Infectious disease department nursing information management system and method

Abstract

The invention relates to the technical field of medical information management, in particular to an infectious disease department nursing information management system and method, and the system comprises a task triggering module, a risk assessment module, a path analysis module, an equipment interaction module and a feedback updating module. According to the method, task nodes and a path set are dynamically associated through a node mapping matrix, the contact density, the environment killing frequency and the biological index concentration are monitored in real time, a path risk value is generated through double-item maximum value operation, and a model is constructed to achieve dynamic matching of path planning and environment variables. Eliminating redundant paths through difference operation to generate a sequence with an optimal reachable rate; mapping risk levels to an equipment interface by applying a color separation rendering mechanism; forming spatial visual guidance; introducing a flow control factor to quantify a cross infection probability; a node and biological safety index closed-loop feedback mechanism is established, and real-time updating of path decisions along with environment data is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical information management, and in particular to an infectious disease department nursing information management system and method. BACKGROUND

[0002] The technical field of medical information management includes functions such as collection, classification, storage, transmission and sharing of various types of information in medical activities. Its core content involves systematic processing of multi-dimensional data such as medical record diagnosis data, nursing records, test results, inspection results, drug information and patient basic information. This technical field usually relies on computer software and hardware platforms to build information systems to realize standardized input, efficient query and cross-system interaction of clinical data. Medical information management covers multiple application scenarios such as outpatient, inpatient, inspection, radiation and nursing, and its development direction focuses on improving the structured level of data, enhancing the interconnection and intercommunication capabilities of data, and improving the information security protection mechanism.

[0003] The infectious disease department nursing information management system refers to an information processing scheme for infectious disease specialist nursing scenarios. It mainly manages nursing records, infectious disease patient sign monitoring, isolation management, nursing assessment and medication information, etc. The system sets nursing workflow standards, configures infectious disease specialist nursing items, builds patient health data electronic forms, establishes a dynamic health status record mechanism, and designs nursing level division and work time reminder functions in combination with the characteristics of infectious diseases to realize comprehensive collection and classification of nursing information. At the same time, in combination with the identity recognition mechanism and the permission control rules, the association and traceability of data input behavior and user behavior are ensured, the patient data is tracked and accessed in the time dimension through the database logical structure, and the data synchronization and sharing are realized through the existing information systems in the hospital by means of directional information transmission.

[0004] The prior art adopts a static nursing process configuration, and its path planning relies on preset rules and historical data, without establishing a dynamic association mechanism between environmental variables and path selection. When the nursing task is executed, dynamic parameters such as disinfection frequency and biological index concentration cannot be obtained in real time, resulting in a lack of path risk assessment dimension. For example, in the daily nursing of infectious disease areas, the system only assigns tasks according to the established route, and cannot identify the abnormal biological index in a certain area due to sudden pollution, and still assigns nursing personnel to enter the high-risk area. The existing data interaction mechanism is limited to basic information sharing, and a fusion model of nursing operation and real-time environmental data has not been constructed, so that path optimization lacks multi-source data support. The nursing level division function triggers reminders based on fixed threshold values, and cannot dynamically adjust the task priority according to the personnel contact density, which may delay the high-risk nursing response. The data traceability mechanism focuses on operation record traceback, and does not predict potential risk points before path execution, resulting in a time difference between nursing behavior and risk control. Such static data processing mode is prone to disconnection between path planning and real-time environment, increasing the probability of cross infection. SUMMARY

[0005] The object of the present application is to solve the drawbacks existing in the prior art and propose an infectious disease department nursing information management system and method.

[0006] In order to achieve the above object, the present application adopts the following technical scheme: an infectious disease department nursing information management system comprises:

[0007] A task triggering module is configured to obtain a path set associated with a current task node through a node mapping matrix, call a path flow limiting factor to perform screening on the path set, generate an initial path set and transmit the initial path set to a risk assessment module;

[0008] The risk assessment module is configured to monitor personnel contact density, environmental disinfection frequency and biological index concentration in real time based on the initial path set, perform double maximum value operation on path start and end nodes to generate an instant risk value, perform subtraction operation on the instant risk value and a preset threshold difference, assign a path label level to a path exceeding the threshold and transmit the path label level to a path analysis module;

[0009] The path analysis module is configured to receive the path label level, call a flow control factor parameter and a regional cross-infection point count item to generate a single path reachability value, perform difference operation on the single path reachability value after sorting, prune paths and generate a recommended path sequence and transmit the recommended path sequence to a device interaction module;

[0010] The device interaction module is configured to receive the recommended path sequence, call the path label level to perform color rendering, synchronize the path label level and the recommended path sequence to a nursing patrol auxiliary device interface.

[0011] As a further scheme of the present application, the initial path set specifically comprises a node mapping relationship, a path flow limiting factor and a screening condition parameter, the instant risk value comprises a personnel contact density maximum value, an environmental disinfection frequency maximum value and a biological index concentration threshold difference, the path label level specifically refers to a risk overflow identifier, a level classification code and a dynamic threshold deviation amount, and the recommended path sequence comprises a single path reachability value, a flow control factor parameter, a regional cross-infection point count item and a path sorting difference value, and the path sequence after color rendering specifically comprises a color coding rule, a nursing patrol device interface parameter and path label synchronization data.

[0012] As a further scheme of the present application, the task triggering module comprises:

[0013] The path mapping submodule is configured to obtain an adjacent path identifier of the current task node in the node mapping matrix, extract a capacity parameter and a connection direction of an associated path, calculate a path topology weight and generate an adjacent path topology table.

[0014] The path flow restriction factor is called by the restriction screening submodule, and based on the difference between the capacity parameter and the real-time load in the adjacent path topology table, a formula is used:

[0015]

[0016] The path dynamic adaptation value is obtained by operation, the adaptation value is compared with a preset threshold value, path identifiers meeting the condition are screened, and a restriction filtered path set is generated;

[0017] Wherein, Γ represents the path dynamic adaptation value, C k represents the capacity upper limit of the kth path, L k represents the real-time load value of the kth path, D k represents the matching degree of the kth path direction and the transmission direction of the current task flow, T k represents the topology weight of the kth path, and epsilon represents the anti-zero interference constant, which is a minimum positive number for avoiding zero denominator, k is the path number, and n is the total number of paths associated with the current task node;

[0018] The set integration submodule combines the priority labels and transmission delay parameters of the path identifiers in the restriction filtered path set, arranges in ascending order of delay, removes the repeated paths, and generates an initial path set.

[0019] As a further scheme of the application, the risk assessment module comprises:

[0020] The path monitoring submodule generates a path dynamic parameter set based on the initial path set, real-time collection of the personnel contact density of the starting point and the ending point of each path, recording of the time distribution of the environmental disinfection frequency, monitoring of the dynamic change of the biological index concentration, integration of the three data into a structured set, and generation of a path dynamic parameter set;

[0021] The risk quantification submodule calls the path dynamic parameter set, extracts the personnel contact density of the starting point and the ending point, the environmental disinfection frequency and the biological index concentration, and uses a formula:

[0022]

[0023] The product of the maximum value of the personnel contact density of the starting point and the ending point and the maximum value of the environmental disinfection frequency is calculated to generate an instant risk value;

[0024] Wherein, R t represents the product of the maximum value of the personnel contact density of the starting point and the ending point and the maximum value of the disinfection frequency, P s represents the measured value of the personnel contact density of the starting point node, P e represents the measured value of the personnel contact density of the ending point node, F s represents the statistical value of the environmental disinfection frequency of the starting point node in unit time, and F eThe environmental disinfection frequency statistical value of the path end node per unit time, B s The biological index concentration monitoring value of the path start node, B e The biological index concentration monitoring value of the path end node, φ represents a minimum positive constant introduced to avoid a zero denominator;

[0025] The label decision submodule calls the instantaneous risk value, performs difference operation on the preset risk threshold, determines whether the difference is greater than zero, and outputs the path label level according to the range division of the absolute value of the difference for the path exceeding the threshold.

