Cloud computing-based integrated childcare service resource sharing system and method

Abstract

The present invention discloses a cloud computing-based integrated childcare service resource sharing system and method, specifically relating to the technical field of childcare service resource scheduling, and is used to solve the existing problem of the contradiction between static resource allocation rules and real-time supply and demand changes due to the strong volatility of the spatiotemporal distribution of childcare resources; by real-time collection of childcare service demand data and associating time window attributes, the state of the institutional resource pool is dynamically analyzed to generate a regional resource state matrix, and priority modifiers are extracted in combination with policy constraints to construct an access whitelist; based on the betweenness centrality algorithm, key hub nodes are identified and a dual-link disaster recovery network is created to achieve elastic generation of cross-regional resource scheduling strategies; by executing a state feedback closed loop to correct resource supply capacity and quota parameters, a policy self-optimization mechanism is formed; resource scheduling efficiency and system fault tolerance are significantly improved, and the reliability of efficient coordination of regional childcare resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of childcare service resource scheduling, and more specifically, to a cloud computing-based integrated childcare service resource sharing system and method. Background Art

[0002] Currently, the childcare service sector uses cloud computing technology to build a resource sharing platform to achieve cloud-based integration of educational content, operational data and other resources, forming a resource management system based on a distributed architecture; existing technologies mainly adopt a static resource configuration model, relying on preset load balancing strategies to handle routine service requests, and establishing fixed allocation rules through historical data. Under stable demand scenarios, basic service capabilities can be maintained, and basic resource interoperability between institutions can be achieved through unified storage and authority management, forming a regional childcare service network.

[0003] However, traditional methods have significant flaws when dealing with the dynamic and heterogeneous demands unique to childcare services. That is, due to the strong volatility in the spatiotemporal distribution of childcare resources (such as service migration caused by a surge in regional demand during holidays), static resource allocation rules conflict with real-time changes in supply and demand, which can easily lead to chain resource mismatches, specifically manifested in the failure of cross-regional service coordination, delayed response to sudden demands, and imbalanced resource utilization, affecting the elastic expansion capabilities and service quality stability of the childcare service system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a cloud computing-based integrated childcare service resource sharing system and method to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The cloud computing-based method for sharing resources of integrated childcare services includes the following steps:

[0007] S1. Collect childcare service demand data including demand timestamps;

[0008] S2. Analyze the real-time status of the childcare institution resource pool in the cloud computing platform and generate a regional resource status matrix that includes resource supply capacity and administrative division policy constraints;

[0009] S3. Extract priority modifiers from administrative division policy constraints, generate scheduling weight parameters based on the intensity levels of the priority modifiers to construct a dynamic access whitelist;

[0010] S4. Identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold based on a betweenness centrality algorithm, and generate backup scheduling paths for the key hub nodes to form a dual-link resource supply network;

[0011] S5. Generate a cross-region resource scheduling strategy based on the time window attribute associated with the demand timestamp, combined with the dynamic admission whitelist and the dual-link resource supply network;

[0012] S6. Obtain the execution status data of the cross-region resource scheduling strategy and update the regional resource status matrix through feedback correction.

[0013] In a preferred embodiment, collecting childcare service demand data including demand timestamps includes:

[0014] Periodically collect childcare service demand data through IoT terminals, including demand timestamps;

[0015] Verify the validity of the required timestamp and filter out invalid data whose timestamp outliers exceed the preset time window;

[0016] Convert the childcare service demand data after validity verification into a standardized format, which includes a demand timestamp field and a service type identification field;

[0017] Store childcare service demand data in a standardized format into the distributed message queue of the cloud computing platform.

[0018] In a preferred embodiment, the real-time status of the childcare institution resource pool in the cloud computing platform is analyzed to generate a regional resource status matrix containing resource supply capacity and administrative division policy constraints, including:

[0019] Obtain the real-time available childcare places and maximum capacity of each institution in the childcare institution resource pool through the polling interface, and calculate the resource supply capacity as the percentage of the real-time available childcare places and the maximum capacity;

[0020] Extract the original text data of administrative division policy constraints from the data platform interface, and parse the cross-region service quota ratio and priority service object clauses in the original text data;

[0021] Construct a regional resource status matrix, where the row dimension is the childcare institution identifier, and the column dimension includes resource supply capacity, cross-regional service quota ratio, and priority service object code; the priority service object code is the structured data identifier converted from the priority service object terms;

[0022] The regional resource status matrix is associated with the service type identification field and then stored in the time series database of the cloud computing platform.

[0023] In a preferred embodiment, priority modifiers in administrative division policy constraints are extracted, and scheduling weight parameters are generated according to the intensity levels of the priority modifiers to construct a dynamic admission whitelist, including:

[0024] The priority modifiers are located in the text paragraphs of administrative division policy constraints. The priority modifier location rule is as follows: in the sentences containing policy-oriented modifiers, the modifying phrases that are close to the service object are extracted;

[0025] Determine the dynamic weight coefficient based on the semantic strength of the modifying phrase, which is quantified by the depth of the context of the modifying phrase in the policy text and the number of related policy clauses;

[0026] The base weight corresponding to the service type identification field and the dynamic weight coefficient are combined according to a preset superposition rule to generate a scheduling weight parameter; the preset superposition rule is that when the priority service object code matches the service type identification field, the coefficient is accumulated, otherwise the coefficient is reduced;

[0027] The access threshold is dynamically adjusted according to the ratio of cross-regional service quotas in the regional resource status matrix, and the identifications of childcare institutions whose scheduling weight parameters exceed the dynamically adjusted access threshold are written into the dynamic access whitelist according to the service type classification.

[0028] In a preferred embodiment, a betweenness centrality algorithm is used to identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold, and an alternative scheduling path is generated for the key hub nodes to form a dual-link resource supply network, including:

[0029] The topological network of the childcare institution resource pool is traversed using the betweenness centrality algorithm. The shortest path intermediary ratio of the node in the cross-regional resource scheduling path is calculated. Key hub nodes are selected based on the cross-region service quota ratio in the dynamic access whitelist. Key hub nodes must meet the requirements of both betweenness values exceeding the dynamically set threshold and not triggering cross-region service quota alarms.

