A method and system for assisting in the planning of public service facilities in industrial parks

Abstract

This application belongs to the field of urban planning technology and discloses a method and system for assisting in the planning of public service facilities in industrial parks. By introducing service activity chains, accurately matching existing facilities, identifying peak usage periods, comprehensively assessing actual accessibility and carrying capacity, and providing intelligent early warning and planning suggestions, it can guide the planning of public service facilities in industrial parks more scientifically and accurately, effectively solve the problems of resource waste, service conflicts and experience degradation in existing technologies, and significantly improve the practicality and effectiveness of planning.

Description

Technical Field

[0001] This application relates to the field of urban planning technology, and more specifically, to a method and system for assisting in the planning of public service facilities in industrial parks. Background Technology

[0002] In urban planning, especially when emerging industrial parks are adjacent to established urban residential areas, the rational allocation of public service facilities to avoid redundant construction and ensure efficient resource utilization has always been a crucial consideration for planning departments. Existing planning methods typically begin by collecting detailed information on existing public service facilities in urban residential areas, such as their location, service area, and officially published service capacity. Next, based on the size of the industrial park and the expected number of businesses and employees, future service needs are estimated. Finally, this information is overlaid and analyzed using a geographic information system to determine whether the service area of ​​urban facilities can cover the industrial park, thus deciding which facilities can be shared and which need to be constructed independently by the park.

[0003] However, this seemingly scientific approach often encounters unexpected challenges in practical applications because it frequently overlooks the dynamic changes in user needs, subtle differences in service types, and the complexities of interrelationships between facilities. This can ultimately lead to discrepancies between planned solutions and actual service demands, resulting in service conflicts or a decline in user experience. For example, when planning public service facilities in industrial parks, existing methods may only consider the geographical coverage and nominal service capacity of the facilities to determine whether they can meet the park's needs, ignoring the unique work and rest patterns of industrial park employees and the peak usage times of each activity node in the service activity chain. This static and isolated assessment method cannot accurately predict whether existing facilities can effectively handle the concentrated demand from industrial park employees during specific peak periods, leading to problems such as insufficient capacity and service interruptions in actual use.

[0004] Furthermore, existing planning methods often rely on simple straight-line distances or pre-set service radii when assessing facility accessibility, failing to adequately consider the impact of actual traffic conditions and inclement weather on travel time and distance. This leads to inaccurate assessments of actual facility accessibility. Simultaneously, existing methods fail to deeply analyze the types of supporting services required at each node in different service activity chains, and the corresponding service accessibility thresholds for these types of services. Consequently, they cannot accurately define the dynamic accessibility range of supporting service facilities. This deficiency in assessing actual accessibility and carrying capacity makes it difficult for planning schemes to effectively identify potential disruptions in service activity chains, and hinders the timely generation of early warning information and targeted planning recommendations. Consequently, it affects the rational allocation and efficient utilization of public service facilities in industrial parks.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for assisting in the planning of public service facilities in industrial parks, which can guide the planning of public service facilities in industrial parks more scientifically and accurately, effectively solve the problems of resource waste, service conflict and experience decline in existing technologies, and significantly improve the practicality and effectiveness of planning.

[0007] Firstly, this application provides a method for assisting in the planning of public service facilities in industrial parks, comprising the following steps:

[0008] A1. Based on the type of target service needed by employees in the industrial park, query the service activity chain required to complete the target service; the service activity chain contains an ordered sequence of activity nodes and the type of supporting service required for each activity node;

[0009] A2. For each activity node in the service activity chain, determine the existing matching facilities located around the industrial park according to the supporting service type;

[0010] A3. Based on the industry type information of the industrial park, obtain the peak usage time of each activity node in the service activity chain for employees of the industrial park;

[0011] A4. For each activity node in the service activity chain, assess the actual accessibility of the corresponding existing matching facilities and their carrying capacity during peak usage periods;

[0012] A5. Based on the assessment results of the actual reachability and carrying capacity of each activity node, determine whether the service activity chain is interrupted, and generate early warning information and corresponding planning suggestions when the service activity chain is interrupted.

[0013] Secondly, this application provides an auxiliary planning system for public service facilities in industrial parks, the system comprising:

[0014] The query module is used to retrieve the service activity chain required to complete the target service based on the type of target service needed by the employees of the industrial park; the service activity chain includes an ordered sequence of activity nodes and the type of supporting service required for each activity node.

[0015] The matching module is used to determine the existing matching facilities located around the industrial park for each activity node in the service activity chain, based on the type of supporting service.

[0016] The time period identification module is used to obtain the peak usage time of each activity node in the service activity chain by employees of the industrial park based on the industry type information of the industrial park;

[0017] The evaluation module is used to evaluate the actual accessibility of the corresponding existing matching facilities and their carrying capacity during peak usage periods for each activity node in the service activity chain.

[0018] The auxiliary planning module is used to determine whether the service activity chain is interrupted based on the actual reachability and carrying capacity assessment results of each activity node, and to generate early warning information and corresponding planning suggestions when the service activity chain is interrupted.

[0019] Beneficial effects: The method and system for assisting in the planning of public service facilities in industrial parks provided in this application introduce service activity chains, accurately match existing facilities, identify peak usage periods, comprehensively assess actual accessibility and carrying capacity, and provide intelligent early warning and planning suggestions. This can guide the planning of public service facilities in industrial parks more scientifically and accurately, effectively solve the problems of resource waste, service conflicts and experience degradation in existing technologies, and significantly improve the practicality and effectiveness of planning. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for assisting in the planning of public service facilities in industrial parks, provided for this application.