[0026] As a further scheme of the application, the path analysis module comprises:

[0027] The reachable rate generation submodule receives the path label level, calls the flow control factor parameter and the regional cross-infection point count item, and adopts the formula:

[0028]

[0029] The operation obtains the initial value of the path reachable rate, and simultaneously generates a path reachable rate value set based on the priority weighting of the initial value on the path label level;

[0030] Wherein, R pq The reachable rate value of the pth to qth path, H k The flow control factor parameter of the kth path, S c The cross-infection point count item of the cth region, Φ w The flow fluctuation coefficient of the wth path, V n The node density index of the nth path, B j The density balance threshold of the jth region, Lambda d The dynamic interference correction item of the dth path;

[0031] The difference pruning submodule calls the path reachable rate value set, generates a reachable rate sorting table in descending order of numerical value, calculates the reachable rate difference of adjacent paths one by one, compares the difference with the dynamic pruning threshold, deletes the low-value path with a difference exceeding the threshold, and generates a pruned path set;

[0032] The sequence construction submodule calls the pruned path set, extracts the node stress factor of the equipment and the path label level, matches the stress factor according to the priority of the label level, and generates a recommended path sequence.

[0033] As a further scheme of the application, the device interaction module comprises:

[0034] The path receiving verification submodule detects the correspondence between the number of nodes of the recommended path sequence and the path label level, calls a sequence integrity checking rule, compares the consistency of the number of nodes and the number of label levels, generates an abnormal marking signal when the numbers are inconsistent, and generates a path verification value according to the checking result;

[0035] The label color mapping submodule, based on the path verification value, calls a priority division table of the path label level, corresponds red, yellow and green three color parameters to different priorities, extracts RGB channel values of the label level sequence, binds node coordinates, and generates a color mapping coefficient;

[0036] The interface synchronization execution submodule calls the effective node sequence of the path verification value and the RGB parameter set of the color mapping coefficient, converts path coordinates into interface grid coordinates, fills grid units according to color rules, detects the adaptation degree of coordinates to screen resolution and scales adjustment, and generates interface synchronization parameters completely matched with the nursing patrol auxiliary equipment.

[0037] As a further scheme of the present application, the system further comprises:

[0038] The feedback updating module is configured to collect path selection data, task completion time and node risk fluctuation parameters through the nursing patrol auxiliary equipment, replace the infection risk parameters of the path set with the node risk fluctuation parameters, and return the updated parameters to the task triggering module.

[0039] The updated parameters include node risk fluctuation parameters, path selection data, task completion time records and infection risk replacement indexes.

[0040] As a further scheme of the present application, the feedback updating module comprises:

[0041] The trajectory collection submodule collects path selection data and task completion time output by the nursing patrol auxiliary equipment, extracts node positions with a stay duration exceeding a preset duration threshold in a path trajectory, calculates a moving time consumption average between adjacent nodes, integrates the path trajectory and the time consumption average to generate a path trajectory data set.

[0042] The risk updating submodule calls node risk fluctuation parameters and node positions in the path trajectory data set, replaces infection risk parameters in the path set with corresponding node risk fluctuation parameters, screens nodes with a fluctuation value exceeding a preset infection reference value multiple, calculates a whole risk average of the replaced path, and generates risk updating parameters.

[0043] The parameter returning submodule integrates task completion time and node risk fluctuation parameters based on the moving time consumption average in the path trajectory data set and the risk updating parameters, constructs a parameter transmission queue, returns queue data according to an interface format of the task triggering module, and generates updated parameters.

[0044] An infectious disease department nursing information management method is executed based on the infectious disease department nursing information management system, and includes the following steps:

[0045] S1: Obtain the path set associated with the current task node through the node mapping matrix, call the path flow limiting factor parameter to perform screening processing on the path set, the screening condition is the bidirectional constraint relationship between the path length and the preset flow threshold, and generate an initial path set;

[0046] S2: Based on the initial path set, real-time personnel density data, environmental disinfection frequency data and biological index concentration data are monitored in real time, and the maximum value operation of the path start and end nodes is performed to generate an instant risk value, the instant risk value is subjected to difference operation with the preset risk threshold, and the path label level is assigned to the path whose difference value is greater than zero;

[0047] S3: Input the path label level into the reachability analysis model, combine the path flow limiting factor parameter and the number of regional cross-infection points to perform single-path reachability calculation, perform descending arrangement on the calculation result through the sorting algorithm, perform pruning processing based on the adjacent path reachability difference, and generate a recommended path sequence;

[0048] S4: Call the recommended path sequence, perform color rendering processing on the path according to the path label level, and synchronously transmit the rendered path data and label data to the interactive interface of the nursing patrol auxiliary equipment;

[0049] S5: Collect the path selection record, task execution time and node risk fluctuation data of the nursing patrol auxiliary equipment, call the parameter replacement algorithm to perform replacement processing on the node risk fluctuation data and the path infection risk parameter, and return the updated parameter to the node mapping matrix.

[0050] Compared with the prior art, the advantages and positive effects of the present application are that:

[0051] In the application, the task nodes and path sets are dynamically associated through the node mapping matrix, the real-time monitoring system collects personnel contact density, environmental disinfection frequency and biological index concentration data, and the path risk quantization value is generated by combining the double maximum value operation. This way establishes a mathematical correlation model between the nursing path planning and the real-time environmental variables, eliminates redundant paths through difference operation, and constructs an optimal sequence of reachable rate. The color rendering mechanism maps the risk level to the device interface to form a spatial dimension risk visualization guide. Traditional methods rely on fixed nursing processes and manual experience, and do not include environmental disinfection status and biological index fluctuations in path decision-making, resulting in a disconnect between the execution path and real-time risk. This scheme quantifies the probability of cross-infection in the region through the flow control factor, optimizes the path sequence with a dynamic pruning algorithm, and reduces the invalid exposure time of nursing staff in contaminated areas. The nursing task nodes and biological safety indicators form a closed-loop feedback, so that the path decision is updated in real time with environmental data, improving the accuracy of personnel flow and resource scheduling in infectious disease care scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The system flowchart of the application is shown in the figure;

[0053] Figure 2 The task trigger module flowchart of the application is shown in the figure;

[0054] Figure 3 The risk assessment module flowchart of the application is shown in the figure;

[0055] Figure 4 The path analysis module flowchart of the application is shown in the figure;

[0056] Figure 5 The device interaction module flowchart of the application is shown in the figure;

[0057] Figure 6 The feedback update module flowchart of the application is shown in the figure. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0059] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0060] Embodiment one

[0061] Please refer to Figure 1 The present application provides a technical solution: an infectious disease nursing information management system comprises:

[0062] The task triggering module is configured to obtain a path set associated with the current task node through a node mapping matrix, call a path flow limiting factor to perform screening on the path set, generate an initial path set and transmit it to the risk assessment module.

[0063] The risk assessment module is configured to monitor the personnel contact density, the environmental disinfection frequency and the biological index concentration in real time based on the initial path set, perform a double maximum value operation on the start and end nodes of the path to generate an instant risk value, perform a subtraction operation on the instant risk value and the preset threshold difference, assign a path label level to the path that exceeds the threshold value and transmit it to the path analysis module.

[0064] The path analysis module is configured to receive the path label level, call a flow control factor parameter and a regional cross-infection point count item to generate a single path reachability value, perform a difference operation after sorting the single path reachability value, prune the path and generate a recommended path sequence and transmit it to the device interaction module.

[0065] The device interaction module is configured to receive the recommended path sequence, call the path label level to perform color rendering, synchronize the path label level and the recommended path sequence to the nursing patrol auxiliary device interface.

[0066] The feedback updating module is configured to collect path selection data, task completion time and node risk fluctuation parameters through the nursing patrol auxiliary device, call the node risk fluctuation parameters to replace the infection risk parameters of the path set, and return the updated parameters to the task triggering module.

[0067] The initial path set is specifically a node mapping relationship, a path flow restriction factor, a screening condition parameter, the real-time risk value includes a maximum personnel contact density, a maximum environmental disinfection frequency, a biological index concentration threshold difference, and a path label level specifically refers to a risk overflow identifier, a level classification code, and a dynamic threshold deviation amount, the recommended path sequence includes a single path reachability value, a flow control factor parameter, a regional cross-infection point count item, and a path sorting difference value, and the path sequence after color rendering is specifically a color coding rule, a nursing patrol device interface parameter, and path label synchronization data, and the updated parameters include a node risk fluctuation parameter, path selection data, a task completion time record, and an infection risk replacement indicator.

[0068] Please refer to Figure 2 , the task triggering module includes:

[0069] The path mapping submodule obtains the adjacent path identifier of the current task node in the node mapping matrix, extracts the capacity parameter and connection direction of the associated path, calculates the path topology weight, and generates an adjacent path topology table.