[0030] Generate a geographically isolated backup dispatch path for each key hub node. The conditions for determining geographical isolation include that the spatial straight-line distance between the backup dispatch path and the main path is not less than the preset minimum distance, and the difference in resource supply capacity between the childcare institution to which the backup dispatch path belongs in the regional resource status matrix and the resource supply capacity of the main path node does not exceed the preset tolerance range;

[0031] A dual-link resource supply network is constructed, and the relationship between the main path and the backup scheduling path is dynamically weighted based on the priority service object code in the regional resource status matrix. The weight allocation rule is that when the service type identification field of the main path node matches the priority service object code, the main path weight ratio is increased to the preset upper limit value.

[0032] In a preferred embodiment, the dynamically set threshold is the product of the regional demand mean square error value and a preset safety margin, and the regional demand mean square error value is calculated based on the period demand fluctuations recorded in historical service records.

[0033] In a preferred embodiment, a cross-region resource scheduling strategy is generated based on the time window attribute associated with the demand timestamp, combined with a dynamic admission whitelist and a dual-link resource supply network, including:

[0034] Service requests are divided into peak hours and off-peak hours based on the time window attribute associated with the demand timestamp. The peak hours are defined as the core service time window.

[0035] Dynamically adjust the admission rules of the dynamic admission whitelist. During the core service time window, only requests whose service type identification field in the dynamic admission whitelist completely matches the priority service object code are allowed to enter the scheduling queue.

[0036] Based on the weight distribution results of the dual-link resource supply network, the main path weight ratio and the cross-region service quota ratio are dynamically prioritized according to the service type identification field to generate the initial scheduling strategy;

[0037] The initial scheduling strategy is checked for cross-administrative division policy constraints. When the main path weight ratio exceeds the cross-region service quota ratio, the main path weight is proportionally reduced and the backup path weight is simultaneously increased to generate the final cross-regional resource scheduling strategy.

[0038] In a preferred embodiment, the rules for dynamic policy priority sorting are: when the service type identification field is consistent with the priority service object code in the dynamic access whitelist, the main path weight ratio is directly used as the basis for priority sorting; when the service type identification field is inconsistent with the priority service object code, the absolute value of the difference between the cross-region service quota ratio and the main path weight ratio is used as the basis for priority sorting.

[0039] In a preferred embodiment, obtaining the execution status data of the cross-region resource scheduling strategy and updating the regional resource status matrix through feedback correction include:

[0040] Collect execution status data of cross-region resource scheduling policies through distributed monitoring nodes. The execution status data includes resource scheduling success rate, path switching frequency, and actual utilization rate of cross-region service quotas.

[0041] Calculate the deviation between the resource scheduling success rate and the target success rate in the regional resource status matrix, as well as the deviation between the actual utilization rate of cross-region service quotas and the preset quota ratio;

[0042] When any deviation exceeds the preset threshold, the resource supply capacity value in the regional resource status matrix is adjusted downward according to the first preset ratio, and the cross-region service quota ratio value is adjusted upward according to the second preset ratio;

[0043] Synchronize the corrected regional resource status matrix to the time series database and trigger the reload of dynamic access whitelist rules.

[0044] On the other hand, the present invention provides a cloud computing-based integrated childcare service resource sharing system, comprising:

[0045] Demand collection module: collects childcare service demand data including demand timestamps;

[0046] Resource analysis module: Analyzes the real-time status of the childcare institution resource pool in the cloud computing platform and generates a regional resource status matrix that includes resource supply capacity and administrative division policy constraints;

[0047] Access construction module: extracts priority modifiers from administrative division policy constraints, generates scheduling weight parameters based on the intensity level of the priority modifiers to construct a dynamic access whitelist;

[0048] Dual-link generation module: This module uses a betweenness centrality algorithm to identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold, and generates backup scheduling paths for these key hub nodes to form a dual-link resource supply network.

[0049] Policy generation module: Generates cross-region resource scheduling strategies based on the time window attributes associated with the demand timestamp, combined with the dynamic admission whitelist and dual-link resource supply network;

[0050] Feedback correction module: obtains the execution status data of the cross-region resource scheduling strategy and updates the regional resource status matrix through feedback correction.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. By dynamically integrating the real-time demand and resource supply status of childcare services, the contradiction of the traditional static resource allocation model in responding to temporal and spatial fluctuations in demand is effectively resolved; based on the collection of demand timestamps and the division of time window attributes, the difference in resource demand between peak and off-peak periods can be accurately identified. Combined with the priority scheduling mechanism of the dynamic access whitelist, it ensures a rapid response to emergency or high-priority service requests. By building a dual-link resource supply network and pre-setting geographically isolated backup paths at key hub nodes, the reliability and disaster recovery capabilities of cross-regional resource scheduling are significantly improved, avoiding service interruptions caused by single point failures or local resource runs; the dynamic update and feedback correction mechanism of the regional resource status matrix further enhances the flexibility and adaptability of resource allocation, so that resource supply capabilities keep pace with real-time demand changes, thereby reducing resource idleness and mismatch problems.

