nodes, and calculating the comprehensive indicators of each possible path (such as total transportation distance, estimated time, node processing risk level). For example, the original path planning is "collection point → A sorting center → B sorting center → receiving center". If the A sorting center is expected to extend the processing time to 4 hours due to high load, and the adjacent C sorting center has a low processing volume and an estimated processing time of only 1 hour, the system will give priority to the alternative path of "collection point → C sorting center → B sorting center → receiving center". In this process, the system dynamically searches for the optimal path key coordinate point combination through graph algorithms (such as Dijkstra algorithm) to ensure that the newly generated path can avoid real-time risk points and optimize transportation efficiency.

[0135] During the path update process, the system synchronously adjusts the path weight coefficient, which comprehensively reflects the real-time reliability and efficiency of the path. The calculation of the weight coefficient is based on the attribute parameters of each segment of the path. For example, the shorter the transportation distance, the higher the weight; the closer the estimated time is to the standard time, the higher the weight; the more low-load nodes are passed, the higher the weight. The system embeds the adjusted weight coefficient into the path generation algorithm so that subsequent path planning can continue to be optimized based on the latest network status. For example, if a path increases the distance by 5 kilometers due to detouring around a low-load node, but the total estimated time is reduced by 2 hours, its weight coefficient may still be higher than the original path, and it will be given priority in subsequent calculations.

[0136] This mechanism enables the system to dynamically adapt to changes in the transportation environment through real-time data-driven topology correction and route optimization. When traffic conditions suddenly deteriorate or logistics node loads become abnormal, the system automatically triggers route adjustments without human intervention, avoiding sample transport delays or node backlogs caused by static route planning. The entire process relies on the efficient collection of real-time data, dynamic modeling of topology maps, and rapid algorithm calculations to achieve intelligent and dynamic updates of sample return routes, ensuring that samples can be transported safely and efficiently.

[0137] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0138] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A sample return system based on mobile application, characterized in that: include: An information collection module is used to obtain a sample return instruction and input user sample basic information data according to the sample return instruction, wherein the user sample basic information data includes sample collection location data and sample type identification data; A path generation module is used to generate a sample logistics distribution path based on the sample collection location data and the sample type identification data; The status monitoring module is used to build a status detection cycle, obtain the sample location update data and sample integrity verification data submitted by the user during the status detection cycle, and mark them as the overall status value of the sample; An abnormality determination module, used to determine whether the overall state value of the sample meets the first preset condition, and if not, mark the sample as an abnormal state period; A first control module, configured to obtain a first control coefficient according to the overall state value of the sample; The second control module is used to obtain the user's sample transportation timeliness data and sample handover verification data within the status detection period, and mark them as the second control coefficient; a path optimization module, configured to calculate a logistics path deviation value based on the first control coefficient and the second control coefficient, and adjust a threshold range of a sample logistics allocation path based on the logistics path deviation value; The execution module is used to control the execution of the sample return process according to the adjusted threshold range.

2. The mobile application-based sample return system according to claim 1, characterized in that: The steps of obtaining a sample return instruction and inputting user sample basic information data according to the sample return instruction include: Obtain the return requirement type of the mobile application, and generate multiple sample return instructions based on the return requirement type; input the sample collection location data and the corresponding sample type identification data according to each sample return instruction; summarize the sample collection location data and sample type identification data into user sample basic information data.

3. The mobile application-based sample return system according to claim 1, characterized in that: The step of generating a sample logistics distribution path according to the sample collection location data and the sample type identification data includes: Converting sample collection location data and sample type identification data into logistics node coordinate data; Establish regional logistics network topology map based on logistics node coordinate data; Extract the sample collection location node and the sample receiving center node according to the logistics network topology map; Determine a plurality of initial path key coordinate points from the sample collection location nodes; Determine multiple target path key coordinate points from the sample receiving center node; A plurality of initial path key coordinate points and a plurality of target path key coordinate points are combined into a sample logistics distribution path.

