An internet-based elderly care service system

Abstract

The application relates to the technical field of administrative management and dispatching, and discloses an old-age care service system based on the Internet, which comprises a data acquisition unit, a logic operation unit and a resource configuration unit. The system divides a target area into a business management grid and determines a grid resource pre-occupancy weight by extracting service demand distribution characteristics and calculating demand distribution entropy. When the weight is out of limit, a resource node is locked and a virtual dispatching voucher is generated. A response preparation degree deviation is calculated based on the length of time that the node is static, a pre-displacement task is generated, and the resource node is driven to move to a high-probability demand area before a formal work order, the management and dispatching logic is reconstructed from passive response to a pre-displacement mechanism based on prediction, the resource state is locked by using the virtual voucher, and the starting hysteresis effect is eliminated, so that the response certainty of the system in response to sudden demand is synergistically enhanced.

Description

Technical Field

[0001] This invention relates to an internet-based elderly care service system, belonging to the field of administrative management and dispatch technology. Background Technology

[0002] With the current trend of population aging, Internet systems used for resource allocation have become core tools for administrative scheduling and management. Existing elderly care service management systems generally adopt a serial response mode consisting of explicit demand confirmation, system resource optimization, and scheduling instruction issuance. By receiving the exact request signal uploaded by the terminal device, the computing power is triggered to perform static matching between the resource node and the demand coordinate.

[0003] For sudden care scenarios with extremely narrow time windows, this serial processing logic generates inherent response delays due to the system being in a passive receiving state. At the physical execution level, the switching of resource nodes from a static dwelling state to a displacement execution state involves inherent equipment preparation and environmental conversion time, resulting in a misalignment between the timing of dispatching instructions and the actual reach efficiency of physical resources. This makes the system lack response determinism when handling nonlinear sudden demands. In addition to hardware-level start-up lag, the existing logic is insufficient to cope with demand fluctuations and pre-displacement capabilities. Chinese invention patent application CN117575229A discloses a service personnel dispatching method and system that considers the vertical distribution of customers. This solution takes into account the vertical time loss of high-rise buildings and uses algorithms for optimization. However, the underlying logic anchors the response to service request signals with preset premises. When facing nonlinear sudden demands, due to the lack of quantitative representation of uncertainty and pre-adjustment, even if the path calculation is accurate, it cannot compensate for the inherent time consumption of physical cold start of resource nodes.

[0004] Therefore, how to reconstruct the scheduling triggering mechanism and achieve coordinated alignment between digital logic and physical ready state is the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: An Internet-based elderly care service system, the system comprising:

[0006] The data acquisition unit is used to acquire historical data on service demand within the target area;

[0007] The logic operation unit, connected to the data acquisition unit, is used to extract features from historical service demand data. By calculating the negative value of the sum of the probabilities of service demand occurrence within the grid and their logarithmic products, it determines the demand distribution entropy, which represents uncertainty, divides the target area into multiple business management grids, and determines the resource pre-occupancy weight of the business management grids based on the demand distribution entropy. The logic operation unit is also used to construct a resource management model for the corresponding target area and use the resource pre-occupancy weight as the load attribute of each grid node in the resource management model.

[0008] The resource configuration unit, connected to the logic operation unit, is used to lock resource nodes that meet the qualification tags within the scheduling logic when the resource pre-occupancy weight exceeds the preset load threshold, generate a virtual scheduling certificate uniquely bound to the resource node, and set the availability status of the resource node to exclusive state. The resource configuration unit is also used to obtain the static duration of the resource node and calculate the initial response lag based on the static duration, which characterizes the deviation of the resource node's response readiness. The resource configuration unit is also used to generate a pre-displacement task containing the virtual scheduling certificate based on the initial response lag and send it to the resource node, driving the resource node to move to the high-probability demand area in the business management grid before receiving a formal service work order.

[0009] Preferably, the resource configuration unit is also used to set a dynamic validity period attribute for the virtual scheduling certificate, monitor its corresponding locking permission score during the duration of the virtual scheduling certificate, calculate the decay rate of the locking permission score based on the dynamic validity period attribute, and execute the virtual scheduling certificate invalidation processing when the locking permission score drops to the preset recycling limit and no real-time service work order trigger signal is generated in the business management grid, so as to reset the availability status of the resource node from the exclusive state to the allocable state, thereby realizing the dynamic release of nursing resources in the target area.