[0053] 2. By identifying key nodes through the betweenness centrality algorithm and combining it with the quantitative analysis of administrative division policy constraints, the resource scheduling strategy is connected with local management rules. The weight parameter generation mechanism of the dynamic access whitelist converts the policy semantic strength into a computable scheduling priority, avoiding resource rigidity caused by mechanical execution. The closed-loop optimization design of the cross-regional scheduling strategy enables continuous iteration based on actual execution results, maintaining the stability of service quality and elastic expansion capabilities in complex and changing childcare service scenarios, and improving the reliability of efficient coordination of regional childcare resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of the cloud computing-based childcare integrated service resource sharing method of the present invention;

[0055] Figure 2 This is a structural diagram of the cloud computing-based integrated childcare service resource sharing system of the present invention. DETAILED DESCRIPTION

[0056] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] Example 1: Figure 1 The present invention provides a method for sharing resources of integrated childcare services based on cloud computing, which includes the following steps:

[0058] S1. Collect childcare service demand data including demand timestamps;

[0059] S2. Analyze the real-time status of the childcare institution resource pool in the cloud computing platform and generate a regional resource status matrix that includes resource supply capacity and administrative division policy constraints;

[0060] S3. Extract priority modifiers from administrative division policy constraints, generate scheduling weight parameters based on the intensity levels of the priority modifiers to construct a dynamic access whitelist;

[0061] S4. Identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold based on a betweenness centrality algorithm, and generate backup scheduling paths for the key hub nodes to form a dual-link resource supply network;

[0062] S5. Generate a cross-region resource scheduling strategy based on the time window attribute associated with the demand timestamp, combined with the dynamic admission whitelist and the dual-link resource supply network;

[0063] S6. Obtain the execution status data of the cross-region resource scheduling strategy and update the regional resource status matrix through feedback correction.

[0064] S1. Collect childcare service demand data including demand timestamps, including:

[0065] Periodically collect childcare service demand data through IoT terminals, including demand timestamps;

[0066] Verify the validity of the required timestamp and filter out invalid data whose timestamp outliers exceed the preset time window;

[0067] Convert the childcare service demand data after validity verification into a standardized format, which includes a demand timestamp field and a service type identification field;

[0068] Store childcare service demand data in a standardized format into the distributed message queue of the cloud computing platform.

[0069] The specific process of periodically collecting childcare service demand data through IoT terminals is as follows: configure the IoT terminals to collect childcare service demand data at fixed time intervals, for example, triggering data collection every 5 minutes. IoT terminals include mobile application terminals used by parents and smart registration devices deployed by childcare institutions. Childcare service demand data includes the childcare demand timestamp submitted by parents through mobile applications. The demand timestamp is recorded as a string in the "YYYY-MM-DD HH:MM:SS" format, for example, "2024-10-01 09:00:00".

[0070] When validating the required timestamp, the preset time window is set from the current system time to the next 24 hours. If the required timestamp is earlier than the current system time or later than a time point in the next 24 hours, it is marked as an outlier. For example, if the current system time is 2024-09-30 15:00:00, the required timestamp "2024-10-01 09:00:00" is valid data, while the required timestamp "2024-09-30 14:00:00" is considered an outlier because it is earlier than the current system time. The system also verifies whether the required timestamp string format conforms to the regular expression rules. If the required timestamp string fails to match the rules, it is marked as invalid data. When converting the validity-verified childcare service demand data into a standardized format, the demand timestamp field is converted into a Unix timestamp integer value in seconds. For example, the demand timestamp "2024-10-01 09:00:00" is converted into an integer value 1696125600. The service type identification field is mapped into a two-digit code according to the childcare service classification rules. For example, full-day care service is mapped to the numeric code 01, half-day care service is mapped to the numeric code 02, and temporary care service is mapped to the numeric code 03. The converted standardized data record contains an integer timestamp field and a two-digit code field.

[0071] When storing childcare service demand data in a standardized format in the distributed message queue of the cloud computing platform, the distributed message queue middleware is used to distribute the standardized data records to different topic partitions according to the value of the service type identification field. For example, the standardized data record with the service type identification field as the digital code 01 is written to the partition named "full-day care demand", and the standardized data record with the service type identification field as the digital code 02 is written to the partition named "half-day care demand". The mapping relationship between the partition name and the service type identification field is pre-configured in the production-end settings of the message queue.

[0072] S2. Analyze the real-time status of the childcare institution resource pool in the cloud computing platform and generate a regional resource status matrix that includes resource supply capacity and administrative division policy constraints, including:

[0073] Obtain the real-time available childcare places and maximum capacity of each institution in the childcare institution resource pool through the polling interface, and calculate the resource supply capacity as the percentage of the real-time available childcare places and the maximum capacity;

[0074] Extract the original text data of administrative division policy constraints from the data platform interface, and parse the cross-region service quota ratio and priority service object clauses in the original text data;

[0075] Construct a regional resource status matrix, where the row dimension is the childcare institution identifier, and the column dimension includes resource supply capacity, cross-regional service quota ratio, and priority service object code; the priority service object code is the structured data identifier converted from the priority service object terms;

[0076] The regional resource status matrix is associated with the service type identification field and then stored in the time series database of the cloud computing platform.

[0077] The specific method for obtaining the real-time available childcare places and maximum capacity of each institution in the childcare resource pool through the polling interface is as follows: configure the cloud computing platform to send HTTP requests to each institution in the childcare resource pool once a minute. The institution returns JSON-formatted data on the real-time available childcare places and maximum capacity through the API interface. For example, institution A returns {"vacancy": 15, "capacity": 50}, indicating that the current available childcare places are 15 and the maximum capacity is 50. The resource supply capacity is calculated as the percentage of the real-time available childcare places and the maximum capacity. For example, the resource supply capacity of institution A is 15 / 50×100%=30%. The calculated result is rounded to two decimal places and stored as a floating-point value.

[0078] When extracting the original text data of administrative division policy constraints from the data platform interface, the RESTful API interface of the government data open platform is called to obtain a text file containing the cross-district service quota ratio and priority service object clauses. For example, the text contains the clauses "the proportion of children accepted across districts shall not exceed 10% of the total number of childcare places" and "priority is given to the admission needs of children with disabilities"; when parsing the text data, the cross-district service quota ratio value is extracted through regular expression matching, for example, the regular expression "the proportion of children accepted across districts shall not exceed (\d+)% of the total number of childcare places" is used to extract the value 10, and the priority service object clause is identified through keyword matching. For example, when "children with disabilities" is matched, the priority service object code 01 is generated.