4. The mobile application-based sample return system according to claim 1, characterized in that: The steps of establishing a status detection cycle and obtaining sample location update data and sample integrity verification data submitted by users within the status detection cycle include: Obtain the number of times the user submits the sample return instruction; Get the preset submission threshold number; Determine whether the number of submissions exceeds the submission threshold. If the number of submissions exceeds the submission threshold, mark the current time as the detection start time; Get the status detection interval duration; Determine the detection end time based on the detection start time and status detection interval; Delimit the status detection cycle based on the detection start time and detection end time; Obtain the sample location update data and sample integrity verification data submitted by the user during the status detection cycle, and mark them as the overall status value of the sample.

5. The mobile application-based sample return system according to claim 1, characterized in that: The step of determining whether the overall state value of the sample meets the first preset condition, and if not, marking the sample as being in an abnormal state period, includes: Get the sample status threshold range; Determine whether the overall state value of the sample exceeds the sample state threshold range; If the overall status value of the sample exceeds the sample status threshold range, the sample transportation is determined to be abnormal and marked as a sample abnormal status period; If the overall status value of the sample does not exceed the sample status threshold range, the sample transportation is determined to be normal.

6. The mobile application-based sample return system according to claim 1, characterized in that: The step of obtaining the first control coefficient according to the overall state value of the sample includes: Obtain the local state value of the sample position update data of each sample return instruction completed by the user within the state detection cycle; The first control coefficient is calculated based on the multiple local state values ​​and the overall state value of the sample, wherein the calculation formula is: In the formula, C represents the first control coefficient, j represents the number of the local state value, m represents the total number of local state values, k represents the overall state value of the sample, and K j Represented as the jth local state value.

7. The mobile application-based sample return system according to claim 1, characterized in that: The step of obtaining the user's sample transportation timeliness data and sample handover verification data within the status detection period includes: Obtain the actual sample transportation time data for each sample return instruction completed by the user during the status detection cycle; Obtain the standard sample transportation time data for each sample return instruction completed by the user; The transportation time deviation value of each sample return instruction completed by the user within the status detection period is calculated based on multiple actual time data and multiple standard time data, and marked as the second control coefficient. The calculation formula is: Where D is the second control coefficient, p is the number of the sample return instruction, q is the total number of sample return instructions, and s is the total number of sample return instructions. p It is represented as the actual duration data of the p-th sample return instruction, S p It represents the standard duration data of the p-th sample return instruction.

8. The mobile application-based sample return system according to claim 1, characterized in that: The step of calculating the logistics path deviation value according to the first control coefficient and the second control coefficient includes: Get the standard path deviation benchmark value; The logistics path deviation value is calculated according to the first control coefficient and the second control coefficient, wherein the calculation formula is: X=Y×C×D Where X represents the logistics path deviation value, Y represents the standard path deviation reference value, C represents the first control coefficient, and D represents the second control coefficient; Obtaining a path control mapping table, wherein the path control mapping table includes a plurality of logistics path deviation interval values ​​and a logistics allocation path threshold range corresponding to each interval value; Match the target deviation interval value according to the logistics path deviation value; According to the target deviation interval value, the corresponding logistics allocation path threshold range is extracted from the path control mapping table.

9. The mobile application-based sample return system according to claim 3, characterized in that: After the step of generating the sample logistics distribution path, the method further includes: Get the transport priority parameter corresponding to the sample type identification data; Adjust the weight coefficient of the sample logistics allocation path according to the transportation priority parameter; The optimized logistics distribution path is regenerated based on the adjusted weight coefficients.

10. The mobile application-based sample return system according to claim 9, characterized in that: The step of generating the sample logistics distribution path further includes: Obtain real-time traffic status data and logistics node load data; Dynamically modify the logistics network topology based on traffic status data and node load data; The key coordinate point combination of the sample logistics distribution path is updated based on the revised logistics network topology graph.

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