[0010] Preferably, the logic operation unit calculates the demand distribution entropy. At that time, the following quantification rules shall be executed: ,in, Let n be the probability of the demand occurring in the i-th grid of the business management grid within a preset sampling period, and n be the total number of business management grids in the target area.

[0011] Preferably, the resource configuration unit also includes a qualification matching module, which is used to obtain the skill qualification tags of resource nodes, map the skill qualification tags to the task types of overloaded grids in the business management grid, and select resource nodes with a skill qualification matching degree greater than 0.9 as binding objects for virtual scheduling credentials.

[0012] Preferably, the resource allocation unit calculates the initial response lag. When this happens, the following rules will be applied: ,in, The duration of stillness. Prepare the preset response coefficients.

[0013] Preferably, the pre-displacement task corresponds to a low-priority prior administrative task. When generating the pre-displacement task, the resource allocation unit searches for a preset communication base station or administrative check-in point in a high-probability demand area and sets the trip to the communication base station to perform equipment inventory or health check-in as the preset displacement target of the resource node.

[0014] Preferably, the system also includes an interrupt handling unit, which is used to monitor external trigger signals in real time, identify the real-time care needs corresponding to the external trigger signal when an external trigger signal is received, and convert the virtual scheduling certificate of the resource node in exclusive state into an physical execution work order.

[0015] Preferably, when the logic operation unit performs feature extraction, the extracted feature dimensions include: response timing threshold, resource node qualification, and settlement confirmation data, and a scheduling triggering benchmark on the time axis is established based on the response timing threshold and settlement confirmation data.

[0016] Preferably, the division density of the business management grid is determined by the spatial distribution density of historical service demand data. In areas where the density of historical service demand data is higher than a preset distribution threshold, the unit area of ​​the business management grid is reduced to improve the spatial identification accuracy of resource pre-occupancy weight.

[0017] Preferably, the system also includes a parameter feedback module, which is used to record the actual arrival time after the resource node performs displacement, calculate the time difference between the actual arrival time and the time when the pre-displacement task is issued, and iteratively correct the response preparation coefficient based on the time difference.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. In the elderly care service system, features are extracted from historical service consumption sequence data and demand fluctuation entropy is calculated. Combined with the generation and revocation mechanism of shadow scheduling credentials, the traditional scheduling system relies on the serial processing method triggered by explicit demand. When the preset threshold is exceeded, the system locks qualified resource nodes within the logical topology and issues shadow scheduling credentials, so that the resource nodes enter the pre-occupancy state before the actual demand bursts. This logical pre-simulation based on the uncertainty of demand fluctuations eliminates the structural rigidity of the underlying control flow of the management system, avoids the delay in rescue response caused by optimization lag, and enables the system to have the response determinism to handle massive, fragmented, nonlinear sudden tasks.

[0020] 2. By using a computer-aided design engine, demand nodes and resource nodes are mapped to an elastic topological mesh model in a virtual design space. The demand fluctuation entropy is translated into a topological distortion stress scalar acting on the mesh coordinates. When local stress is overloaded, the system automatically generates directed geometric constraint edges within the logical topology to clear the motion degrees of freedom of resource nodes. This technical approach, which transforms the management and scheduling problem into geometric topology reshaping, is based on the ability of the resource network to automatically stiffen its spatial structure when facing sudden loads. Since the geometric constraint edges have a dynamic half-life attribute, the system can automatically reclaim global resources by exponentially decaying the logical locking weight while ensuring the locking of high-priority tasks, thus eliminating the risk of systemic deadlock.

[0021] 3. A physical cold start hysteresis assessment mechanism is introduced. The physical cold start hysteresis assessment value is calculated by the static residence time of resource nodes and the friction constant of the basic cold machine. Based on this, a pre-state transition work order is generated. The abstract digital pre-occupancy state is transformed into physical kinetic energy that drives resource nodes to move to high-probability demand areas. This eliminates the equipment preparation and environmental conversion time required for resource nodes to transition from a static state to an execution state. This deep alignment between digital locking and physical ready state ensures that when an abnormal interruption signal is received from the environmental hardware, resource nodes in the dynamic preheating state can achieve zero-hysteresis switching of work orders, ensuring the success rate of rescue missions within a very narrow time window. Attached Figure Description

[0022] Figure 1 This is a flowchart of the elderly care resource scheduling process based on the prediction and pre-displacement mechanism of this invention;

[0023] Figure 2 This is a diagram showing the functional modules and multi-dimensional interactive architecture of the elderly care service system of the present invention.