[0021] Figure 2 This is a schematic diagram of an auxiliary planning system for public service facilities in an industrial park, provided for the purposes of this application.

[0022] Labeling Explanation: 1. Query Module; 2. Matching Module; 3. Time Period Identification Module; 4. Evaluation Module; 5. Auxiliary Planning Module. Detailed Implementation

[0023] The technical model of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] refer to Figure 1 This application proposes a method for assisting in the planning of public service facilities in industrial parks, including the following steps:

[0026] A1. Based on the type of target service needed by employees in the industrial park, query the service activity chain required to complete the target service; the service activity chain contains an ordered sequence of activity nodes and the type of supporting service required for each activity node;

[0027] A2. For each activity node in the service activity chain, determine the existing matching facilities located around the industrial park according to the supporting service type;

[0028] A3. Based on the industry type information of the industrial park, obtain the peak usage time of each activity node in the service activity chain for employees of the industrial park;

[0029] A4. For each activity node in the service activity chain, assess the actual accessibility of the corresponding existing matching facilities and their carrying capacity during peak usage periods;

[0030] A5. Based on the assessment results of the actual reachability and carrying capacity of each activity node, determine whether the service activity chain is interrupted, and generate early warning information and corresponding planning suggestions when the service activity chain is interrupted.

[0031] This application, by introducing the concept of service activity chains and comprehensively considering actual accessibility, peak usage capacity, and the dynamic needs of industrial park employees, can more accurately identify potential shortcomings in public service facilities and provide targeted planning suggestions, thereby effectively avoiding resource waste and improving service efficiency.

[0032] The term "industrial park" as used in this application refers to an area where one or more enterprises or institutions are clustered, characterized by a relatively concentrated group of employees and a specific type of industry. For example, a high-tech industrial park may mainly consist of R&D-oriented enterprises, whose employees have specific time patterns and preferences regarding public services such as catering, fitness, and commuting.

[0033] "Target services" refer to the various services needed by employees in the industrial park in their daily work and life, such as catering services, medical services, fitness services, commuting services, etc. These services are usually not isolated, but consist of a series of interconnected "service activity chains".

[0034] A "service activity chain" refers to a series of ordered "activity nodes" required to complete a specific service objective. For example, for the need to "exercise in the evening," the service activity chain might include activity nodes such as "going to sports facilities," "changing clothes," "exercising," "showering," "replenishing a light meal," and "returning to the park or accommodation." Each activity node may require specific "supporting service types." For instance, "going to sports facilities" and "returning to the park or accommodation" require transportation support services; "changing clothes," "exercising," and "showering" require corresponding changing, exercise, and showering facilities support services; and "replenishing a light meal" requires catering facilities support services.

[0035] "Existing matching facilities" refers to facilities that already exist in the area surrounding the industrial park and can provide the type of supporting services required for a certain activity node in the service activity chain. For example, for the "supplementary light meal" activity node, the supporting service type is catering facilities, so restaurants, canteens, etc. around the industrial park can be regarded as existing matching facilities.

[0036] "Peak usage period" refers to the time period during which the demand for a certain activity node in a service activity chain is the highest among employees in the industrial park.

[0037] "Actual accessibility" refers to the actual ease with which employees of an industrial park can reach a certain existing matching facility from the park. It takes into account not only geographical distance, but also factors such as traffic conditions and travel time.

[0038] "Carrying capacity" refers to the ability of an existing matching facility to provide services within a specific time period.

[0039] The proposed method for auxiliary planning of public service facilities in industrial parks aims to provide a scientific basis and auxiliary decision-making support for the planning of public service facilities in industrial parks through a comprehensive analysis of the aforementioned key elements.

[0040] In practical implementation, the auxiliary planning method for public service facilities in industrial parks proposed in this application can be carried out in the following manner:

[0041] First, in step A1, the system queries the service activity chain required to complete the target service based on the type of target service needed by the industrial park employees. This step can be implemented in various ways. For example, it can be done through manual surveys, such as questionnaires or interviews with industrial park employees, to understand their needs for various public services, and then manually sorting out the corresponding service activity chains based on the type of need. Another approach is to pre-establish a service activity chain knowledge base, which stores various target services, their corresponding service activity chains, and the supporting service types required for each activity node. When a target service type is received, the system can automatically query the knowledge base to obtain the corresponding service activity chain. For example, when the target service type is "daily commuting," the system can query the service activity chain, which includes activity nodes such as "departing from residence," "taking transportation," and "arriving at the industrial park," and identify the supporting service types required for each activity node, such as "transportation hub" and "public transportation."

[0042] Secondly, in step A2, for each activity node in the service activity chain, existing matching facilities located around the industrial park are determined based on the type of supporting service. This step can be implemented by manually marking all public service facilities around the industrial park on a map and manually determining whether they are existing matching facilities for the corresponding activity node based on the type of service they provide. For example, for the "dining" activity node, nearby restaurants, canteens, etc., can be manually identified. Another approach is to utilize Geographic Information Systems (GIS) and big data technology. For example, data on public service facilities in the area surrounding the industrial park can be collected in advance, including their location, service type, and operating hours, and stored in a database. When it is necessary to determine existing matching facilities, the system can filter and match in the database according to the type of supporting service required by the activity node, and combine this with geographical location information to determine existing matching facilities located around the industrial park. For example, for the "fitness" activity node, the system can automatically identify nearby gyms, stadiums, etc., that meet the criteria.