[0079] When constructing the regional resource status matrix, the row dimension of the matrix is the childcare institution ID. Each institution ID corresponds to a unique code of a childcare institution in the resource pool. For example, the ID of institution A is DAYCARE_001. The column dimension contains the floating-point percentage value of resource supply capacity, the integer value of the cross-region service quota ratio, and the string of the priority service object code. For example, the matrix corresponding to institution A is [30.00, 10, "01"].

[0080] When associating the regional resource status matrix with the service type identifier field, the matrix data is categorized and stored according to the value of the service type identifier field. For example, the full-day care demand data with a service type identifier of 01 is associated with the data of institutions in the matrix whose resource supply capacity is greater than 20%. The associated data is stored in the time series database of the cloud computing platform. The time series database is indexed by the timestamp field. For example, the resource status record of institution A at 2024-10-01 09:00:00 is stored as {"timestamp": 1696125600, "institution_id": "DAYCARE_001", "supply": 30.00, "quota": 10, "priority": "01"}.

[0081] S3. Extract the priority modifiers in the administrative division policy constraints and generate scheduling weight parameters based on the intensity level of the priority modifiers to build a dynamic access whitelist, including:

[0082] The priority modifiers are located in the text paragraphs of administrative division policy constraints. The priority modifier location rule is as follows: in the sentences containing policy-oriented modifiers, the modifying phrases that are close to the service object are extracted;

[0083] Determine the dynamic weight coefficient based on the semantic strength of the modifying phrase, which is quantified by the depth of the context of the modifying phrase in the policy text and the number of related policy clauses;

[0084] The base weight corresponding to the service type identification field and the dynamic weight coefficient are superimposed according to a preset rule to generate a scheduling weight parameter. The preset superposition rule is that when the priority service object code matches the service type identification field, the coefficient is accumulated, otherwise the coefficient is reduced;

[0085] The access threshold is dynamically adjusted according to the ratio of cross-regional service quotas in the regional resource status matrix, and the identifications of childcare institutions whose scheduling weight parameters exceed the dynamically adjusted access threshold are written into the dynamic access whitelist according to the service type classification.

[0086] The specific operation process of locating priority modifiers from the text paragraphs constrained by administrative division policies is as follows: the policy text is divided into sentences, and sentences containing policy-oriented modifiers are detected sentence by sentence. The detection method is to match the verb-object structure phrases starting with policy-oriented modifiers through regular expressions. For example, in the policy text "priority acceptance of disabled children with local household registration", the verb-object phrase "priority acceptance" starting with "priority" is matched. At this time, the phrase and the service object subject it modifies "disabled children with local household registration" are extracted to form a binding relationship between the modified phrase and the service object subject "priority acceptance | disabled children with local household registration", and this binding relationship is marked as a priority modification unit to be processed.

[0087] It is worth noting that policy-oriented modifiers include but are not limited to:

[0088] Positive priority: such as "priority", "key guarantee", "priority arrangement";

[0089] Negative restrictions: such as "restrict", "prohibit", "temporarily not accept";

[0090] Conditional access: such as “priority when conditions are met” and “guaranteed within the quota”.

[0091] When determining the dynamic weight coefficient based on the semantic strength of the modified phrase, the quantification process of semantic strength is divided into two dimensions: the context depth is determined by analyzing the promulgating agency level of the policy clause where the priority modification unit is located. For example, the priority modification unit in the provincial policy document is assigned a value of 2, the municipal policy document is assigned a value of 1, and the district policy document is assigned a value of 0.5; the number of related policy clauses is counted by searching the number of times the priority modification unit is cited in other policy documents within the same administrative region. For example, "giving priority to the admission of children with disabilities" is cited twice in the Special Children's Education Guarantee Measures and once in the Social Welfare Institution Management Rules, so the number of related policy clauses is 3. The final dynamic weight coefficient calculation formula is the context depth value multiplied by the number of related policy clauses and then weighted summed. For example, if the context depth value of a modification unit in the provincial policy is 2 and the number of related clauses is 3, then the dynamic weight coefficient = 2×3=6.

[0092] When the benchmark weight and dynamic weight coefficient corresponding to the service type identification field are used to generate the scheduling weight parameter according to the preset superposition rule, the benchmark weight is set according to the historical demand frequency of the service type corresponding to the service type identification field. For example, the full-day care service type identification field is 01, the historical demand accounts for 60%, and the benchmark weight is 1.0. The half-day care service type identification field is 02, the historical demand accounts for 30%, and the benchmark weight is 0.5. The superposition rule is that when the priority service object code and the service type identification field completely match, the scheduling weight parameter = benchmark weight + dynamic weight coefficient. For example, when the service type identification field is 01 and the priority service object code is 01, the scheduling weight parameter = 1.0 + 6 = 7.0. When there is incomplete match, the scheduling weight parameter = benchmark weight × dynamic weight coefficient × 0.3. For example, when the service type identification field is 02 and the priority service object code is 01, the scheduling weight parameter = 0.5 × 6 × 0.3 = 0.9.

[0093] When the admission threshold is dynamically adjusted according to the cross-regional service quota ratio in the regional resource status matrix, the initial value of the admission threshold is set to 80% of the benchmark admission threshold. The benchmark admission threshold is calculated based on the average scheduling success rate of the past 30 days. For example, if the average scheduling success rate is 65%, the benchmark admission threshold is 65%×80%=52%. The dynamic adjustment rule is that for every 5 percentage points decrease in the cross-regional service quota ratio, the admission threshold is lowered by 2 percentage points. For example, when the cross-regional service quota ratio drops from 15% to 10%, the admission threshold is dropped from 52% to 50%. Finally, the childcare institution identifiers whose scheduling weight parameters exceed the dynamically adjusted admission threshold are written into the dynamic admission whitelist according to the service type classification. For example, when the scheduling weight parameter is 7.0 and the admission threshold is 50%, the institution identifier is written into the dynamic admission whitelist partition corresponding to the full-day care service type. When the scheduling weight parameter is 0.9 and the admission threshold is 50%, the institution identifier is excluded due to non-compliance.