[0070] Firstly, the system obtains the adjacent path identifier of the current task node in the node mapping matrix, which is stored in the form of a two-dimensional array, specifically showing a floor layout including five key nursing areas (node 1 to node 5), and the mapping relationship is shown in Table 1. The value 1 in the matrix indicates that there is a direct physical path between the nodes, and 0 indicates that there is no direct physical path. It is assumed that the current task starts at node 3, the path mapping submodule retrieves the 3rd row and the 3rd column of the matrix, and identifies that the nodes directly connected to node 3 are node 2 and node 4, so it is determined that there are paths 3-2 and 3-4.

[0071] Next, the capacity parameter C k and the connection direction D k of the two associated paths are extracted. The capacity parameter C k is defined as the maximum amount of nursing resources that can pass through the path per unit time, specifically the number of standard size equipment trolleys. Through analysis of historical traffic data, the design capacity of path 3-2 is C1=10 times / minute, and the capacity of path 3-4 is C2=8 times / minute due to the slightly narrower passage. The connection direction is determined based on the preset one-way or two-way traffic rules in the floor. The current task flow direction requires departure from node 3, according to the electronic map data, path 3-2 allows 3->2 traffic, and path 3-4 allows 3->4 traffic, so the direction D k of the two paths is considered to be completely matched with the task flow. In order to facilitate calculation, the direction matching degree needs to be quantified: setting the complete match as 1, the partial match (such as reverse traffic requiring significant detour) as 0.5, and the complete mismatch (such as the opposite direction of a one-way path) as 0. Therefore, the quantified direction matching degrees of the two paths are D1=1 and D2=1, respectively.

[0072] Subsequently, the topological weight T of the path is calculated k . The weight T k is a dimensionless value that comprehensively reflects the physical complexity of the path, and its setting aims to quantify the ease of passage of the path. Its calculation refers to multiple physical characteristics of the path, mainly including path length, number of turns, and ground slope. First, these characteristics are scored: the path length scoring rule is 1 point for 0-5 meters, 2 points for 5-10 meters, and 3 points for more than 10 meters; the number of turns scoring rule is 0 points for 0 times, 1 point for 1-2 times, and 2 points for 3 times or more; the ground slope scoring rule is 0 points for flat (slope <1%), 1 point for gentle slope (1%-3%), and 2 points for steep slope (>3%). Then, weights are assigned according to the importance of each factor on the passage, with length weight 0.5, turn weight 0.3, and slope weight 0.2. Path 3-2 is relatively direct, with a length of 6 meters (score 2), 1 turn (score 1), and flat ground (score 0), and its topological weight T1 = 0.5 x 2 + 0.3 x 1 + 0.2 x 0 = 1.0 + 0.3 + 0 = 1.3. Path 3-4 requires a 90-degree corner, with a length of 8 meters (score 2), 1 turn (score 1), and flat ground (score 0), and its topological weight T2 = 0.5 x 2 + 0.3 x 1 + 0.2 x 0 = 1.0 + 0.3 + 0 = 1.3. This weight value is negatively related to the priority of path selection, and the lower the weight, the more preferred the path.

[0073] Finally, the data extracted and calculated above is integrated to generate an adjacent path topology table to summarize the key parameters of each adjacent path from node 3. The specific content is shown in Table 2.

[0074] Table 1 Example of node mapping matrix

[0075] Node Node 1 Node 2 Node 3 Node 4 Node 5 Node 1 0 1 0 0 1 Node 2 1 0 1 0 0 Node 3 0 1 0 1 0 Node 4 0 0 1 0 1 Node 5 1 0 0 1 0

[0076] As shown in Table 1, the matrix clearly shows the direct connection relationship between the 5 key nursing area nodes in the example floor.

[0077] Table 2 Adjacent path topology table (based on node 3)

[0078]

[0079]

[0080] As shown in Table 2, this table provides detailed basic data for paths P32 and P34 from node 3 for the subsequent screening module, including their passage capacity, direction compliance, and physical complexity evaluation.

[0081] The path flow restriction factor is called by the restriction screening submodule, and based on the difference between the capacity parameter in the adjacency path topology table and the real-time load, the formula is:

[0082]

[0083] The path dynamic adaptation value is obtained by operation, and the adaptation value is compared with the preset threshold value to screen the path identifiers meeting the condition to generate a restricted filtered path set;

[0084] Where Γ represents the path dynamic adaptation value, C k represents the capacity upper limit of the kth path, L k represents the real-time load value of the kth path, D k represents the matching degree of the kth path direction and the transmission direction of the current task flow, T k represents the topology weight of the kth path, and ε represents the anti-zero interference constant, which is the minimum positive number to avoid zero denominator, k is the path number, and n is the total number of paths associated with the current task node;

[0085] The path flow restriction factor is called by the system, which reflects the current global resource state or temporary regulation information. In this example, no special global restriction is triggered. Then, the capacity parameter C k in the adjacency path topology table (see Table 2) generated by the previous module is operated with the real-time load L k of the corresponding path. The real-time load L k is obtained by real-time monitoring by sensors (such as infrared counters or cameras combined with image recognition algorithms) deployed on each path, representing the actual device cart traffic on the current path. At the current time, the monitoring data shows that the real-time load L1 of path P32 is 7 times / minute, and the real-time load L2 of path P34 is 6 times / minute.

[0086] The system uses the path dynamic adaptation value formula to evaluate the immediate adaptability of each adjacency path. This formula aims to comprehensively consider the remaining capacity, direction compliance, and physical complexity of the path. Where k is the path number, the current node 3 is associated with n=2 paths, i.e. k takes values 1 (corresponding to P32) and 2 (corresponding to P34). C k is the capacity upper limit of the kth path (C1=10, C2=8 times / minute), L k is the real-time load value of the kth path (L1=7, L2=6 times / minute), D k is the quantitative direction matching degree (D1=1, D2=1), and T kThe topology weight is unitless (T1 = 1.3, T2 = 1.3). ∈ is a zero interference constant, which is set to avoid the denominator zero or near zero in the calculation, to ensure numerical stability, and its value is set to a small positive number according to the system operation accuracy requirement, and ∈ = 0.001 is selected. The Γ calculated by the formula k is an adaptive evaluation value without unit.

[0087] For path P32 (k = 1): Γ1≈0.3·0.87672, Γ1≈0.2630;

[0088] For path P34 (k = 2): Γ2≈0.25·0.87672, Γ2≈0.2192;

[0089] The dynamic adaptation values of paths P32 and P34 obtained by operation are Γ1≈0.2630 and Γ2≈0.2192 respectively. Next, compare these adaptation values with the preset dynamic adaptation threshold to filter out the paths that meet the basic traffic conditions. The preset threshold is set with reference to the lowest acceptable level of path adaptability, which is determined based on historical operation data analysis and nursing process efficiency requirements, and is intended to exclude paths with capacity close to saturation or extremely complex topology. The preset threshold is set to 0.15. If the adaptation value is lower than 0.15, it is considered that the path is not suitable for traffic at present. Compare Γ1≈0.2630 with 0.15, because 0.2630>0.15, path P32 meets the condition. Compare Γ2≈0.2192 with 0.15, because 0.2192>0.15, path P34 also meets the condition. Therefore, the path identifiers that meet the conditions include P32 and P34, and the restricted filtering path set {P32, P34} is generated.

[0090] The advantage of the formula is that it integrates the real-time available capacity ratio of the path and a factor that comprehensively reflects the direction suitability and traffic difficulty so as to dynamically and quantitatively evaluate the comprehensive adaptability of each path under the current load, task direction requirement and its own physical characteristics, and preferentially consider the path with sufficient remaining capacity, correct direction and smooth physical traffic.

[0091] The calculation result shows that under the current conditions, paths P32 and P34 both have basic traffic adaptability and will enter the next stage of evaluation.

[0092] The set integration submodule combines the priority tags and transmission delay parameters of the path identifiers in the restricted filtering path set, arranges them in ascending order of delay, removes duplicate paths, and generates an initial path set.

[0093] Integrate additional information of each path in the filtered path set {P32, P34} and perform preliminary sorting. First, attach a predefined priority label and an estimated transmission delay parameter to each path (P32, P34). The priority label is set according to the general importance of the path or the management strategy of a specific period, both P32 and P34 are labeled as "general" priority. The transmission delay parameter T delay refers to the time required for data or instructions to be transmitted in the system network supporting the path, real-time delay data is obtained through network monitoring tools, or estimated according to network topology and historical data. The network transmission delay T delay,1 of path P32 is measured to be 60 ms, and the transmission delay T delay,2 of path P34 is 65 ms.