[0043] Secondly, in step A3, based on the industry type information of the industrial park, the peak usage periods for each activity node in the service activity chain by employees of the industrial park are obtained. This step can be implemented by: manually surveying companies within the industrial park to understand their work-rest patterns and employees' preferred times for different services, then manually summarizing and analyzing the data to determine peak usage periods. For example, for an industrial park primarily focused on software development, its employees may generally have a habit of working overtime, so the peak demand for dinner and late-night snack services may continue into the late hours. Another approach is to utilize big data analytics. For example, attendance data, access control data, and internal system usage data from each company within the industrial park can be collected, combined with company type information, to analyze employee work-rest patterns. Simultaneously, historical usage data of various public services by employees can be collected (e.g., through the park's app or third-party service platforms) to analyze employees' preferred times and preferences for each activity node. Through comprehensive analysis and modeling of this data, the peak usage periods for each activity node by employees of the industrial park can be automatically identified. For example, employees in a biomedical industrial park may have a strong demand for coffee at 10 a.m. and 3 p.m., so the peak usage time for coffee shops may occur at these times.

[0044] Next, in step A4, for each activity node in the service activity chain, the actual accessibility and carrying capacity of the corresponding existing matching facility during peak hours are evaluated. This step can be implemented by: conducting manual on-site inspections to measure the actual travel time or distance from the industrial park to the existing matching facility, and manually counting the facility's passenger flow during peak hours to assess its carrying capacity. For example, manually driving or walking to a restaurant to test the time taken and observing the queue situation during the lunch rush. Another approach is to utilize real-time traffic data and historical passenger flow data for evaluation. For example, real-time traffic data from the industrial park to the existing matching facility can be obtained (e.g., through a map API interface) to calculate the actual travel time or distance, thereby assessing actual accessibility. Simultaneously, historical passenger flow data or nominal service capacity data of the existing matching facility can be obtained, combined with employee and citizen demand during peak hours, to calculate the facility's carrying capacity during peak hours. For example, by analyzing a gym's historical card swipe data, its average carrying capacity during the evening rush hour can be estimated and compared with its maximum carrying capacity.

[0045] Finally, in step A5, based on the assessment results of the actual accessibility and carrying capacity of each activity node, it is determined whether the service activity chain is interrupted, and when the service activity chain is interrupted, early warning information and corresponding planning suggestions are generated. This step can be implemented in several ways: First, manual assessment can be used to subjectively determine whether the service activity chain is interrupted based on the assessment results, and planning suggestions can be given based on experience. For example, if a restaurant is found to have long queues during peak lunch hours, it can be manually judged as a service interruption, and additional catering facilities can be suggested. Second, quantitative assessment standards and interruption judgment rules can be set. For example, quantitative indicators and thresholds can be set for actual accessibility and carrying capacity. When either indicator falls below the threshold, it is determined that the service activity chain is interrupted at that activity node. The degree of interruption can be quantified based on the degree of deviation from the threshold. When a service activity chain is determined to be interrupted, the system can automatically generate early warning information and provide targeted planning suggestions based on preset rules or models. For example, if a transportation hub is found to have insufficient carrying capacity during peak commuting hours, the system can generate a "traffic congestion warning" and suggest "increasing service frequency" or "opening new commuter routes."

[0046] In summary, the auxiliary planning method for public service facilities in industrial parks proposed in this application, by introducing service activity chains, accurately matching existing facilities, identifying peak usage periods, comprehensively assessing actual accessibility and carrying capacity, and providing intelligent early warning and planning suggestions, can more scientifically and accurately guide the planning of public service facilities in industrial parks. It effectively solves problems such as resource waste, service conflicts, and decreased experience existing in current technologies, and significantly improves the practicality and effectiveness of planning.

[0047] In some implementations, step A2 includes steps performed for each active node in the service activity chain:

[0048] A201. Based on the types of supporting services required for this activity node, obtain the service accessibility threshold corresponding to the types of supporting services; the service accessibility threshold is a time threshold or a distance threshold;

[0049] A202. Based on the service accessibility threshold, define the dynamic accessibility range of the supporting service facilities;

[0050] A203. Within the dynamically accessible range, identify existing matching facilities located around the industrial park.

[0051] The service accessibility threshold is a quantitative standard for measuring the accessibility of service facilities, which can be expressed as a time threshold or a distance threshold. For example, for catering services, a time threshold can be set, such as a 15-minute walk; for medical services, a distance threshold can be set, such as a 5-kilometer drive. This threshold aims to quantify employees' acceptance of specific service facilities, ensuring that the identified matching facilities are conveniently accessible in actual use.

[0052] Furthermore, dynamic accessibility is a geographical area defined based on a service accessibility threshold. This area represents the region that employees can easily reach while meeting the service accessibility threshold. For example, if the service accessibility threshold is a 15-minute walk, then dynamic accessibility is all areas within a 15-minute walk from the industrial park. Defining this area helps to precisely define the geographical boundaries of existing matching facilities for searching.

[0053] Based on this, existing matching facilities located around the industrial park can be identified within the dynamically accessible range. This means that only facilities that meet both the supporting service type requirements and are located within the dynamically accessible range will be identified as valid existing matching facilities.