[0024] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0026] An internet-based elderly care service system, comprising:

[0027] The data acquisition unit is used to acquire historical data on service demand within the target area;

[0028] The logic operation unit, connected to the data acquisition unit, is used to extract features from historical service demand data. By calculating the negative value of the sum of the probabilities of service demand occurrence within the grid and their logarithmic products, it determines the demand distribution entropy, which represents uncertainty, divides the target area into multiple business management grids, and determines the resource pre-occupancy weight of the business management grids based on the demand distribution entropy. The logic operation unit is also used to construct a resource management model for the corresponding target area and use the resource pre-occupancy weight as the load attribute of each grid node in the resource management model.

[0029] The resource configuration unit, connected to the logic operation unit, is used to lock resource nodes that meet the qualification tags within the scheduling logic when the resource pre-occupancy weight exceeds the preset load threshold, generate a virtual scheduling certificate uniquely bound to the resource node, and set the availability status of the resource node to exclusive state. The resource configuration unit is also used to obtain the static duration of the resource node and calculate the initial response lag based on the static duration, which characterizes the deviation of the resource node's response readiness. The resource configuration unit is also used to generate a pre-displacement task containing the virtual scheduling certificate based on the initial response lag and send it to the resource node, driving the resource node to move to the high-probability demand area in the business management grid before receiving a formal service work order.

[0030] Preferably, the resource configuration unit is also used to set a dynamic validity period attribute for the virtual scheduling certificate, monitor its corresponding locking permission score during the duration of the virtual scheduling certificate, calculate the decay rate of the locking permission score based on the dynamic validity period attribute, and execute the virtual scheduling certificate invalidation processing when the locking permission score drops to the preset recycling limit and no real-time service work order trigger signal is generated in the business management grid, so as to reset the availability status of the resource node from the exclusive state to the allocable state, thereby realizing the dynamic release of nursing resources in the target area.

[0031] Preferably, the logic operation unit calculates the demand distribution entropy. At that time, the following quantification rules shall be executed: ,in, Let n be the probability of the demand occurring in the i-th grid of the business management grid within a preset sampling period, and n be the total number of business management grids in the target area.

[0032] Preferably, the resource configuration unit also includes a qualification matching module, which is used to obtain the skill qualification tags of resource nodes, map the skill qualification tags to the task types of overloaded grids in the business management grid, and select resource nodes with a skill qualification matching degree greater than 0.9 as binding objects for virtual scheduling credentials.

[0033] Preferably, the resource allocation unit calculates the initial response lag. When this happens, the following rules will be applied: ,in, The duration of stillness. Prepare the preset response coefficients.

[0034] Preferably, the pre-displacement task corresponds to a low-priority prior administrative task. When generating the pre-displacement task, the resource allocation unit searches for a preset communication base station or administrative check-in point in a high-probability demand area and sets the trip to the communication base station to perform equipment inventory or health check-in as the preset displacement target of the resource node.

[0035] Preferably, the system also includes an interrupt handling unit, which is used to monitor external trigger signals in real time, identify the real-time care needs corresponding to the external trigger signal when an external trigger signal is received, and convert the virtual scheduling certificate of the resource node in exclusive state into an physical execution work order.

[0036] Preferably, when the logic operation unit performs feature extraction, the extracted feature dimensions include: response timing threshold, resource node qualification, and settlement confirmation data, and a scheduling triggering benchmark on the time axis is established based on the response timing threshold and settlement confirmation data.

[0037] Preferably, the division density of the business management grid is determined by the spatial distribution density of historical service demand data. In areas where the density of historical service demand data is higher than a preset distribution threshold, the unit area of ​​the business management grid is reduced to improve the spatial identification accuracy of resource pre-occupancy weight.

[0038] Preferably, the system also includes a parameter feedback module, which is used to record the actual arrival time after the resource node performs displacement, calculate the time difference between the actual arrival time and the time when the pre-displacement task is issued, and iteratively correct the response preparation coefficient based on the time difference.