[0094] The integrated information set is {(P32, priority: general, delay: 60 ms), (P34, priority: general, delay: 65 ms)}. Then, arrange the paths in the set in ascending order of transmission delay T delay . Compare the delay of P32, 60 ms, with the delay of P34, 65 ms, P32 has a lower delay and is arranged first. The sorted list is [(P32, delay: 60 ms, priority: general), (P34, delay: 65 ms, priority: general)].

[0095] Subsequently, the operation of eliminating duplicate paths is performed. Check the sorted list and confirm that the path identifiers P32 and P34 are both unique, and there are no duplicate paths. Finally, generate an initial path set containing paths and their related attributes that have been preliminarily filtered and sorted: {P32 (delay: 60 ms, priority: general), P34 (delay: 65 ms, priority: general)}. This set will be used as input for the subsequent risk assessment module.

[0096] Please refer to Figure 3 , the risk assessment module includes:

[0097] The path monitoring sub-module, based on the initial path set, collects the personnel contact density at the start and end of each path in real time, records the time distribution of environmental disinfection frequency, monitors the dynamic changes of biological indicators concentration, integrates the three data into a structured set, and generates a path dynamic parameter set;

[0098] Based on the initial path set {P32, P34}, start the real-time environmental parameter monitoring process for the two paths. The monitoring system performs data collection through the environmental sensor network deployed at the start of each path (node 3), the end of P32 (node 2), and the end of P34 (node 4).

[0099] The system collects the personnel contact density P s at the start and end of each path in real time.e This is achieved by installing wide-angle cameras on the ceiling combined with people counting algorithms that calculate the average number of people within a monitoring area. For path P32 (node 3 to node 2), in the current time period, the density of people P s1 = 6 people / m2 is monitored near the start node 3, and the density of people P e1 = 9 people / m2 is monitored near the end node 2. For path P34 (node 3 to node 4), the density of people P s2 = 6 people / m2 is monitored near the start node 3, and the density of people P e2 = 5 people / m2 is monitored near the end node 4.

[0100] At the same time, the time distribution of environmental disinfection frequency F s F e is recorded. This is done by automatically reading the operation logs of the intelligent disinfection devices connected to each area, counting the number of effective disinfections within a specified time window (set to the past hour). Node 3 records disinfection F s1 = F s2 = 1 times / hour. Node 2 records disinfection F e1 = 2 times / hour. Node 4 records disinfection F e2 = 1 times / hour.

[0101] In addition, the dynamic changes of biological indicator concentration B s B e are monitored. Using air quality monitoring stations fixedly installed in each node area, the concentration readings of specific biological aerosols or indicative microorganisms are collected. Node 3 (start point) monitors B s1 = B s2 = 20 units / m3. Node 2 (end point of P32) monitors B e1 = 12 units / m3. Node 4 (end point of P34) monitors B e2 = 25 units / m3. Here, "units" refers to the standard measurement unit of the specific biological indicators being monitored.

[0102] Finally, the above three types of real-time monitoring data (people density, disinfection frequency, biological indicator concentration) are integrated to construct a structured data set for each path. For path P32: {start density P s1 = 6, end density P e1 = 9, start disinfection frequency F s1 = 1, end disinfection frequency F e1 = 2, start biological concentration B s1 = 20, end biological concentration B e1 = 12} For path P34: {start density P s2= 6, end point density P e2 = 5, start point disinfection frequency F s2 = 1, end point disinfection frequency F e2 = 1, start point biological concentration B s2 = 20, end point biological concentration B e2 = 25} These sets constitute the path dynamic parameter set, providing basic data for subsequent risk quantification calculation.

[0103] The risk quantification submodule calls the path dynamic parameter set, extracts the start point and end point personnel contact density, environmental disinfection frequency, and biological index concentration, and uses the formula:

[0104]

[0105] The maximum value of the start point and end point personnel contact density is multiplied by the maximum value of the environmental disinfection frequency to generate the immediate risk value;

[0106] Where, R t represents the product of the maximum value of the start point and end point personnel contact density and the maximum value of the disinfection frequency, P s represents the measured value of the personnel contact density at the start point node of the path, P e represents the measured value of the personnel contact density at the end point node of the path, F s represents the statistical value of the environmental disinfection frequency per unit time at the start point node of the path, F e represents the statistical value of the environmental disinfection frequency per unit time at the end point node of the path, B s represents the biological index concentration monitoring value at the start point node of the path, B e represents the biological index concentration monitoring value at the end point node of the path, represents a small positive constant introduced to avoid a zero denominator;

[0107] Call the path dynamic parameter set to perform immediate risk assessment on each path (P32, P34) in the initial path set.

[0108] First, extract the start point and end point personnel contact density P s , P e (unit: person / m2), environmental disinfection frequency F s , F e (unit: times / hour), and biological index concentration B s , B e (unit: unit / m3) of each path. P32 data: P s1 = 6, P e1 = 9, F s1 = 1, F e1 = 2, B s1 = 20, B e1= 12P34 Data: P s2 = 6, P e2 = 5, F s2 = 1, F e2 = 1, B s2 = 20, B e2 = 25

[0109] Use the immediate risk value formula for calculation. This formula aims to quantify the risk of cross-infection that may be encountered through this path, integrating information on personnel activities, cleaning and disinfection measures, and environmental bioburden. Among them, R t is the calculated dimensionless immediate risk index. P′ s , P′ e , F′ s , F′ e , B′ s , B′ e are the parameter values after standardization. φ is a very small positive constant introduced to avoid a zero denominator, set according to the required calculation accuracy and the range of biological indicator concentrations, taking φ = 0.001.

[0110] Before substituting the original data into the formula, standardization is required to convert parameters with different physical meanings and value ranges into a unified comparable scale, using a 0 - 10 scoring system here. The conversion rules are based on the division of the common value ranges of each parameter in a specific environment (hospital floor) and the understanding of risk relevance: <00​​​​​​​​​​​​​​​​​​F>2 (high frequency): F' = 2 - (F - 2) x 1, lower limit is 0. (Note: the higher the frequency of killing, the lower the risk score corresponds)

[0120] Bio-concentration B (unit / m3) conversion rule B': (assuming normal range 10-30) * B < 10 (low): B' = (B / 10) x 3;

[0121] 10≤B≤30 (normal): B' = 3 + (B - 10) x (7 - 3) / (30 - 10) = 3 + (B - 10) x 4 / 20 = 3 + (B - 10) x 0.2;

[0122] B>30 (high): B' = 7 + (B - 30) x (10 - 7) / (50 - 30) = 7 + (B - 30) x 3 / 20 = 7 + (B - 30) x 0.15, upper limit is 10.

[0123] Apply conversion rule:

[0124] For P32: P' = 7 + (6 - 5) x 3 / 5 = 7 + 0.6 = 7.6; s1 (P s = 6) = 7 + (6 - 5) x 3 / 5 = 7 + 0.6 = 7.6;

[0125] P' = 7 + (9 - 5) x 3 / 5 = 7 + 4 x 0.6 = 7 + 2.4 = 9.4; e1 (P e = 9) = 7 + (9 - 5) x 3 / 5 = 7 + 4 x 0.6 = 7 + 2.4 = 9.4;

[0126] F' = 10 - 1 x 5 = 5; s1 (F s = 1) = 10 - 1 x 5 = 5;

[0127] F' = 5 - (2 - 1) x 3 = 5 - 3 = 2; e1 (F e = 2) = 5 - (2 - 1) x 3 = 5 - 3 = 2;

[0128] B' = 3 + (20 - 10) x 0.2 = 3 + 10 x 0.2 = 3 + 2 = 5; s1 (B s = 20) = 3 + (20 - 10) x 0.2 = 3 + 10 x 0.2 = 3 + 2 = 5;

[0129] B' = 3 + (12 - 10) x 0.2 = 3 + 2 x 0.2 = 3 + 0.4 = 3.4; e1 (B e = 12) = 3 + (12 - 10) x 0.2 = 3 + 2 x 0.2 = 3 + 0.4 = 3.4;

[0130] For P34: P' = 7.6; s2 (P s = 6) = 7.6;

[0131] P' = 9.4; e2 (P e= 5) = 3 + (5 - 2) x 4 / 3 = 3 + 3 x 4 / 3 = 3 + 4 = 7;

[0132] F' s2 (F s = 1) = 5;

[0133] F' e2 (F e = 1) = 5;

[0134] B' s2 (B s = 20) = 5;

[0135] B' e2 (B e = 25) = 3 + (25 - 10) x 0.2 = 3 + 15 x 0.2 = 3 + 3 = 6;

[0136] Substitute the formula to calculate R t :

[0137] For path P32 (k = 1):

[0138] R t1 ≈ 37.1453;

[0139] For path P34 (k = 2):

[0140] R t2 ≈ 37.9810;

[0141] The operation obtains the instant risk index R t1 ≈ 37.1453 of path P32 and the instant risk index R t2 ≈ 37.9810 of path P34. These two instant risk values are generated.