[0054] This application's solution refines the steps for identifying existing matching facilities by introducing the concepts of service accessibility thresholds and dynamic accessibility ranges. Specifically, firstly, by obtaining service accessibility thresholds corresponding to specific supporting service types, a quantitative standard is provided for subsequent facility screening. Secondly, based on these service accessibility thresholds, the dynamic accessibility range of supporting service facilities is precisely defined, ensuring that the considered facilities are geographically feasible. Finally, existing matching facilities are identified within the defined dynamic accessibility range, ensuring that the identified facilities not only meet service type requirements but also have high accessibility in actual use. This method avoids blind searches or simple judgments based solely on distance, improving the accuracy and practicality of facility matching.

[0055] The above technical solutions enable a more scientific and accurate assessment of the actual accessibility of existing facilities when identifying surrounding industrial parks. By introducing service accessibility thresholds and defining dynamic accessibility ranges accordingly, it ensures that identified existing facilities not only meet functional requirements but are also conveniently accessible geographically. This significantly improves the accuracy and practicality of auxiliary planning, providing industrial park employees with a more efficient and convenient service experience.

[0056] In some of the above embodiments, step A201 proposes obtaining a service accessibility threshold corresponding to the type of supporting service required for this activity node. However, in practical applications, simply obtaining a preset service accessibility threshold may not fully consider the dynamic environmental factors of the industrial park's location, such as traffic congestion and inclement weather. These factors significantly affect the actual time and distance for employees to reach service facilities. If these dynamic factors are not taken into account, the determined service accessibility threshold may deviate from the actual situation, thereby affecting the accuracy of subsequent assessments of the actual accessibility of existing matching facilities, and potentially leading to inaccurate planning recommendations.

[0057] Therefore, in some possible implementations, step A201 includes:

[0058] Based on the types of supporting services required for this activity node, obtain the preset baseline service accessibility threshold for the supporting service type; the baseline service accessibility threshold is a time threshold or a distance threshold.

[0059] Obtain the average traffic congestion index and the average percentage of severe weather days in the area where the industrial park is located; the average percentage of severe weather days is the average proportion of days with a preset type of severe weather in the total number of days in the year.

[0060] Based on the average traffic congestion index and the average proportion of severe weather days, calculate the traffic impact correction coefficient and the weather impact correction coefficient;

[0061] The baseline service accessibility threshold is corrected using the traffic impact correction coefficient and the weather impact correction coefficient to obtain the final service accessibility threshold corresponding to the supporting service type.

[0062] Specifically, the benchmark service accessibility threshold can be understood as the reasonable range of accessibility of supporting service facilities required to complete a specific service activity under ideal or standard conditions. This threshold can be time-based, such as "within a 15-minute walk" or "within a 30-minute public transportation distance," or distance-based, such as "within a 5-kilometer radius." This benchmark threshold can be preset based on industry standards, historical data, or expert experience.

[0063] To more accurately reflect actual accessibility, it is necessary to obtain the average traffic congestion index and the average percentage of severe weather days in the area where the industrial park is located. The average traffic congestion index can be obtained from real-time or historical data from traffic management departments, reflecting the comparison between traffic flow and road capacity in the area; a higher index indicates more severe congestion. The average percentage of severe weather days can be obtained from long-term statistical data from meteorological departments, such as the average percentage of days with preset types of severe weather like heavy rain, heavy snow, and heavy fog in the total number of days in a year. These data can quantify the external environmental factors affecting traffic efficiency.

[0064] Furthermore, based on the obtained average traffic congestion index and the average proportion of severe weather days, traffic impact correction coefficients and weather impact correction coefficients can be calculated. These correction coefficients aim to quantify the degree of impact of traffic congestion and severe weather on travel time or distance. For example, the traffic impact correction coefficient can be a multiplier greater than 1; the higher the traffic congestion index, the larger the coefficient, indicating a corresponding increase in actual travel time. Similarly, the weather impact correction coefficient can also be calculated based on the proportion of severe weather days, reflecting the negative impact of severe weather on traffic efficiency. The calculation methods for these correction coefficients can be based on empirical formulas, statistical models, or machine learning algorithms.

[0065] Therefore, the preset baseline service accessibility threshold is corrected using the calculated traffic impact correction coefficient and weather impact correction coefficient. The correction process typically involves multiplying or adding these correction coefficients to the baseline threshold to obtain a final service accessibility threshold that more closely reflects actual conditions. For example, if the baseline service accessibility threshold is a time threshold, the corrected final service accessibility threshold will be longer due to traffic congestion and severe weather; if the baseline service accessibility threshold is a distance threshold, the corrected final service accessibility threshold will be shorter due to reduced traffic efficiency, or the correction coefficient may be considered when calculating the actual travel distance.

[0066] This application's solution effectively addresses the disconnect between service accessibility assessments and actual conditions in traditional methods by introducing a dynamic correction mechanism for baseline service accessibility thresholds. Specifically, while baseline service accessibility thresholds provide a basic measure of service facility accessibility, they do not consider complex dynamic environmental factors in the real world. Traffic congestion index and the proportion of days with severe weather, as key external influencing factors, can directly quantify the objective conditions affecting traffic efficiency. By calculating corresponding traffic impact correction coefficients and weather impact correction coefficients, this solution can transform these external impacts into quantitative adjustments to the accessibility thresholds. For example, when traffic congestion is severe or severe weather is frequent, the correction coefficients make the final service accessibility thresholds more stringent (for time thresholds, this may mean a longer time to reach the destination, or for distance thresholds, a shorter distance to reach within the same time frame), thus more accurately reflecting the actual accessibility of service facilities under adverse conditions. This correction mechanism ensures that the subsequently defined dynamic accessibility range is more accurate, avoiding planning deviations caused by the neglect of environmental factors.