[0039] Example 1: In the grid management scenario of high-density elderly care communities in large cities facing nonlinear sudden care demand shocks, the traditional serial response mode consisting of explicit demand confirmation, system optimization, and dispatch command issuance puts the system in a passive receiving state, resulting in response delays. Especially when resource nodes with advanced life support qualifications are in a static state for a long time, there is a time consumption for equipment preparation and environmental transformation between receiving the command and physical displacement, which leads to a timing mismatch between the timing of dispatch command issuance at the digital logic level and the actual reach efficiency at the physical execution level.

[0040] When the system faces the above operating conditions, the data acquisition unit de-identifies the historical service consumption sequence data collected within the target area to obtain historical service demand data; the logic operation unit extracts the features of the historical service demand data, calculates the negative value of the sum of the probabilities of service demand occurrence within the business management grid and their logarithmic products according to quantification rules, and determines the demand distribution entropy that represents uncertainty. The specific calculation formula is as follows: ,in, Let n be the probability of demand occurrence for the i-th grid in the business management grid within a preset sampling period, and n be the total number of business management grids in the target area. When the calculated demand distribution entropy value in a local area exceeds a preset first threshold, this entropy value is converted into a distortion stress scalar acting on the local logical topology grid model. This triggers the resource allocation unit to lock specific resource nodes with a skill qualification tag matching degree greater than 0.9 with the overload grid task type when the resource pre-occupancy weight exceeds a preset load threshold. The system presets a binary mask containing 10 core qualification features. It performs a bitwise AND operation between the 10-bit qualification code of the resource node and the 10-bit feature requirement of the current task. The total number of bits with a value of 1 in the statistical result is counted. If the ratio obtained by dividing the total number of qualification items required by the task is greater than 0.90, the resource node is determined to have the physical qualification to execute the current overload grid task. The system generates a virtual scheduling certificate uniquely bound to the resource node and sets the availability status of the resource node to an exclusive state, blocking it from receiving regular scheduling instructions with a priority lower than the first preset level. The resource allocation unit obtains the idle duration of the resource node. Combined with the preset response preparation coefficient Calculate the initial response lag that characterizes the deviation in the response readiness of resource nodes. The specific calculation formula is as follows: The system generates a pre-displacement task containing virtual scheduling credentials based on the initial response lag. It sets the pre-defined communication base station inventory equipment in the high-probability demand area as the pre-defined displacement target for the resource node. This drives resource nodes in a cold-run state to move to the high-probability demand area in the business management grid before receiving a formal service work order. The resource configuration unit extracts the probability of demand occurrence from the business management grid. In the highest centroid region, a physical node search is performed with a radius of 500 meters centered on the centroid. Given the probability of demand occurring in the i-th grid within a preset sampling period in the business management grid, communication base stations or administrative check-in points with communication signal strength greater than -90 decibels and public parking conditions within the range are selected as preset displacement targets for resource nodes. When there are multiple candidate nodes within the search range, the node with the shortest travel time is selected as the final displacement target point based on the current latitude and longitude coordinates of the resource node and the real-time road network travel weight between each candidate node, so that the physical path of the resource node is aligned with the centroid of the potential demand distribution.

[0041] The target resource node responds to the pre-displacement task to break its static state and approach the high-probability demand coordinates. The interrupt handling unit inside the system monitors external trigger signals in real time. When a corresponding real-time care demand is identified, the virtual scheduling certificate of the resource node in the exclusive state and in the displacement process is converted into a physical execution work order, realizing the switch from digital instructions to physical rescue momentum. If no real-time service work order trigger signal is generated within the 15-minute to 30-minute dynamic validity period attribute set for the virtual scheduling certificate, the system continuously monitors its corresponding locking permission score and calculates the exponential decay rate of the locking permission score based on the dynamic validity period attribute. When the locking permission score drops to the preset reclamation limit and no real-time service work order trigger signal is generated in the business management grid, the system invalidates the virtual scheduling certificate, resets the availability status of the resource node from the exclusive state to the allocatable state, and maintains the responsiveness of the system architecture through the generation of local topological rigid constraints and the dynamic release mechanism of global resources.