[0142] The advantage of the formula is that it enables meaningful operations on parameters of different natures (density, frequency, concentration) in the same framework through standardization, and comprehensively considers the differences in personnel density, environmental cleanliness, and potential biological pollution levels at both ends of the path to quantitatively evaluate the instantaneous cross-infection risk of passing through the path. The maximum value operation emphasizes the influence of the higher risk end of the path, and the difference in the denominator reflects the risk adjustment effect of environmental changes.

[0143] The calculation result provides a quantitative evaluation of the risk levels of the two candidate paths under the current environmental state.

[0144] The label decision submodule calls the real-time risk value, performs a difference operation with the preset risk threshold, and determines whether the difference is greater than zero. For paths exceeding the threshold, the label level is output according to the range of the absolute value of the difference.

[0145] The real-time risk index of each path R is received. t1 (P32)≈37.1453 and R t2 (P34)≈37.9810. The system compares these risk indices with the preset risk threshold to determine the risk level and output the path label.

[0146] The division criteria and corresponding thresholds of the risk level are determined according to the requirements of hospital infection control and historical risk data analysis, aiming to map continuous risk indices to discrete and easy-to-understand risk categories. The risk levels are set as follows: Low Risk: R t ≤20 Medium Risk: 20<R t ≤40 High Risk: R t >40

[0147] The calculated risk indices are compared with these thresholds: for path P32: R t1 ≈37.1453. Since 20<37.1453≤40, the value falls into the medium risk interval. For path P34: R t2 ≈37.9810. Since 20<37.9810≤40, the value also falls into the medium risk interval.

[0148] According to the comparison results, the system outputs the risk label level for path P32 as “Medium Risk”, and the risk label level for path P34 is also “Medium Risk”. These labels will be used for subsequent path analysis and decision-making.

[0149] Please refer to Figure 4 , the path analysis module includes:

[0150] The reachability generation submodule receives the path label level, calls the flow control factor parameter and the regional cross-infection point count item, and uses the formula:

[0151]

[0152] to calculate the initial value of the path reachability, and simultaneously prioritize the initial value based on the path label level to generate a set of path reachability values;

[0153] where R pq represents the reachability value of the pth to qth path, H k represents the flow control factor parameter of the kth type of path, and S ccross-infection point count term representing the c-th region, S w traffic fluctuation coefficient representing the w-th path, V n node density index representing the n-th path, B j density balance threshold value representing the j-th region, A d dynamic interference correction term representing the d-th path;

[0154] The risk label level of each path is received. The label levels of paths P32 and P34 are both "medium risk". The system calls a series of parameters to calculate the accessibility rate of each path, which is an index to comprehensively evaluate the feasibility of passing.

[0155] Firstly, the flow control factor H k is called, which is set according to the risk level of the path and reflects the degree of recommendation or restriction of the management strategy on the passing of the path with different risk levels. The setting of the factor value is based on the risk aversion principle, that is, the higher the risk, the stronger the passing restriction (the lower the factor value). The setting rule is: low-risk path H k = 1.0, medium-risk path H k = 0.8, and high-risk path H k = 0.6. Since P32 and P34 are both medium-risk, their H k values are both 0.8.

[0156] Then, the region cross-infection point count term S c is called. This parameter counts the number of fixed position points in the core region C that the path passes through, which are identified as positions that may significantly increase the risk of cross-infection, such as the entrance of an elevator hall with dense flow of people, the intersection of narrow passages, etc. These points are pre-marked by hospital infection experts according to the building layout and people flow mode. The region passed by path P32 contains S c1 = 2 such cross points. The region passed by path P34 contains S c2 = 1 such cross point.

[0157] Then, the traffic fluctuation coefficient V w is called. This coefficient is calculated by analyzing the standard deviation of the real-time monitoring data of the traffic (or device passing volume) of the path in the near future (set to the past 2 hours), and then normalized to obtain a quantitative index of the instability or congestion risk of the path. The calculation rule is: map the standard deviation to the range of 0-1, the larger the standard deviation, the higher the coefficient. The traffic fluctuation coefficient V w1 of path P32 is calculated as 0.3, and the traffic fluctuation coefficient V w2 of path P34 is calculated as 0.2.

[0158] The system adopts the path accessibility rate formula The initial value of the accessibility rate of a path is calculated. This formula integrates the risk management strategy, the inherent risk of the region, the stability of the flow, the matching degree of the path density and the region capacity, and the temporary interference factors. R pq is the calculated unitless accessibility value. H k is the flow control factor. S' c is the processed cross-infection point count. Φ w is the flow fluctuation coefficient. V' n is the standardized path node density index. B' j is the standardized region density balance threshold. Λ d is the dynamic interference correction term.

[0159] Before calculation, some parameters need to be processed to ensure the rationality of the calculation: the cross-infection point count S c needs to be converted S' c to avoid its value being too large to dominate the result and to coordinate with other 0-1 range parameters. The conversion rule is: S' c = S c / (S c +5). In this way, S' c will be between 0 and 1, and the growth rate will slow down as S c increases. S' c1 (S c1 =2) = 2 / (2+5) = 2 / 7 ≈ 0.2857 S' c2 (S c2 =1) = 1 / (1+5) = 1 / 6 ≈ 0.1667 The path node density index V n (per square meter) and the region density balance threshold B j (per square meter) need to be standardized using the same rules as the risk quantification module to obtain V' n and B' j , which are unitless scores of 0-10. The V n1 of path P32 = (P s1 +P e1 ) / 2 = (6+9) / 2 = 7.5. After standardization, V' n1 (V n1 =7.5) = 7+(7.5-5)×3 / 5 = 7+2.5×0.6 = 7+1.5 = 8.5. The V n2 of path P34 = (P s2 +P e2 ) / 2 = (6+5) / 2 = 5.5. After standardization, V' n2 (V n2= 5.5) = 7 + (5.5 - 5) x 3 / 5 = 7 + 0.5 x 0.6 = 7 + 0.3 = 7.3. *Assume both paths are in region j, whose density balance threshold B j = 8.0 people / m2. This threshold is set based on the region design capacity and management goal, standardized as B' j (B j = 8.0) = 7 + (8.0 - 5) x 3 / 5 = 7 + 3 x 0.6 = 7 + 1.8 = 8.8. *Dynamic disturbance correction term Λ d is a unitless value, whose setting is based on temporary obstacles or events (such as ground slippery warning, temporary maintenance occupation) on the path monitored in real time. The base value is set as 0.1, which increases if there is disturbance. Currently, both paths have no significant disturbance, so Λ d1 = 0.1, Λ d2 = 0.1.

[0160] Substitute the formula to calculate the initial reachable rate: for path P32 (k = 1): R 32,initial ≈ 0.7234, for path P34 (k = 2): R 34,initial ≈ 0.2214,

[0161] The operation obtains the initial reachable rate of path P32 as R 32,initial ≈ 0.7234, and the initial reachable rate of path P34 as R 34,initial ≈ 0.2214.

[0162] Subsequently, the initial reachable rate is prioritized based on the risk label level of the path (both are "medium risk"). The weighting aims to further reflect the risk avoidance strategy and moderately penalize the reachable rate of the path with higher risk. The weighting coefficient is set as: low risk 1.0, medium risk 0.9, high risk 0.7. The weighted calculation is: R 32,final = R 32,initial x 0.9 = 0.7234 x 0.9 = 0.6511;

[0163] R 34,final = R 34,initial x 0.9 = 0.2214 x 0.9 = 0.1993;

[0164] The path reachable rate value set {P32: 0.6511, P34: 0.1993} is generated.

[0165] The formula is beneficial in that it combines multiple dimension information, such as risk level strategy H k , regional inherent risk point S' c , flow volatility Φ w , path density and regional capacity matching degree |V' n -B' j | and temporary dynamic interference Λ d , into a single, unitless accessibility index, and combines risk level for final adjustment, to provide a more comprehensive and refined decision basis for path selection.