[0067] Through the above technical solution, this application provides a more accurate and dynamic method for determining service accessibility thresholds. Compared to solutions that only use preset benchmark thresholds, this solution fully considers the impact of actual traffic conditions and climate conditions in the industrial park area on the accessibility of service facilities. Therefore, the determined service accessibility thresholds can more accurately reflect the actual mobility of employees under different external environments, making subsequent assessments of the actual accessibility of existing matching facilities more reliable. This accurate assessment helps avoid planning defects caused by errors in accessibility judgments, such as avoiding the mistaken assumption of good service facility accessibility in traffic-congested areas or underestimating the difficulty for employees to obtain services in areas with frequent severe weather. Ultimately, this solution can significantly improve the scientific rigor and practicality of auxiliary planning for public service facilities in industrial parks, providing managers with more valuable planning suggestions, thereby better meeting the actual service needs of industrial park employees.

[0068] In one specific implementation, suppose we need to plan supporting facilities for a "lunch service" for employees in an industrial park. One activity node is the "dining location." Based on experience, the baseline service accessibility threshold for this "dining location" is set at "10 minutes' walk." To obtain a more accurate service accessibility threshold, the system first obtains the average traffic congestion index of the area where the industrial park is located. For example, by calling the urban traffic big data platform interface, the average traffic congestion index of the area over the past year is obtained as 1.3 (meaning the average travel time is 1.3 times the ideal situation). Simultaneously, the system obtains the average percentage of severe weather days in the area over the past year. For example, through a meteorological data interface, the average percentage of days with severe weather affecting travel, such as heavy rain or snow, is obtained as 0.05. Next, the system calculates a traffic impact correction coefficient based on the average traffic congestion index of 1.3, for example, set to 1.3. It also calculates a weather impact correction coefficient based on the average percentage of severe weather days of 0.05, for example, set to 1.1 (meaning severe weather will increase travel time by 10%). Finally, the system uses these two correction coefficients to adjust the baseline service accessibility threshold. The revised final service accessibility threshold can be calculated as: 10 minutes (baseline) × 1.3 (traffic correction) × 1.1 (weather correction) ≈ 14.3 minutes. This means that, after considering actual traffic and weather conditions, the reasonable time threshold for employees to walk to the "dining location" should be adjusted to approximately 14.3 minutes. This revised threshold will be used to subsequently define the dynamic accessibility of dining facilities, thereby ensuring that the planning results are more consistent with reality.

[0069] In some implementations, step A202 includes:

[0070] The area surrounding the industrial park is divided into multiple grid units;

[0071] Calculate the actual travel time or actual travel distance from the center point of each grid cell to the industrial park, and use it as the actual travel parameter;

[0072] The set of grid cells whose actual access parameters do not exceed the service accessibility threshold is determined as the dynamic accessibility range of the supporting service facilities.

[0073] Specifically, when determining the dynamic accessibility of supporting service facilities, the geographical area surrounding the industrial park first needs to be gridded. These grid cells can be pre-defined regular shapes, such as squares or hexagons, and their size can be selected according to the level of planning detail and data availability. Each grid cell represents a discrete spatial area of ​​the surrounding area, and its center point can serve as the representative location of that grid cell. The area surrounding the industrial park can be the region located outside the industrial park, within a pre-defined shape and size range centered on the industrial park's center point (e.g., a circle with a pre-defined radius, but not limited to this).

[0074] Subsequently, for each grid cell, the actual travel time or actual travel distance from its center point to the industrial park needs to be calculated. Here, "actual travel time" or "actual travel distance" refers to real-world travel data considering factors such as actual road conditions, traffic rules, and road network topology. For example, estimated travel times or distances for different modes of transportation (such as walking, driving, and public transportation) can be obtained by calling a map service API, and then these estimated results are combined (e.g., by performing a weighted average calculation) to obtain the final actual travel time or actual travel distance. These calculated values ​​are used as "actual travel parameters" to quantify the ease of reaching the grid cell from the industrial park.

[0075] Furthermore, the calculated actual accessibility parameters of each grid cell are compared with a preset service accessibility threshold. This threshold can be a time threshold (e.g., accessible within 15 minutes) or a distance threshold (e.g., accessible within 5 kilometers), the specific value of which depends on the planned service type and expected service level. A grid cell is considered accessible only if its actual accessibility parameters do not exceed this service accessibility threshold. The set of all grid cells that meet this condition is determined as the dynamic accessibility range of the supporting service facilities. This range dynamically reflects the geographical area that industrial park employees can easily reach under specific accessibility requirements.

[0076] This application's solution meticulously divides the area surrounding the industrial park into multiple grid units and precisely calculates the actual travel parameters from each grid unit to the industrial park. This allows for the selection of areas that truly meet accessibility requirements based on a preset service accessibility threshold. This method overcomes the limitations of traditional approaches that rely solely on straight-line distance or simple buffer zones to define service areas. Instead, it fully considers the actual traffic network and traffic efficiency, making the defined dynamic accessibility range more realistic and accurately reflecting the true convenience for employees to reach supporting service facilities. Consequently, the subsequent identification and evaluation of existing matching facilities will be based on a more reliable spatial accessibility foundation.