[0094] S4. Identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold based on a betweenness centrality algorithm, and generate backup scheduling paths for the key hub nodes to form a dual-link resource supply network, including:

[0095] The topological network of the childcare institution resource pool is traversed using the betweenness centrality algorithm. The shortest path intermediary ratio of the node in the cross-regional resource scheduling path is calculated. Key hub nodes are selected based on the cross-region service quota ratio in the dynamic access whitelist. Key hub nodes must meet the requirements of both betweenness values exceeding the dynamically set threshold and not triggering cross-region service quota alarms.

[0096] Generate a geographically isolated backup dispatch path for each key hub node. The conditions for determining geographical isolation include that the spatial straight-line distance between the backup dispatch path and the main path is not less than the preset minimum distance, and the difference in resource supply capacity between the childcare institution to which the backup dispatch path belongs in the regional resource status matrix and the resource supply capacity of the main path node does not exceed the preset tolerance range;

[0097] A dual-link resource supply network is constructed, and the relationship between the main path and the backup scheduling path is dynamically weighted based on the priority service object code in the regional resource status matrix. The weight allocation rule is that when the service type identification field of the main path node matches the priority service object code, the main path weight ratio is increased to the preset upper limit value.

[0098] Among them, the dynamically set threshold is the product of the regional demand mean square deviation value and the preset safety margin. The regional demand mean square deviation value is calculated based on the period demand fluctuations recorded in historical service records.

[0099] The specific implementation process of traversing the topological network of the childcare institution resource pool through the betweenness centrality algorithm is as follows: the childcare institution resource pool is modeled as an undirected graph data structure, where each node represents a childcare institution, and the node attributes include the institution's unique code, geographic location coordinates, and resource supply capacity value. Each edge represents the resource scheduling path between two childcare institutions, and the edge weight value is calculated based on the path length and service type compatibility. For example, the weight value increases by 0.1 for every 1 km increase in path length, and the weight value increases by an additional 0.5 when the service type compatibility is inconsistent. For example, if the path length between institution A and institution B is 3 kilometers and the service types are compatible, then the edge weight = 3×0.1=0.3; if the path length between institution A (full-day care) and institution C (temporary care) is 2 kilometers but the service types are incompatible, then the edge weight = 2×0.1+0.5=0.7.

[0100] Based on this weighted undirected graph data structure, the shortest path of all cross-regional resource scheduling paths is calculated. A cross-regional resource scheduling path is defined as a path whose starting and ending points belong to different administrative regions. The proportion of the number of times each node appears in the cross-region shortest path to the total number of cross-region paths is counted as the betweenness value. For example, if institution A participates in 15 of the 50 cross-region shortest paths, the betweenness value = 15 / 50 = 30%.

[0101] When screening key hub nodes in combination with the cross-region service quota ratio value in the dynamic access whitelist, the cross-region service quota ratio value is the cross-region service quota ratio value predefined in the regional resource status matrix. For example, if the cross-region service quota ratio value is 10%, when the intermediary value of institution A exceeds the dynamically set threshold and the current cross-region service quota utilization rate does not exceed 10%, institution A is determined to be a key hub node. If the cross-region service quota utilization rate exceeds 10%, a quota alarm is triggered and the node is excluded. The quota utilization rate is calculated by dividing the current number of cross-region service requests by the total number of supported locations and then multiplying by 100%.

[0102] When generating a geographically isolated backup dispatch path for each key hub node, for example, the preset minimum interval is set to 5 kilometers, and the preset tolerance range is that the resource supply capacity of the childcare institution to which the backup dispatch path belongs is not less than 50% of the resource supply capacity of the main path node. For example, the resource supply capacity of institution A at the main path node is 40%, and the resource supply capacity of institution B, a candidate for the backup dispatch path, must be ≥20%, and the straight-line distance between institution B and institution A must be ≥5 kilometers. The straight-line distance is calculated by the difference in longitude and latitude of the geographic coordinates of the two places. Specifically, the spherical distance is calculated using the Haversine formula. After screening the candidate institutions that meet the conditions, the institution with the highest resource supply capacity is selected as the backup dispatch path node. If multiple candidate institutions have the same capacity, the institution with the farthest distance is selected to maximize the geographical isolation effect.

[0103] When building a dual-link resource supply network, dynamic weight allocation is performed based on the priority service object code in the regional resource status matrix. For example, if the service type identifier field of primary path node institution A is 01 (full-day care) and the priority service object code is 01 (priority for children with disabilities), the primary path weight is increased to 70%, and the backup path weight is 30%. If the service type identifier field is 01 but the priority service object code is 02 (priority for children of martyrs), the primary path weight is adjusted to 50%, and the backup path weight is 50%. The weight allocation result is written into the resource scheduling routing table and synchronized to the central scheduler of the cloud computing platform. An example routing table entry is {"primary path": "Institution A", "backup path": "Institution B", "primary weight": 70, "backup weight": 30}.

[0104] The process of generating the dynamically set threshold is as follows: obtain the resource demand data for each hour in the historical service records, calculate the mean square deviation of the demand in each period, for example, the mean square deviation of the demand from 8:00 to 9:00 on weekdays is 15 childcare places, and from 17:00 to 18:00 in the evening is 10 childcare places, take the maximum value of 15 childcare places as the benchmark mean square deviation value, and preset the safety margin to 1.2. The dynamically set threshold = 15 × 1.2 = 18 childcare places. When the betweenness value of the childcare institution exceeds 18 childcare places, the key hub node screening condition is triggered. The safety margin is set based on the maximum load redundancy coefficient of the childcare institution. For example, when the maximum load of the institution is 50 childcare places, a safety margin of 1.2 corresponds to an elastic capacity of 60 childcare places.