[0042] Example 2: In the objective verification test of the response efficiency of multi-source concurrent care demand in a mega-city, a discrete event simulation platform was constructed to restore the dynamic characteristics of a high-density business management grid. Anonymized logs of real care work orders from a specific main urban area for 12 consecutive months were imported as the driving data source. When generating the test demand sequence, the system actively superimposed a random time jitter deviation with an average of 2.5 minutes and signal delay disturbance components from the corresponding weak network coverage area of ​​the city, forming a test input environment containing real-world uncertainties. When setting the dynamic validity period attribute of the test platform, the risk of global supply shortage caused by resource monopoly and the probability of pre-displacement failure caused by premature release need to be balanced. The resource allocation unit internally determines the attribute range based on the rate of change of the derivative of the demand distribution entropy. When the growth slope of the local demand distribution entropy is greater than the preset critical slope, the system tends to set the dynamic validity period attribute to the lower limit of its value range to accelerate resource circulation. Based on the growth characteristics of the demand distribution entropy in a specific time period in the anonymized logs, the system sets the specific value of the dynamic validity period attribute to 22.5 minutes, and simultaneously sets the sampling period to 10 minutes, extracting the operating parameters of 500 business management grids in the test scenario.

[0043] In the comparative test verifying the elimination of physical latency by scheduling logic, a control group using traditional serial scheduling logic and an experimental group using virtual scheduling credentials and pre-displacement tasks were established. The business management grid was divided into three gradient environments: a low-entropy state region, a medium-entropy state region, and a high-entropy state region with increasing demand occurrence probability. Within a specific high-entropy state region, the logic operation unit calculated and obtained the demand distribution entropy. The specific value was 4.85, which exceeded the preset first threshold of 3.50. The system detected that the resource nodes with high-level life support qualifications in the area had been inactive for a certain period of time. The time is 45.5 minutes, combined with the set response readiness coefficient. The value is 0.35, calculated according to the formula: The initial response lag of the resource node was output as 15.92 min. The experimental group issued a pre-displacement task to drive the resource node to move towards the communication base station, while the control group maintained a stationary state until it received the physical service work order. Sensitivity test data on the duration of the dynamic validity period attribute showed that when the duration setting value crossed the working window of 15 min to 30 min and continued to increase to 35.0 min, the global resource satisfaction rate of the system decreased from 94.2% to 78.5%, showing a clear inflection point of nonlinear performance degradation, and establishing 15 min to 30 min as the physical boundary of the working window.

[0044] Summarizing response performance data under different disturbance intensities and demand gradients, the control group faced an average comprehensive response time of up to 24.3 minutes in the high-entropy state region with superimposed signal delay disturbance components, of which equipment preparation and environmental conversion time accounted for no less than 65%. Under the same input data conditions, the experimental group, relying on the physical kinetic energy reserves caused by the pre-displacement task, reduced the comprehensive response time from the interruption processing unit receiving the trigger signal to the resource node actually reaching the target position to 8.4 minutes. Resource nodes in the exclusive state and already on the displacement path converted virtual scheduling credentials into physical execution work orders, maintaining a 98.1% on-time demand fulfillment rate in a complex environment containing random time jitter deviations. The above data chain objectively presents the actual process of using an administrative pre-set strategy based on uncertainty quantification to offset physical cold start delays, and relying on the dynamic life cycle management mechanism of virtual scheduling credentials to convert probability prediction into physical kinetic energy reserves, resolving the temporal misalignment contradiction between the issuance of digital scheduling instructions and physical spatial displacement under the single serial response mode.

[0045] Example 3: This example combines Figures 1 to 2 A description of an internet-based elderly care service system, such as... Figure 1As shown, the data acquisition unit obtains historical service demand data within the target area and passes this data down to the feature extraction and grid partitioning steps to calculate the demand distribution entropy, partition the business management grid, and determine the resource pre-occupancy weight of the grid. The resource pre-occupancy weight is then output to the resource management model construction step, where it is used as the load attribute of each grid node in the resource management model. When the resource pre-occupancy weight exceeds a preset load threshold, the node locking and credential generation steps are triggered. When the load threshold is exceeded, the qualified matching resource node is locked, a virtual scheduling credential is generated, and its state is set to exclusive. The locked exclusive resource node is then output to the response readiness calculation step to obtain the static duration of the resource node and calculate the initial response lag, which characterizes the response readiness deviation. The initial response lag is then output to the pre-displacement task generation step to generate a pre-displacement task based on the initial response lag, driving the node to move towards the high-probability demand area before the formal work order. Finally, the pre-displacement task is issued to drive the node to generate a real displacement, enabling the resource node to reach the high-probability demand area in the business management grid.