[0077] The aforementioned technical solution enables precise definition of the dynamic accessibility range of supporting service facilities. This delineation method, based on grid cells and actual traffic parameters, transforms the definition of the service area from a simple geometric zone into a dynamic zone that fully considers actual traffic conditions and efficiency. This significantly improves the accuracy in identifying existing matching facilities, ensuring that the identified facilities are truly within easy reach of employees. This provides a more solid and reliable spatial foundation for subsequent facility assessments and planning recommendations, enhancing the scientific rigor and practicality of the overall planning methodology.

[0078] In some implementations, step A3 includes:

[0079] A301. Based on the industry type information of each enterprise in the industrial park, identify multiple enterprise groups contained in the industrial park;

[0080] A302. For each of the enterprise groups, obtain the working and rest patterns of the enterprise group and the preferred time periods and preference degrees for each activity node in the service activity chain;

[0081] A303. For each of the aforementioned enterprise groups, obtain the number of employees in that enterprise group;

[0082] A304. Based on the number of employees, work-rest patterns, preferred time periods, and preference levels of each enterprise group, the peak usage time of employees in the industrial park for each activity node in the service activity chain is calculated by superimposing these factors.

[0083] Specifically, in step A301, the enterprise group can be understood as a collection of enterprises with similar characteristics in terms of industry type, work nature, and employee composition. For example, enterprises engaged in research and development can be grouped into one group, enterprises engaged in manufacturing into another, or enterprises providing administrative services into a third. Identifying these enterprise groups can be done by analyzing enterprise registration information, business scope, employee composition data, etc.

[0084] In step A302, the work-rest pattern refers to the daily work and rest schedule of the employees in the enterprise group, such as commuting time, lunch time, and off-get off work time. The preferred time period refers to the time period during which the employees in the enterprise group show a high willingness to use specific service activities (such as catering, fitness, medical care, etc.), and the preference degree quantifies this preference degree, for example, through questionnaires, historical data analysis, or behavioral pattern recognition.

[0085] In step A303, the number of employees refers to the total number of employees included in each enterprise group. This data can be obtained directly from the enterprise's human resources department or statistically analyzed through the park management system.

[0086] In step A304, the overlay calculation is a method for integrating demand data from different enterprise groups. Specifically, based on the number of employees in each enterprise group, their active periods determined by their work-rest patterns, and their preferred periods and preferences for various activity nodes, the demand for specific service activity nodes for each enterprise group in different time periods can be calculated. Subsequently, the demand for the same activity node from all enterprise groups in the same time period is summed to obtain the total demand for that activity node in that time period. By analyzing the trend of these total demands over time, the time period with the highest demand can be identified, i.e., the peak usage period.

[0087] This application's solution refines the needs of industrial park employees into different enterprise groups and comprehensively considers the work-rest patterns, preferred time periods, and preference levels of each group, enabling a more accurate capture of demand fluctuations for specific service activities across different time periods. Traditional methods may only make rough estimates based on the overall industry type of the industrial park, ignoring the diversity of enterprises within the park. However, by identifying enterprise groups and obtaining their refined behavioral data, the errors caused by such extensive estimations can be avoided. Specifically, the number of employees provides the basic scale of demand, work-rest patterns define the time range in which employees are likely to be active, and preferred time periods and preference levels further refine the specific intensity of demand for specific services during active periods. By overlaying and calculating these multi-dimensional data, a more realistic employee demand curve can be simulated, thereby accurately identifying the true peak usage periods for each service activity node within a day or week. This effectively avoids deviations in public service facility planning caused by inaccurate demand forecasting.

[0088] By employing the aforementioned technical solution, this application overcomes the limitations of traditional methods in identifying peak usage periods, significantly improving the accuracy and precision of forecasts. Through in-depth analysis of the behavioral patterns of different enterprise groups within the industrial park, it can more accurately reflect employees' actual needs for public service facilities, thereby providing more reliable data support for subsequent facility assessment and planning. This refined demand forecasting helps optimize the layout and scale of public service facilities, ensuring that employee needs are effectively met during peak periods while avoiding resource waste and improving the overall operational efficiency and employee satisfaction of the industrial park.

[0089] Preferably, step A304 may include:

[0090] Based on the work and rest patterns of each of the aforementioned enterprise groups, the time periods to be identified are determined;

[0091] The time period to be identified is divided into multiple time windows;

[0092] For each time window, based on the number of employees of the enterprise group, the preferred time period, and the preference degree, calculate the number of employees required by each enterprise group for each activity node within that time window;

[0093] For each activity node, the number of employees needed by all the enterprise groups within the same time window is summed to obtain the total number of employees needed in that time window.

[0094] For each activity node, the peak usage period corresponding to that activity node is identified based on the total demand of employees.

[0095] Determining the target time period involves identifying the service needs that require special attention based on the work schedules of various businesses within the industrial park, such as their commuting and lunch breaks on weekdays, weekends, and holidays. For example, if most businesses work from 9:00 AM to 6:00 PM, the target time period can be set as this time period and a certain range extending before and after it, to cover employees' commuting and lunch needs.

[0096] Furthermore, the time period to be identified is divided into multiple time windows, such as 15-minute, 30-minute, or 1-hour intervals. This fine-grained division helps to more accurately capture changes in employee demand, thereby more accurately identifying peak usage periods.