[0105] S5. Generate a cross-region resource scheduling strategy based on the time window attribute associated with the demand timestamp, combined with the dynamic admission whitelist and the dual-link resource supply network, including:

[0106] Service requests are divided into peak hours and off-peak hours based on the time window attribute associated with the demand timestamp. The peak hours are defined as the core service time window.

[0107] Dynamically adjust the admission rules of the dynamic admission whitelist. During the core service time window, only requests whose service type identification field in the dynamic admission whitelist completely matches the priority service object code are allowed to enter the scheduling queue.

[0108] Based on the weight distribution results of the dual-link resource supply network, the main path weight ratio and the cross-region service quota ratio are dynamically prioritized according to the service type identification field to generate the initial scheduling strategy;

[0109] The initial scheduling strategy is checked for cross-administrative division policy constraints. When the main path weight ratio exceeds the cross-region service quota ratio, the main path weight is proportionally reduced and the backup path weight is simultaneously increased to generate the final cross-regional resource scheduling strategy.

[0110] Among them, the rules for dynamic policy priority sorting are: when the service type identification field is consistent with the priority service object code in the dynamic access whitelist, the main path weight ratio is directly used as the basis for priority sorting; when the service type identification field is inconsistent with the priority service object code, the absolute value of the difference between the cross-region service quota ratio and the main path weight ratio is used as the basis for priority sorting.

[0111] The specific implementation process of dividing service requests into peak and off-peak hours based on the time window attribute associated with the demand timestamp is as follows: parse the date and hour fields in the demand timestamp, combine the average resource request volume for the same date and hour in historical service records, and define the time period in which the resource request volume continuously exceeds the resource supply capacity in the regional resource status matrix as the core service time window. For example, if historical data shows that the average request volume from 09:00 to 11:00 every Monday is 150 pallet slots, and the supply capacity for this period in the regional resource status matrix is 100 pallet slots, the system will automatically mark 09:00 to 11:00 every Monday as the core service time window, and mark the remaining time periods as flexible service time windows.

[0112] When dynamically adjusting the admission rules of the dynamic admission whitelist, the strict matching mode is activated within the core service time window. The strict matching mode is defined as allowing only requests whose service type identification field in the dynamic admission whitelist is exactly the same as the priority service object code to enter the scheduling queue. For example, institutions with a service type identification field of 01 (full-day care) and a priority service object code of 01 (priority for children with disabilities) in the dynamic admission whitelist are allowed to enter the queue, while requests from institutions with a service type identification field of 02 (half-day care) and a priority service object code of 01 will be intercepted, and the intercepted requests will be transferred to the manual review queue.

[0113] When generating the initial scheduling policy based on the weight allocation results of the dual-link resource supply network, the main path weight ratio and the cross-region service quota ratio are dynamically prioritized according to the service type identification field. The dynamic policy priority sorting rules are specifically implemented as follows: when the service type identification field is consistent with the priority service object code in the dynamic access whitelist, they are directly sorted from high to low according to the main path weight ratio. For example, if the main path weight ratio of institution A is 70% and that of institution B is 50%, then institution A has a higher priority than institution B. When the service type identification field and the priority service object code are inconsistent, the absolute value of the difference between the cross-region service quota ratio and the main path weight ratio is calculated, and the systems are sorted from small to large according to the absolute value of the difference. For example, if the cross-region service quota ratio is 10% and the main path weight ratio of institution C is 50%, then the absolute value of the difference is 40%. If the main path weight ratio of institution D is 15%, then the absolute value of the difference is 5%, and then institution D has a higher priority than institution C.

[0114] When verifying the initial scheduling strategy against cross-administrative region policy constraints, the verification rule is that if the primary path weight exceeds the cross-region service quota ratio, the primary path weight is reduced by the excess proportion and the backup path weight is increased simultaneously. For example, if the cross-region service quota ratio is 10% and the primary path weight of institution E is 15%, if it exceeds 5%, the primary path weight is reduced to 10% and the backup path weight is increased from 85% to 90%. After the final cross-region resource scheduling strategy is generated, the strategy details are written to the resource scheduling routing table. The routing table entries contain the primary path organization code, the backup path organization code, and the revised primary and backup path weights. For example, {"Primary Path": "DAYCARE_001", "Backup Path": "DAYCARE_002", "Primary Weight": 10, "Backup Weight": 90}. The routing table is synchronized in real time to the central scheduler of the cloud computing platform via the message queue.

[0115] S6. Obtain the execution status data of the cross-region resource scheduling strategy and update the regional resource status matrix through feedback correction, including:

[0116] Collect execution status data of cross-region resource scheduling policies through distributed monitoring nodes. The execution status data includes resource scheduling success rate, path switching frequency, and actual utilization rate of cross-region service quotas.

[0117] Calculate the deviation between the resource scheduling success rate and the target success rate in the regional resource status matrix, as well as the deviation between the actual utilization rate of cross-region service quotas and the preset quota ratio;

[0118] When any deviation exceeds the preset threshold, the resource supply capacity value in the regional resource status matrix is adjusted downward according to the first preset ratio, and the cross-region service quota ratio value is adjusted upward according to the second preset ratio;

[0119] Synchronize the corrected regional resource status matrix to the time series database and trigger the reload of dynamic access whitelist rules.

[0120] The specific method for collecting the execution status data of the cross-region resource scheduling strategy through distributed monitoring nodes is as follows: deploy a monitoring agent on each resource scheduling execution node of the cloud computing platform. For example, the monitoring agent counts the resource scheduling success rate, path switching frequency and actual utilization rate of the cross-region service quota at a frequency of once per minute. The resource scheduling success rate is calculated as the percentage of successfully completed childcare service requests to the total number of scheduling requests. For example, 100 requests are scheduled during the statistical period, of which 85 are successful, and the resource scheduling success rate is 85%; the path switching frequency is calculated as the number of times the main path switches to the backup path per unit time. For example, 3 main-backup path switches occur within 1 hour, and the path switching frequency is 3 times / hour; the actual utilization rate of the cross-region service quota is calculated as the percentage of the actually completed cross-region service requests to the total number of preset cross-region service quotas. For example, the total quota is 100 childcare places, 15 childcare places are actually used, and the actual utilization rate of the cross-region service quota is 15%.