[0046] like Figure 2 As shown, the target area / business management grid includes terminal devices, communication base stations, and administrative check-in points. The terminal devices send external trigger signals to the Internet-based elderly care service system. The Internet-based elderly care service system includes a data acquisition unit, a logic operation unit, a resource configuration unit, and an interrupt handling unit. The Internet-based elderly care service system sends virtual scheduling credentials / pre-displacement tasks to resource nodes in exclusive mode. The interrupt handling unit converts the instructions into physical execution work orders and transmits them to the resource nodes in exclusive mode. At the same time, the resource nodes in exclusive mode point to the communication base station in the target area / business management grid along the path indicating the preset displacement target.

[0047] Example 4: During the initialization phase of system deployment, the system establishes the resource pre-occupancy weight mapping relationship and the calculation benchmark of the preset load threshold. The data acquisition unit acquires full-volume anonymized historical service demand data covering the target area for the past 36 months. The logic operation unit extracts the service consumption peak and trough characteristic parameters of different business management grids within each historical time window from the historical service demand data. The logic operation unit constructs a resource management model corresponding to the target area and instantiates the resource management model at the data layer as a multi-layer weighted directed graph with the spatial geometric center of the business management grid as the node and the road network passage time as the directed edge. The load attribute of the grid node is initially set to zero. The logic operation unit divides the historical service demand data into discrete statistical intervals of 10 minutes in length and filters out the set of interruption intervals at the peak of service demand. The system calculates the statistical lower quartile based on the historical demand distribution entropy within the set of interruption intervals and sets the lower quartile as the sensitive trigger benchmark value of the demand distribution entropy.

[0048] The logic operation unit will calculate the demand distribution entropy in real time. The system maps the pre-occupancy weights to specific resource pre-occupancy weights. When the demand distribution entropy is lower than the sensitive triggering baseline value, the logic operation unit outputs a constant basic maintenance weight as the resource pre-occupancy weight. When the demand distribution entropy is greater than or equal to the sensitive triggering baseline value, the logic operation unit calculates the difference between the demand distribution entropy and the sensitive triggering baseline value, multiplies the difference by a preset amplification factor, and outputs a dynamically increasing value as the resource pre-occupancy weight. The resource pre-occupancy weight is then used as the load attribute of each grid node in the resource management model. The system extracts the mean of the resource pre-occupancy weights of all adjacent grid nodes in the multi-layer weighted directed graph within the preceding 24-hour sliding window, calculates the standard deviation of the mean, and sets the sum of the mean and 1.5 times the standard deviation as the current preset load threshold for a specific business management grid. The preset load threshold is updated in real time based on the fluctuation of the resource pre-occupancy weights of adjacent grid nodes.

[0049] The resource configuration unit compares the current resource pre-occupancy weight with the preset load threshold. When the resource pre-occupancy weight exceeds the preset load threshold, it locks the resource node that meets the qualification tag within the scheduling logic, generates a virtual scheduling certificate uniquely bound to the resource node, and sets the availability status of the resource node to exclusive. The resource configuration unit generates a pre-displacement task containing the virtual scheduling certificate and sends it to the resource node, driving the resource node to move to the high-probability demand area in the business management grid before receiving the formal service work order, and adjusts the resource availability distribution according to the physical load change trend.

[0050] Example 5: Before the system accesses all historical data of de-identified service requests and initiates routine scheduling, a discrete physical performance benchmark for different resource nodes is established. The resource allocation unit divides all candidate execution individuals in the target area into independent calibration sets based on skill qualification tags. It extracts the physical displacement logs of each calibration set in response to sudden commands over the past six months from the historical data of all de-identified service requests. The logic operation unit extracts the time difference set from receiving the scheduling command to the actual change in geographical coordinates for each calibration set, calculates the statistical median of this time difference set, divides the median by the standard response time base set set by the system, and outputs the dimensionless basic hysteresis ratio. The resource allocation unit uses the basic hysteresis ratio as an input variable and substitutes it into the set linear mapping function to calculate the response readiness coefficient corresponding to the specific skill qualification tag. The initial response lag The calculation process is anchored to the objective physical activation characteristics of nursing resources at different levels.