[0097] Specifically, for each time window, the number of employees needed for each activity node within that time window is calculated based on the number of employees in the enterprise group, preferred time periods, and preference levels. A preferred time period can be understood as the time during which employees are inclined to use a particular service, while preference level indicates the intensity or frequency of their use of that service (for example, the percentage of employees willing to use a particular activity node during a given time period can be used as the preference level). For instance, an enterprise group might have a higher preference for catering services during lunchtime and a higher preference for coffee services during afternoon tea. The calculation of employee demand can comprehensively consider factors such as the total number of employees, the overlap between the time window and the preferred time period, and the preference level.

[0098] For example, suppose that enterprise group A has N employees and its preference degree for a certain activity node Q during a certain time period T is c. If the entire preference period T falls within a certain time window t1, then the number of employees m required by enterprise group A for activity node Q during that time window t1 can be calculated as: m = N * c. If the preference period T partially falls within a certain time window t1, then the number of employees m required by enterprise group A for activity node Q during that time window t1 can be calculated as: m = N * c * Δt1, where Δt1 is the proportion of the portion of preference period T that falls within time window t1 in the total duration of preference period T.

[0099] Therefore, for each activity node, the number of employees needed by all enterprise groups within the same time window is summed to obtain the total employee demand for that time window. This summation process ensures a comprehensive statistical analysis of the total demand of all employees within the entire industrial park for a specific service activity node within a given time window.

[0100] Finally, for each activity node, the peak usage period corresponding to that activity node is identified based on the total employee demand. The peak usage period can be defined as the time period when the total employee demand reaches a preset threshold or ranks among the top in all time windows. For example, a dynamic threshold can be set, or several time windows with the highest demand can be selected as peak usage periods.

[0101] This application's solution refines the complex process of calculating and aggregating employee demand into a series of actionable sub-steps, achieving accurate identification of peak usage periods for employees in industrial parks. First, by determining the time periods to be identified based on the work-rest patterns of different business groups, the analysis is focused on the time periods when employees are likely to have service needs, avoiding ineffective calculations. Second, dividing the time periods to be identified into multiple time windows allows for a higher granularity in the analysis of employee demand, thereby capturing more nuanced demand fluctuations. Third, by comprehensively considering the number of employees, preferred time periods, and preference levels to calculate the employee demand for each business group, a more comprehensive reflection of the actual demand intensity for specific services from different business groups at different times is achieved. Subsequently, the demands of all business groups are summed to ensure an accurate assessment of the total demand across the entire industrial park. Finally, peak usage periods are identified based on the summed total employee demand, ensuring that the identified peak periods accurately reflect the actual pressure points on public service facilities within the industrial park.

[0102] The aforementioned technical solution overcomes the potential for coarseness or inaccuracy in identifying peak usage periods for employees in industrial parks using traditional methods. This solution, by finely dividing time windows, quantifying the preferences of different enterprise groups, and overlaying the needs of each group, makes the identified peak usage periods more precise, thus providing a more reliable data foundation for subsequent facility assessments and planning recommendations. This helps to allocate public service resources more effectively, avoid resource waste during off-peak hours, and provide sufficient service capacity during peak hours, significantly improving the utilization efficiency of public service facilities in industrial parks and employee satisfaction.

[0103] In some implementations, step A4 includes performing the following for each active node in the service activity chain:

[0104] A401. Obtain the actual passage data of the existing matching facilities from the industrial park to this activity node;

[0105] A402. Compare the actual passage data with the service accessibility threshold to assess the actual accessibility of the existing matching facilities;

[0106] A403. Obtain the total nominal service capacity of the existing matching facilities of this activity node;

[0107] A404. Obtain the total demand of employees and the total demand of citizens during the peak usage period; the total demand of employees is the total demand of employees in the industrial park for services at this event node, and the total demand of citizens is the total demand of citizens outside the industrial park for services at the existing matching facilities of this event node.

[0108] A405. The total demand of employees and the total demand of citizens during the peak usage period are added together to obtain the total demand of existing matching facilities for this activity node during the peak usage period;

[0109] A406. Compare the total nominal service capacity with the aggregated demand to assess the capacity of the existing matching facilities during the peak usage period.

[0110] Specifically, in step A401, the actual travel data can be understood as data reflecting the real traffic conditions of industrial park employees traveling to existing matching facilities corresponding to specific activity nodes. For example, this data may include real-time traffic information, historical average travel time, public transportation routes' frequency and duration, and the actual distance and time of walking or cycling routes. This data can be obtained in various ways, such as through traffic sensors, map service APIs, user location data, or historical travel records. Its purpose is to provide an objective and dynamic standard for measuring travel costs, rather than solely based on straight-line distance or ideal travel time.

[0111] In step A402, the actual travel data is compared with a preset service accessibility threshold. This threshold can be a time threshold or a distance threshold, used to define the acceptable range of service facilities. By comparing the actual travel time or distance with this threshold, the actual accessibility of existing matching facilities can be quantitatively assessed. For example, if the actual travel time exceeds the time threshold, the facility may be determined to have poor accessibility or be unreachable. Furthermore, the degree to which the actual travel time exceeds the time threshold can be calculated to quantify the degree of unreachability.

[0112] In step A403, the total nominal service capacity refers to the sum of the maximum service capabilities that all existing matching facilities can provide in terms of design or operation. For example, for catering facilities, the total nominal service capacity may be the sum of the maximum number of diners at each catering facility. This capacity is the basis for assessing the facility's carrying capacity.

[0113] In step A404, the total employee demand during peak usage periods refers to the total demand for services at a specific activity node from employees within the industrial park during a particular peak period. Specifically, this can be the total employee demand during peak usage periods calculated in step A304 above. The total demand from citizens refers to the total demand for services at the corresponding existing facilities for that activity node from citizens outside the industrial park during the same peak period. Distinguishing between these two types of demand is crucial because the needs of industrial park employees have unique work-rest patterns and preferences, while the needs of external citizens may be influenced by other factors.