[0121] When calculating the deviation between the resource scheduling success rate and the target success rate in the regional resource status matrix, the target success rate is determined based on the service quality indicator preset in the regional resource status matrix. For example, if the target success rate in the regional resource status matrix is 90%, and the actual scheduling success rate is 85%, the deviation is 90% − 85% = 5%. When calculating the deviation between the actual utilization rate of the cross-region service quota and the preset quota ratio, the preset quota ratio is extracted from the regional resource status matrix. For example, if the preset quota ratio is 10%, and the actual utilization rate is 15%, the deviation is 15% − 10% = 5%.

[0122] When any deviation exceeds the preset threshold, the preset threshold is determined based on the stability analysis of historical scheduling data. For example, the resource scheduling success rate deviation threshold is 5%, and the cross-region service quota deviation threshold is 3%. If the resource scheduling success rate deviation reaches 5% or the cross-region service quota deviation reaches 3%, the parameter correction rule is triggered, and the resource supply capacity value in the regional resource status matrix is lowered according to the first preset ratio. For example, the first preset ratio is set to 5% based on business needs, and the original resource supply capacity is 100 support positions, which is reduced to 95 support positions after the reduction; the cross-region service quota ratio value is increased according to the second preset ratio. For example, the second preset ratio is set to 3%, and the original cross-region service quota ratio is 10%, which is increased to 13%.

[0123] When synchronizing the revised regional resource status matrix to the time series database, the time series database establishes versioned storage based on the timestamp field. For example, the revised resource supply capacity value 95 and the cross-region service quota ratio value 13% are associated with the timestamp 2023-10-01T09:00:00+08:00 and stored as an independent data version. After the synchronization is completed, the dynamic admission whitelist rule reload mechanism is triggered. The dynamic admission whitelist loads the latest version of the regional resource status matrix from the time series database, recalculates the scheduling weight parameters, and updates the admission threshold.

[0124] Example 2: Figure 2 A schematic diagram of the structure of a cloud computing-based integrated childcare service resource sharing system of the present invention is provided. The cloud computing-based integrated childcare service resource sharing system includes:

[0125] Demand collection module: collects childcare service demand data including demand timestamps;

[0126] Resource analysis module: Analyzes the real-time status of the childcare institution resource pool in the cloud computing platform and generates a regional resource status matrix that includes resource supply capacity and administrative division policy constraints;

[0127] Access construction module: extracts priority modifiers from administrative division policy constraints, generates scheduling weight parameters based on the intensity level of the priority modifiers to construct a dynamic access whitelist;

[0128] Dual-link generation module: This module uses a betweenness centrality algorithm to identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold, and generates backup scheduling paths for these key hub nodes to form a dual-link resource supply network.

[0129] Policy generation module: Generates cross-region resource scheduling strategies based on the time window attributes associated with the demand timestamp, combined with the dynamic admission whitelist and dual-link resource supply network;

[0130] Feedback correction module: obtains the execution status data of the cross-region resource scheduling strategy and updates the regional resource status matrix through feedback correction.

[0131] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0132] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0133] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0134] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0136] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0138] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0139] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0140] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cloud computing-based method for sharing resources for integrated childcare services, characterized in that: The steps include: S1. Collect childcare service demand data including demand timestamps; S2. Analyze the real-time status of the childcare institution resource pool in the cloud computing platform and generate a regional resource status matrix that includes resource supply capacity and administrative division policy constraints; S3. Extract priority modifiers from administrative division policy constraints, generate scheduling weight parameters based on the intensity levels of the priority modifiers to construct a dynamic access whitelist; S4. Identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold based on a betweenness centrality algorithm, and generate backup scheduling paths for the key hub nodes to form a dual-link resource supply network; S5. Generate a cross-region resource scheduling strategy based on the time window attribute associated with the demand timestamp, combined with the dynamic admission whitelist and the dual-link resource supply network; S6. Obtain the execution status data of the cross-region resource scheduling strategy and update the regional resource status matrix through feedback correction.

2. The cloud computing-based integrated childcare service resource sharing method according to claim 1, characterized in that: Collect childcare service demand data with demand timestamps, including: Periodically collect childcare service demand data through IoT terminals, including demand timestamps; Verify the validity of the required timestamp and filter out invalid data whose timestamp outliers exceed the preset time window; Convert the childcare service demand data after validity verification into a standardized format, which includes a demand timestamp field and a service type identification field; Store childcare service demand data in a standardized format into the distributed message queue of the cloud computing platform.

3. The cloud computing-based integrated childcare service resource sharing method according to claim 1, characterized in that: Analyze the real-time status of the childcare institution resource pool in the cloud computing platform and generate a regional resource status matrix that includes resource supply capacity and administrative division policy constraints, including: Obtain the real-time available childcare places and maximum capacity of each institution in the childcare institution resource pool through the polling interface, and calculate the resource supply capacity as the percentage of the real-time available childcare places and the maximum capacity; Extract the original text data of administrative division policy constraints from the data platform interface, and parse the cross-region service quota ratio and priority service object clauses in the original text data; Construct a regional resource status matrix, where the row dimension is the childcare institution identifier, and the column dimension includes resource supply capacity, cross-regional service quota ratio, and priority service object code; the priority service object code is the structured data identifier converted from the priority service object terms; The regional resource status matrix is associated with the service type identification field and then stored in the time series database of the cloud computing platform.