[0051] Facing the entropy of demand distribution The first threshold is the fluctuation boundary in the heterogeneous grid topology. The system synchronously executes a specific grid baseline debugging procedure. The data acquisition unit continuously inputs a standard environmental stress test dataset containing gradient density fluctuations into the logic operation unit to drive the model to generate a continuous entropy evolution sequence. The resource allocation unit synchronously tracks the degree of fit between the frequency of virtual scheduling certificate generation triggered by this sequence and the actual resource overload state in the test environment. The algorithm is used to adjust the calibration parameters of the first gradient one by one until the issuance ratio of non-matched pre-displacement tasks drops to the safety limit benchmark value. The system locks the calibration parameters in this convergence state as the exclusive first threshold of this specific business management grid, completes the offline calibration and baseline debugging procedure, extracts 12 months of historical service demand data of the target area, calculates the entropy distribution characteristics of discrete statistical intervals, selects the 75th quantile of the distribution sequence statistics as the first threshold, and establishes a conversion rate feedback adjustment mechanism. When the proportion of pre-displacement task conversion entity execution work orders is less than 15%, the first threshold is adjusted up in steps of 5% until the proportion is in the preset target range of 20% to 30%, thus completing the sensitivity calibration of the business management grid. To characterize the entropy of the uncertainty demand distribution.

[0052] Example 6: When the system enters normal operation, establish a resource status verification and compensation procedure to address physical cold start deviations; the logic operation unit receives the resource displacement coordinate trajectory sequence from the data acquisition unit, extracts features, and compares the physical initial velocity of the trajectory sequence with the initial response lag. The theoretical initial velocity is derived from the set linear mapping function; when the difference between the two exceeds the set tolerance limit, the tolerance limit is set to 15s. If the actual startup time exceeds the theoretical calculation value, the current value of the response preparation coefficient is increased by a fixed step of 0.02, and vice versa, the value is decreased by a step of 0.01, until the error fluctuation range of 5 consecutive samples is reduced to within 3s, thus completing the dynamic adaptation to the physical startup characteristics of the specific resource node; the logic operation unit records the difference as an implicit negative event record for the specific resource node; the resource allocation unit, for resource nodes with implicit negative event records, adjusts the response preparation coefficient... An additional compensation gain is added, and the subsequent calculation formula is used to correct it: Initial response hysteresis of the output ,in The initial response lag of the computational output is determined by the static duration of the resource node. The parameter feedback module records the time taken for a resource node to receive a pre-displacement task and change its geographic coordinates to initiate physical startup, approximating the actual physical startup characteristics of the resource node. Input into the regression model and preset response preparation coefficients Establish a connection, To characterize the deviation in resource node response readiness, a quantification coefficient is used. The actual physical startup time of the resource node, for five consecutive samples. All exceed the formula Output initial response hysteresis 20%, according to With static duration Real-time ratio calculation Mean and overwrite the write, causing the initial response to lag. Characterize the objective physical activation characteristics of nursing resources at different qualification levels. The duration of stillness. This represents the initial response lag.

[0053] When the system faces high-density community conditions where communication base station signal coverage is attenuated, the system introduces an asynchronous displacement confirmation procedure to handle abnormal virtual scheduling credentials. When the resource allocation unit issues a pre-displacement task containing virtual scheduling credentials, it simultaneously writes a local countdown attribute independent of the central system clock into the virtual scheduling credentials. During the execution of the pre-displacement task, if the resource node detects an interruption in the connection with the logical topology mesh model constructed by the logic operation unit, it switches to local timing mode and monitors the countdown attribute. If the network connection is not restored within the set safety buffer period and no entity execution work order is received, the resource node invalidates the virtual scheduling credentials locally according to the set release logic, and sends an allocatable state identifier back to the logic operation unit when the network connection is restored. This allows the resource node to autonomously release the exclusive state during the communication interruption, maintaining the update continuity of resource availability distribution in the logical topology mesh model.