[0166] The calculation result gives the passing feasibility scores of the two candidate paths after comprehensive evaluation and risk adjustment.

[0167] The difference pruning submodule calls the path accessibility value set, generates an accessibility ranking table in descending order of numerical value, calculates the accessibility difference of adjacent paths one by one, compares the difference with the dynamic pruning threshold, deletes the low-value path with a difference exceeding the threshold, and generates a pruned path set;

[0168] The received path accessibility value set is {P32: 0.6511, P34: 0.1993}. First, arrange the accessibility values in descending order to generate an accessibility ranking table: [(P32: 0.6511), (P34: 0.1993)].

[0169] Next, calculate the accessibility difference of adjacent paths in the ranking table. Calculate the difference between P32 and P34: ΔR = R

[0170] R 32,final -R 34,final = 0.6511-0.1993 = 0.4518.

[0171] Compare the calculated difference with the dynamic pruning threshold. The dynamic pruning threshold is set to optimize the recommendation result, remove paths that meet the basic conditions but have a large gap relative to the optimal option, and avoid providing users with too many options with a large performance gap. The threshold is set according to the system's desired recommendation accuracy and user experience, for example, set to a certain percentage of the maximum accessibility or a fixed value. Here, a fixed threshold value of 0.4 is set. The value is based on historical data analysis, which found that when the accessibility difference is less than 0.4, user selection behavior is more random, and when the difference is greater than 0.4, users tend to choose high-accessibility paths. Compare the calculated difference 0.4518 with the pruning threshold 0.4. Because 0.4518>0.4, it indicates that the accessibility of P32 is significantly higher than that of P34.

[0172] According to the pruning rule, the lower value path involved in the difference calculation is deleted from the ranking table when the difference of reachability of adjacent paths exceeds the threshold value. In this example, the lower value path P34 needs to be deleted since the difference 0.4518 exceeds the threshold value 0.4.

[0173] After the deletion operation is performed, the pruned path set {P32: 0.6511} is generated. This set only contains the currently optimal optional path.

[0174] The sequence construction submodule calls the pruned path set, extracts the node stress factor of the device and the risk label level of the path, matches the stress factor according to the priority of the label level, and generates a recommended path sequence.

[0175] The pruned path set {P32: 0.6511} is received. The system needs to combine the device factors to construct the final recommended path sequence.

[0176] First, the node stress factor P factor of the nursing patrol auxiliary device associated with the path P32 is extracted. This factor is an evaluation value without unit (range 0 to 1) for quantifying the stress caused by selecting this path on the device itself (such as the CPU load of the mobile workstation, network traffic, or battery consumption rate of the tablet computer). The value of this factor is derived from the performance monitoring module of the device itself or calculated based on the estimated model of the path characteristics (such as length, signal strength). For path P32, the obtained device node stress factor P factor,1 = 0.4.

[0177] At the same time, the risk label level of path P32 is extracted, which is “medium risk”.

[0178] The system performs a check of matching the stress factor according to the priority of the label level. This step is to ensure that the selected path not only has high reachability, but also does not cause overload or performance bottleneck to the device when executing the path. The priority matching rule is pre-set, and the maximum device stress factor upper limit acceptable for paths of different risk levels is set: the highest P factor allowed for high-risk paths is 0.3, for medium-risk paths is 0.5, and for low-risk paths is 0.8. These upper limit values are set considering that in high-risk environments, the device may need to maintain higher response speed and stability, and therefore is more sensitive to resource occupation. The actual stress factor P factor,1 of path P32 = 0.4 is compared with the allowed upper limit 0.5 corresponding to its risk level “medium risk”. 0.4≤0.5, and the matching is successful. This indicates that the device running state is sufficient to support the selection and execution of the medium-risk path P32.

[0179] Since there is only one path P32 left in the pruned path set and it passed the device pressure factor matching check, the system finally generates the recommended path sequence [P32]. If there are multiple paths left after pruning and all of them pass the pressure factor check, the system usually constructs the sequence in descending order of reachability.

[0180] Referring to Figure 5 , the device interaction module comprises:

[0181] The path receiving and verifying submodule detects the correspondence between the number of nodes in the recommended path sequence and the level of path tags, calls the sequence integrity checking rule, compares the consistency of the number of nodes and the number of tag levels, generates an abnormal flag signal when the numbers are inconsistent, and generates a path verification value according to the checking result;

[0182] The recommended path sequence [P32] is received and subjected to final format and content verification.

[0183] The system detects the number of path segments contained in the sequence and the risk label level information associated with each path segment. The recommended sequence [P32] contains one path segment (from node 3 to node 2), and its associated label level information is "medium risk" passed from the upstream module, and the number of labels is 1.

[0184] The system calls the sequence integrity checking rule for comparison. The rule aims to ensure that the information finally passed to the device display is complete and consistent. The core verification content is that the number of path segments in the sequence must be completely consistent with the number of label level information associated with it. Rule instance: check whether the length of the path segment list is equal to the length of the label list. In this example, the number of path segments is 1, and the number of label levels is also 1. They are consistent.

[0185] If the verification is inconsistent, for example, the sequence contains two path segments but only one label, the system will generate an abnormal flag signal for internal log recording or to alert the administrator, indicating that there may be errors in the data stream.

[0186] According to the current verification result, since the number of path segments and the number of label levels are consistent, the verification is passed. The system generates a path verification value indicating the validity of the sequence. This verification value can be a boolean value TRUE or a status code. Here, the verification value "Valid" is generated.

[0187] The label color mapping submodule, based on the path verification value, calls the priority division table of the path label level, maps the red, yellow, and green three-color parameters corresponding to different priorities, extracts the RGB channel values of the label level sequence, binds the node coordinates, and generates the color mapping coefficient;

[0188] Based on the path verification value "Valid", it is confirmed that the visualization preparation work of the path can be carried out.

[0189] The system calls the predefined path label rank-color priority partition table. This table establishes the mapping relationship between the risk rank and the interface display color, in order to intuitively convey the risk state of the path on the user interface. The partition table content is: high risk-red (RGB: 255, 0, 0), medium risk-yellow (RGB: 255, 255, 0), low risk-green (RGB: 0, 128, 0). The selection of these colors follows the general risk warning color scale.

[0190] The system matches the risk label rank of path P32 "medium risk" with the table, and determines that its corresponding display color is yellow, and the corresponding RGB value is (255, 255, 0).

[0191] Then, the coordinates of the nodes connected by path P32 are extracted. These coordinate information is stored in the system map database. The coordinates of node 3 are (100, 200), and the coordinates of node 2 are (150, 250), in pixels or map units.

[0192] The system binds the path identification, start and end nodes, node coordinates, and corresponding color information together to generate a color mapping coefficient. This is a structured data object, the content of which is: {PathID: P32, StartNode: 3, EndNode: 2, StartCoords: (100, 200), EndCoords: (150, 250), ColorRGB: (255, 255, 0)}. This object contains all the key information required to draw this path segment on the interface.

[0193] The interface synchronization execution submodule calls the valid node sequence of the path verification value and the RGB parameter set of the color mapping coefficient, converts the path coordinates to interface grid coordinates, fills the grid cells according to the color rule, detects the coordinate and screen resolution adaptation and scales, and generates interface synchronization parameters that completely match the nursing patrol auxiliary equipment.

[0194] The valid recommended path sequence [P32] and its corresponding color mapping coefficient {PathID: P32, …, ColorRGB: (255, 255, 0)} are received.

[0195] The system first performs coordinate conversion, converts the logical coordinates of the path (StartCoords: (100, 200), EndCoords:

[0196] (150,250)) to actual display coordinates (interface grid coordinates) on the care patrol assistant device screen. This conversion process relies on the device's screen resolution (set to 800x600 pixels) and the current map's display scale and viewport position. Through a coordinate conversion function, (100,200) is mapped to screen coordinates (gx1,gy1) and (150,250) is mapped to screen coordinates (gx2,gy2).

[0197] Subsequently, the path is drawn on the device screen according to the color rule. The system calls the device's graphics interface library function to draw a yellow line segment between the calculated screen coordinates (gx1,gy1) and (gx2,gy2) using the obtained RGB color parameters (255,255,0), representing the recommended path P32. If the interface uses a grid display, the grid cells that make up the path segment are filled.