4. The method for sharing resources of integrated childcare services based on cloud computing according to claim 1, characterized in that: Extract the priority modifiers in the administrative division policy constraints and generate scheduling weight parameters based on the intensity level of the priority modifiers to build a dynamic access whitelist, including: The priority modifiers are located in the text paragraphs of administrative division policy constraints. The priority modifier location rule is as follows: in the sentences containing policy-oriented modifiers, the modifying phrases that are close to the service object are extracted; Determine the dynamic weight coefficient based on the semantic strength of the modifying phrase, which is quantified by the depth of the context of the modifying phrase in the policy text and the number of related policy clauses; The base weight corresponding to the service type identification field and the dynamic weight coefficient are combined according to a preset superposition rule to generate a scheduling weight parameter; the preset superposition rule is that when the priority service object code matches the service type identification field, the coefficient is accumulated, otherwise the coefficient is reduced; The access threshold is dynamically adjusted according to the ratio of cross-regional service quotas in the regional resource status matrix, and the identifications of childcare institutions whose scheduling weight parameters exceed the dynamically adjusted access threshold are written into the dynamic access whitelist according to the service type classification.

5. The method for sharing resources of integrated childcare services based on cloud computing according to claim 1, characterized in that: Based on the betweenness centrality algorithm, key hub nodes with resource supply capabilities exceeding a dynamically set threshold are identified, and alternative scheduling paths are generated for the key hub nodes to form a dual-link resource supply network, including: The topological network of the childcare institution resource pool is traversed using the betweenness centrality algorithm. The shortest path intermediary ratio of the node in the cross-regional resource scheduling path is calculated. Key hub nodes are selected based on the cross-region service quota ratio in the dynamic access whitelist. Key hub nodes must meet the requirements of both betweenness values exceeding the dynamically set threshold and not triggering cross-region service quota alarms. Generate a geographically isolated backup dispatch path for each key hub node. The conditions for determining geographical isolation include that the spatial straight-line distance between the backup dispatch path and the main path is not less than the preset minimum distance, and the difference in resource supply capacity between the childcare institution to which the backup dispatch path belongs in the regional resource status matrix and the resource supply capacity of the main path node does not exceed the preset tolerance range; A dual-link resource supply network is constructed, and the relationship between the main path and the backup scheduling path is dynamically weighted based on the priority service object code in the regional resource status matrix. The weight allocation rule is that when the service type identification field of the main path node matches the priority service object code, the main path weight ratio is increased to the preset upper limit value.

6. The cloud computing-based integrated childcare service resource sharing method according to claim 5, characterized in that: The dynamically set threshold is the product of the regional demand mean square deviation value and the preset safety margin. The regional demand mean square deviation value is calculated based on the period demand fluctuations recorded in historical service records.

7. The method for sharing resources of integrated childcare services based on cloud computing according to claim 1, characterized in that: Based on the time window attributes associated with the demand timestamp, a cross-region resource scheduling strategy is generated by combining a dynamic admission whitelist and a dual-link resource supply network, including: Service requests are divided into peak hours and off-peak hours based on the time window attribute associated with the demand timestamp. The peak hours are defined as the core service time window. Dynamically adjust the admission rules of the dynamic admission whitelist. During the core service time window, only requests whose service type identification field in the dynamic admission whitelist completely matches the priority service object code are allowed to enter the scheduling queue. Based on the weight distribution results of the dual-link resource supply network, the main path weight ratio and the cross-region service quota ratio are dynamically prioritized according to the service type identification field to generate the initial scheduling strategy; The initial scheduling strategy is checked for cross-administrative division policy constraints. When the main path weight ratio exceeds the cross-region service quota ratio, the main path weight is proportionally reduced and the backup path weight is simultaneously increased to generate the final cross-regional resource scheduling strategy.

8. The cloud computing-based integrated childcare service resource sharing method according to claim 7, characterized in that: The rules for dynamic policy priority sorting are as follows: when the service type identification field is consistent with the priority service object code in the dynamic access whitelist, the main path weight ratio is directly used as the basis for priority sorting; when the service type identification field is inconsistent with the priority service object code, the absolute value of the difference between the cross-region service quota ratio and the main path weight ratio is used as the basis for priority sorting.

9. The cloud computing-based integrated childcare service resource sharing method according to claim 1, characterized in that: Obtain the execution status data of the cross-region resource scheduling strategy and update the regional resource status matrix through feedback correction, including: Collect execution status data of cross-region resource scheduling policies through distributed monitoring nodes. The execution status data includes resource scheduling success rate, path switching frequency, and actual utilization rate of cross-region service quotas. Calculate the deviation between the resource scheduling success rate and the target success rate in the regional resource status matrix, as well as the deviation between the actual utilization rate of cross-region service quotas and the preset quota ratio; When any deviation exceeds the preset threshold, the resource supply capacity value in the regional resource status matrix is adjusted downward according to the first preset ratio, and the cross-region service quota ratio value is adjusted upward according to the second preset ratio; Synchronize the corrected regional resource status matrix to the time series database and trigger the reload of dynamic access whitelist rules.

10. A cloud computing-based integrated childcare service resource sharing system, used to implement the cloud computing-based integrated childcare service resource sharing method according to any one of claims 1 to 9, characterized in that: include: Demand collection module: collects childcare service demand data including demand timestamps; Resource analysis module: Analyzes the real-time status of the childcare institution resource pool in the cloud computing platform and generates a regional resource status matrix that includes resource supply capacity and administrative division policy constraints; Access construction module: extracts priority modifiers from administrative division policy constraints, generates scheduling weight parameters based on the intensity level of the priority modifiers to construct a dynamic access whitelist; Dual-link generation module: This module uses a betweenness centrality algorithm to identify key hub nodes whose resource supply capacity exceeds a dynamically set threshold, and generates backup scheduling paths for these key hub nodes to form a dual-link resource supply network. Policy generation module: Generates cross-region resource scheduling strategies based on the time window attributes associated with the demand timestamp, combined with the dynamic admission whitelist and dual-link resource supply network; Feedback correction module: obtains the execution status data of the cross-region resource scheduling strategy and updates the regional resource status matrix through feedback correction.

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