[0054] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An internet-based elderly care service system, characterized in that, The system includes: The data acquisition unit is used to acquire historical data on service demand within the target area; The logic operation unit, connected to the data acquisition unit, is used to extract features from historical service demand data. By calculating the negative value of the sum of the probabilities of service demand occurrence within the grid and their logarithmic products, it determines the demand distribution entropy, which represents uncertainty, divides the target area into multiple business management grids, and determines the resource pre-occupancy weight of the business management grids based on the demand distribution entropy. The logic operation unit is also used to construct a resource management model for the corresponding target area and use the resource pre-occupancy weight as the load attribute of each grid node in the resource management model. The resource configuration unit, connected to the logic operation unit, is used to lock resource nodes that meet the qualification tags within the scheduling logic when the resource pre-occupancy weight exceeds the preset load threshold, generate a virtual scheduling certificate uniquely bound to the resource node, and set the availability status of the resource node to exclusive state. The resource configuration unit is also used to obtain the static duration of the resource node and calculate the initial response lag based on the static duration, which characterizes the deviation of the resource node's response readiness. The resource configuration unit is also used to generate a pre-displacement task containing the virtual scheduling certificate based on the initial response lag and send it to the resource node, driving the resource node to move to the high-probability demand area in the business management grid before receiving a formal service work order.

2. The Internet-based elderly care service system according to claim 1, characterized in that, The resource configuration unit is also used to set a dynamic validity period attribute for the virtual scheduling certificate, monitor its corresponding locking permission score during the duration of the virtual scheduling certificate, calculate the decay rate of the locking permission score based on the dynamic validity period attribute, and execute the virtual scheduling certificate invalidation processing when the locking permission score drops to the preset recycling limit and no real-time service work order trigger signal is generated in the business management grid, so as to reset the availability status of the resource node from the exclusive state to the allocable state.

3. The Internet-based elderly care service system according to claim 1, characterized in that, The logic unit calculates the demand distribution entropy. At that time, the following quantification rules shall be executed: ,in, Let n be the probability of the demand occurring in the i-th grid of the business management grid within a preset sampling period, and n be the total number of business management grids in the target area.

4. The Internet-based elderly care service system according to claim 1, characterized in that, The resource configuration unit also includes a qualification matching module, which is used to obtain the skill qualification tags of resource nodes, map the skill qualification tags to the task types of the overloaded grid in the business management grid, and select resource nodes with a skill qualification matching degree greater than 0.9 as the binding objects of virtual scheduling credentials.

5. The Internet-based elderly care service system according to claim 1, characterized in that, The resource allocation unit calculates the initial response lag. When this happens, the following rules will be applied: ,in, The duration of stillness. Prepare the preset response coefficients.

6. The Internet-based elderly care service system according to claim 1, characterized in that, Pre-displacement tasks correspond to low-priority prior administrative tasks. When generating pre-displacement tasks, the resource allocation unit searches for preset communication base stations or administrative check-in points in high-probability demand areas and sets the process of going to the communication base station to perform equipment inventory or health check-in as the preset displacement target of the resource node.

7. The Internet-based elderly care service system according to claim 1, characterized in that, The system also includes an interrupt handling unit, which monitors external trigger signals in real time. When an external trigger signal is received, it identifies the real-time care needs corresponding to the external trigger signal and converts the virtual scheduling credentials of the resource node in exclusive state into an physical execution work order.

8. The Internet-based elderly care service system according to claim 1, characterized in that, When performing feature extraction, the logic operation unit extracts the following feature dimensions: response timing threshold, resource node qualification, and settlement confirmation data. Based on the response timing threshold and settlement confirmation data, a scheduling triggering benchmark is established on the time axis.

9. The Internet-based elderly care service system according to claim 1, characterized in that, The density of the business management grid is determined by the spatial distribution density of historical service demand data. In areas where the density of historical service demand data is higher than the preset distribution threshold, the unit area of ​​the business management grid is reduced to improve the spatial identification accuracy of resource pre-occupancy weight.

10. The Internet-based elderly care service system according to claim 1, characterized in that, The system also includes a parameter feedback module, which records the actual arrival time of resource nodes after they perform displacement, calculates the time difference between the actual arrival time and the time when the pre-displacement task is issued, and iteratively corrects the response preparation coefficient based on the time difference.

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