[0198] The system performs coordinate and screen resolution adaptation detection and adjustment. It checks whether the coordinates (gx1,gy1,gx2,gy2) required to draw the path are completely within the visible area of the screen (0<=gx<=800, 0<=gy<=600). If the path exceeds the screen boundary, or because of the zoom level, the path is displayed too small or too large, the system will automatically adjust the map viewport (pan or zoom) to ensure that the recommended path is clearly visible and completely displayed.

[0199] Finally, a series of interface synchronization parameters that perfectly match the care patrol assistant device display are generated. These parameters are specific drawing instruction sets or updated interface state descriptions that directly drive the update of the device screen content, presenting the recommended path with color risk levels to the user.

[0200] Please refer to Figure 6 , the feedback update module includes:

[0201] The trajectory collection submodule collects the path selection data and task completion time output by the care patrol assistant device, extracts the node positions in the path trajectory where the stay duration exceeds the preset duration threshold, calculates the average moving time between adjacent nodes, and integrates the path trajectory and the average time consumption to generate a path trajectory dataset;

[0202] This module is responsible for collecting actual behavior data during the user's use of the care patrol assistant device to perform tasks.

[0203] The system records the path selection data output by the device, and the user actually selects the recommended path P32 (node 3 to node 2) and travels along this path. At the same time, the start and end times of the task are recorded, and the task completion time T complete = 18 minutes.

[0204] The system analyzes the timestamp and location sequence provided by the positioning module built-in the system (such as Wi-Fi fingerprint or Bluetooth beacon positioning) to extract the length of stay at each node in the path trajectory. A threshold for the length of stay is set to identify abnormal long stays, which is set to 2 minutes in reference to the standard operation time of each nursing operation. The analysis shows that the length of stay at node 3 is 1 minute, the moving time is 4 minutes, and the length of stay at node 2 is 1.5 minutes. All the lengths of stay do not exceed the threshold of 2 minutes, so no abnormal long stay node is recorded.

[0205] The average moving time between adjacent nodes is calculated. In this task, only one path segment P32 is executed, and the moving time is T move,32 = 4 minutes. Therefore, the average moving time is 4 minutes.

[0206] Finally, the key information collected is integrated: the actual executed path trajectory [P32], the total task completion time of 18 minutes, the average moving time of the path segment of 4 minutes, and the abnormal long stay information (none). The path trajectory dataset for this task is generated {ActualPath: [P32], TaskCompletionTime: 18min, AvgSegmentMoveTime: 4min, LongStays: []}. This dataset will be used for subsequent system feedback and updates.

[0207] The risk update submodule calls the node risk fluctuation parameters and the node positions in the path trajectory dataset, replaces the infection risk parameters in the path set with the corresponding node risk fluctuation parameters, filters the nodes whose fluctuation values exceed the preset infection benchmark value times, calculates the average risk of the entire path after replacement, and generates risk update parameters;

[0208] Using the path trajectory dataset, combined with the preset parameters, the risk model of the system is updated or feedback parameters are generated.

[0209] The system calls the preset node risk fluctuation parameter ΔR node . This parameter is estimated based on historical data or infectious disease models, representing the average change in potential associated risk of a node after being visited once. The risk fluctuation parameter of node 3 is ΔR3 = +0.1 risk units / visit, and the risk fluctuation parameter of node 2 is ΔR2 = +0.15 risk units / visit. (Here, "risk units" are related to the scale of the risk index R t calculated before, but only represent the increment). The path trajectory dataset shows that node 3 and node 2 are actually visited in this task.

[0210] The system calculates the change in path-related risk due to the current actual access activity. This is achieved by accumulating the risk fluctuation parameters of the path nodes, resulting in the risk increment of path P32: ΔR path = ΔR3 + ΔR2 = 0.1 + 0.15 = 0.25.

[0211] The system performs a check to filter out nodes whose risk fluctuation values exceed a certain benchmark during this access. A benchmark value R base is set for the infection risk, which can refer to the previous risk level threshold and choose the lower limit of the medium risk R base = 20. A fluctuation multiple M = 0.01 is set, i.e. those nodes whose single access causes a risk fluctuation exceeding 1% of the benchmark value are of concern. The filtering threshold R base × M = 20 × 0.01 = 0.2 is calculated. Compare the risk fluctuation parameters of the visited nodes: ΔR3 = 0.1 < 0.2, ΔR2 = 0.15 < 0.2. In this task, no single access risk fluctuation of a node exceeds the set filtering standard.

[0212] Based on the above calculation and filtering results, the system generates a risk update parameter. This parameter aims to quantify the potential impact of this task execution on the system's risk state. In this example, the main update information is the calculated overall risk increment of the path. The risk update parameter ΔR update = 0.25 is generated. This parameter can be used to adjust the baseline risk value in subsequent risk assessment models or for long-term risk trend analysis.

[0213] The parameter return submodule integrates the task completion time and node risk fluctuation parameters based on the average move time in the path trajectory dataset and the risk update parameter, constructs a parameter transmission queue, returns the queue data according to the interface format of the task trigger module, and generates updated parameters.

[0214] Based on the key indicators in the path trajectory dataset (average move time: 4 minutes) and the parameters generated by the risk update submodule (risk update parameter ΔR update = 0.25), the system integrates other related data, including the task completion time (18 minutes) and the risk fluctuation parameters of the nodes involved in this task (node 3: +0.1, node 2: +0.15).

[0215] The system constructs a structured parameter transmission queue to organize these information. The queue data content is: QueueData = {AverageMoveTime: 4, RiskIncrement: 0.25, TotalTaskTime: 18, VisitedNodeFluctuations: {Node3: 0.1, Node2: 0.15}}.

[0216] The system encodes the queue data in a predefined interface format (in JSON format). The encoded data is: '{ "AverageMoveTime" : 4, "RiskIncrement" : 0.25, "TotalTaskTime" : 18,

[0217] "VisitedNodeFluctuations" : { "Node3" : 0.1, "Node2" : 0.15}} '.

[0218] This JSON-formatted data is transmitted back to the designated system module interface through a secure network connection. After successfully receiving and parsing the data, the receiving module returns an acknowledgment status, such as HTTP status code 200 OK.

[0219] The system records the status of this parameter transmission operation and generates an operation result record with the content "FeedbackTransmittedSuccessfully". These transmitted parameters will be used to optimize path planning algorithms, update risk maps, evaluate system performance, or support management decisions.

[0220] An infectious disease department nursing information management method is executed based on the infectious disease department nursing information management system, comprising the following steps:

[0221] S1: Obtain the path set associated with the current task node through the node mapping matrix, call the path flow limiting factor parameter to perform screening processing on the path set, the screening condition is the bidirectional constraint relationship between the path length and the preset flow threshold, and generate an initial path set;

[0222] S2: Based on the initial path set, real-time personnel density data, environmental disinfection frequency data and biological index concentration data are monitored in real time, and the maximum value operation is performed on the path start and end nodes to generate an instant risk value, and the instant risk value is subjected to difference operation with the preset risk threshold, and the path label level is assigned to the path whose difference is greater than zero;

[0223] S3: Input the path label level into the reachability analysis model, combine the path flow limiting factor parameter and the number of regional cross-infection points to perform single-path reachability calculation, perform descending arrangement on the calculation result through the sorting algorithm, perform pruning processing based on the adjacent path reachability difference, and generate a recommended path sequence;

[0224] S4: Call the recommended path sequence, perform color rendering processing on the path according to the path label level, and synchronously transmit the rendered path data and label data to the interactive interface of the nursing patrol auxiliary equipment;

[0225] S5: Collect the path selection record, task execution time and node risk fluctuation data of the nursing patrol auxiliary device, call the parameter replacement algorithm to perform replacement processing on the node risk fluctuation data and the path infection risk parameters, and return the updated parameters to the node mapping matrix.

[0226] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any person skilled in the art can use the disclosed technical content to make changes or modifications as equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. An infectious disease nursing information management system, characterized in that, The system includes: The task triggering module is used to obtain the set of paths associated with the current task node through the node mapping matrix, call the path flow restriction factor to perform filtering on the set of paths, generate an initial set of paths and transmit it to the risk assessment module; The risk assessment module is used to monitor personnel contact density, environmental disinfection frequency and biological indicator concentration in real time based on the initial path set, perform a double maximum value calculation on the starting and ending nodes of the path to generate an instant risk value, subtract the difference between the instant risk value and a preset threshold, assign path label level to paths that exceed the threshold and transmit it to the path analysis module. The path analysis module is used to receive the path label level, call the flow control factor parameters and the regional cross-infection point count to generate a single path reachability value, sort the single path reachability value, perform difference calculation, prune the path and generate a recommended path sequence to be transmitted to the device interaction module. The device interaction module is used to receive the recommended path sequence, call the path label level to perform color-separated rendering, and synchronize the path label level and the recommended path sequence to the nursing inspection auxiliary device interface.

2. The infectious disease nursing information management system according to claim 1, characterized in that, The initial path set specifically includes node mapping relationships, path flow restriction factors, and screening condition parameters. The real-time risk value includes the maximum personnel contact density, the maximum environmental disinfection frequency, and the difference in biological indicator concentration thresholds. The path label level specifically refers to the risk spillover identifier, the level classification code, and the dynamic threshold deviation. The recommended path sequence includes the single path reachability value, flow control factor parameters, regional cross-infection point count items, and path ranking difference. The path sequence after color-coded rendering specifically includes color coding rules, nursing inspection equipment interface parameters, and path label synchronization data.

3. The infectious disease nursing information management system according to claim 2, characterized in that, The task triggering module includes: The path mapping submodule obtains the adjacent path identifiers of the current task node in the node mapping matrix, extracts the capacity parameters and connection directions of the associated paths, calculates the path topology weights, and generates an adjacent path topology table. The restriction filtering submodule calls the path flow restriction factor, which is based on the capacity parameter in the adjacent path topology table and the difference between the real-time load, using the following formula: The algorithm calculates and obtains the dynamic adaptation value of the path, compares the adaptation value with the preset threshold, filters the path identifiers that meet the conditions, and generates a set of restricted and filtered paths. Where Γ represents the path dynamic adaptation value, C k L represents the maximum capacity of the k-th path. k D represents the real-time load value of the k-th path. k T represents the degree of matching between the direction of the k-th path and the transmission direction of the current task flow. k The topological weight of the k-th path is represented by ε, which is a zero-interference prevention constant used to avoid the smallest positive number with a denominator of zero. k is the path number, and n is the total number of paths associated with the current task node. The set integration submodule merges the priority tags and transmission delay parameters of the path identifiers in the restricted filtering path set, sorts them in ascending order of delay, removes duplicate paths, and generates an initial path set.

4. The infectious disease nursing information management system according to claim 3, characterized in that, The risk assessment module includes: The path monitoring submodule, based on the initial path set, collects the personnel contact density at the start and end of each path in real time, records the time distribution of environmental disinfection frequency, monitors the dynamic changes in biological indicator concentration, integrates the three data into a structured set, and generates a path dynamic parameter set. The risk quantification submodule calls the dynamic parameter set of the path to extract the personnel contact density, environmental disinfection frequency, and biological indicator concentration at the starting and ending points, using the following formula: The real-time risk value is generated by multiplying the maximum value of personnel contact density at the start and end points of the calculation with the maximum value of environmental disinfection frequency. Among them, R t P represents the product of the maximum contact density of people at the starting point and the maximum frequency of disinfection between the origin and destination of the path. s P represents the measured value of personnel contact density at the starting node of the path. e F represents the measured value of personnel contact density at the end node of the path. s F represents the frequency of environmental disinfection at the starting node of the path within a unit of time. e B represents the frequency of environmental disinfection at the endpoint of the path within a unit of time. s B represents the biomarker concentration monitoring value at the starting node of the path. e The biomarker concentration monitoring value represents the end node of the path, and φ represents a very small positive constant introduced to avoid the denominator being zero. The label decision submodule calls the real-time risk value, performs a difference calculation with the preset risk threshold, determines whether the difference is greater than zero, and for paths that exceed the threshold, divides the level range according to the range of the absolute value of the difference, and outputs the path label level.

5. The infectious disease nursing information management system according to claim 4, characterized in that, The path analysis module includes: The reachability generation submodule receives the path label level, calls the flow control factor parameter and the regional cross-infection point count item, and uses the formula: The initial value of the path reachability is obtained through calculation, and the initial value is weighted according to the priority of the path label level to generate a set of path reachability values. Among them, R pq H represents the reachability value of the path from the p-th to the q-th path. k S represents the flow control factor parameter for the k-th path. c Φ represents the cross-infection point count in region c. w V represents the traffic fluctuation coefficient of the w-th path. n B represents the node density exponent of path n. j The density balance threshold Λ represents region j. d The dynamic interference correction term represents path d; The differential pruning submodule calls the path reachability value set, sorts it in descending order of numerical value to generate a reachability sorting table, calculates the reachability difference between adjacent paths one by one, compares the difference with the dynamic pruning threshold, deletes low-value paths with differences exceeding the threshold, and generates a pruned path set. The sequence construction submodule calls the pruned path set, extracts the node pressure factor of the device and the path label level, matches the pressure factor according to the label level priority, and generates a recommended path sequence.

6. The infectious disease nursing information management system according to claim 5, characterized in that, The device interaction module includes: The path receiving and verification submodule detects the correspondence between the number of nodes in the recommended path sequence and the path label level, calls the sequence integrity verification rules, compares the consistency between the number of nodes and the number of label levels, generates an abnormal marker signal when the number is inconsistent, and generates a path verification value based on the verification result. The label color mapping submodule, based on the path verification value, calls the priority division table of the path label level, assigns red, yellow, and green parameters to different priorities, extracts the RGB channel values ​​of the label level sequence, binds the node coordinates, and generates color mapping coefficients. The interface synchronization execution submodule calls the valid node sequence of the path verification value and the RGB parameter set of the color mapping coefficient, converts the path coordinates into interface grid coordinates, fills the grid cells according to the color rules, detects the compatibility between the coordinates and the screen resolution and scales and adjusts them, and generates interface synchronization parameters that are completely matched with the nursing inspection auxiliary equipment.

7. The infectious disease nursing information management system according to claim 6, characterized in that, The system also includes: The feedback update module is used to collect path selection data, task completion time and node risk fluctuation parameters through the nursing inspection auxiliary equipment, call the node risk fluctuation parameters to replace the infection risk parameters of the path set, and send the updated parameters back to the task triggering module. The updated parameters include node risk fluctuation parameters, path selection data, task completion time records, and infection risk replacement indicators.

8. The infectious disease nursing information management system according to claim 7, characterized in that, The feedback update module includes: The trajectory acquisition submodule collects the path selection data and task completion time output by the nursing inspection auxiliary equipment, extracts the node positions in the path trajectory whose dwell time exceeds a preset time threshold, calculates the average movement time between adjacent nodes, and integrates the path trajectory and the average time to generate a path trajectory dataset. The risk update submodule calls the node risk fluctuation parameters and the node positions in the path trajectory dataset, replaces the infection risk parameters in the path set with the corresponding node risk fluctuation parameters, filters nodes whose fluctuation values ​​exceed a preset infection benchmark value multiple, calculates the overall risk average of the replaced path, and generates risk update parameters. The parameter feedback submodule integrates the task completion time and node risk fluctuation parameters based on the average movement time in the path trajectory dataset and the risk update parameters, constructs a parameter transmission queue, and sends back queue data according to the interface format of the task triggering module to generate updated parameters.

9. A method for managing nursing information in infectious disease departments, characterized in that, The implementation of the infectious disease nursing information management system according to any one of claims 1-8 includes the following steps: S1: Obtain the set of paths associated with the current task node through the node mapping matrix, call the path flow restriction factor parameter to perform filtering processing on the set of paths, the filtering condition is the bidirectional constraint relationship between the path length and the preset flow threshold, and generate an initial set of paths; S2: Based on the initial path set, monitor real-time personnel density data, environmental disinfection frequency data and biological indicator concentration data in real time, perform double maximum value calculation on the starting point and ending point of the path to generate an instant risk value, perform difference calculation between the instant risk value and the preset risk threshold, and assign path label level to the path with difference greater than zero. S3: Input the path label level into the accessibility analysis model, combine the path flow restriction factor parameter with the number of cross-infection points in the region to perform single path accessibility calculation, sort the calculation results in descending order through a sorting algorithm, perform pruning based on the accessibility difference between adjacent paths, and generate a recommended path sequence; S4: Call the recommended path sequence, perform color rendering on the path according to the path label level, and transmit the rendered path data and label data synchronously to the interactive interface of the nursing inspection auxiliary device. S5: Collect path selection records, task execution time and node risk fluctuation data of nursing inspection auxiliary equipment, call the parameter replacement algorithm to perform replacement processing on the node risk fluctuation data and path infection risk parameters, and send the updated parameters back to the node mapping matrix.

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