[0114] In step A405, the total demand of employees and the total demand of citizens are summed to obtain the aggregated demand of existing matching facilities corresponding to the activity node during peak usage periods. This summation operation ensures a comprehensive consideration of the total load on facilities and avoids evaluation bias caused by considering only a single user group.

[0115] In step A406, the total nominal service capacity is compared with the aggregated demand to assess the carrying capacity of existing matching facilities during peak usage periods. This comparison determines whether the facilities can meet the total demand during peak periods. For example, if the aggregated demand exceeds the total nominal service capacity, it indicates that the facility is underutilized during peak periods. Furthermore, the degree to which the aggregated demand exceeds the total nominal service capacity can be calculated to quantify the extent of the underutilization.

[0116] This application's solution assesses the actual accessibility of facilities by acquiring and utilizing real-world traffic data, thus overcoming the idealized assumptions that may exist in traditional methods and making the accessibility assessment results closer to reality. Simultaneously, by meticulously distinguishing and aggregating the demand from industrial park employees and external citizens, and comparing this with the facility's total nominal service capacity, it can comprehensively and accurately reflect the actual carrying capacity pressure of the facility during peak hours. It is precisely because of this more refined and comprehensive quantitative assessment of accessibility and carrying capacity that subsequent judgments on service activity chain disruptions and the generation of planning recommendations become more scientific and instructive.

[0117] The aforementioned technical solutions significantly improve the accuracy and reliability of auxiliary planning for public service facilities in industrial parks. Specifically, by considering actual traffic data, facilities that are geographically close but actually inconvenient to access can be identified more accurately, avoiding overestimation of their service capacity. Furthermore, by comprehensively considering the combined demands of employees and citizens during peak hours, the load on facilities can be more accurately reflected, effectively preventing overloading or decreased service quality due to insufficient demand estimation. Therefore, this application provides stronger data support and decision-making basis for the planning of public service facilities in industrial parks, ensuring that the planning scheme can more effectively meet the actual service needs both inside and outside the park.

[0118] In some embodiments of this application, when assessing the carrying capacity of existing matching facilities during peak usage periods, it is necessary to obtain the total demand of citizens during those peak usage periods. However, directly and accurately obtaining the demand of citizens outside industrial parks for specific public service facilities is somewhat difficult, which may lead to deviations in the assessment results and thus affect the accuracy of subsequent carrying capacity assessments.

[0119] Therefore, in some preferred embodiments, step A404, the step of obtaining the total demand of citizens during the peak usage period, includes:

[0120] For each existing matched facility of this activity node, a reference neighbor facility located outside the corresponding dynamic reachability range of the existing matched facility is determined based on spatial distance;

[0121] Obtain the actual load of each of the aforementioned reference neighboring facilities during the peak usage period;

[0122] For each of the existing matched facilities in this activity node, the single-point citizen demand of the existing matched facility during the peak usage period is assessed based on the actual load of the corresponding reference neighboring facility during the peak usage period.

[0123] The total demand of citizens at each of the existing matching facilities for this activity node is calculated to obtain the total demand of citizens during the peak usage period.

[0124] The total demand from citizens refers to the total demand from citizens outside the industrial park for services provided by the existing matching facilities at this activity node. To obtain this demand more accurately, this application proposes an assessment method based on reference to nearby facilities.

[0125] First, for each existing matched facility in this activity node, based on its spatial distance relationship with surrounding facilities, some "reference neighboring facilities" located outside its corresponding dynamic reachability range but geographically close are identified. These reference neighboring facilities typically provide similar service types to the existing matched facilities, and their service targets preferably have some overlap or substitution relationship with the existing matched facilities. For example, existing supporting service facilities that can provide similar service types and are located less than a preset distance threshold from an existing matched facility are selected as reference neighboring facilities of that existing matched facility.

[0126] Secondly, the actual load of these identified reference neighboring facilities during the peak usage period is obtained. This actual load can be obtained through various methods such as sensor data, historical operational data, and user traffic statistics, reflecting the actual usage intensity of these reference facilities during a specific period.

[0127] Furthermore, for each existing matched facility in this activity node, the "single-point citizen demand" of that existing matched facility during the peak usage period is assessed based on the actual load of its corresponding reference neighboring facilities during the peak usage period. This assessment process can be based on a preset algorithm model, for example, calculating the average load of the reference neighboring facilities as the single-point citizen demand, or performing a weighted calculation or proportional extrapolation based on factors such as the relative location and service capacity of the reference neighboring facilities and the target existing matched facility to obtain the single-point citizen demand. Finally, the single-point citizen demand of all existing matched facilities in this activity node is summed to obtain the total citizen demand during the peak usage period.

[0128] This application's solution indirectly and effectively assesses the demand for existing matching facilities from citizens outside industrial parks by introducing the concept of reference neighboring facilities and utilizing their actual load during peak usage periods. Specifically, when directly obtaining citizen demand data for the target existing matching facilities is difficult, the actual load of functionally similar reference neighboring facilities within the surrounding area but outside the dynamic accessibility range can be used as a reference. The load of these reference neighboring facilities reflects the overall service demand level of their respective areas. By analyzing the load of these reference facilities, the potential citizen demand from non-industrial park employees that the target existing matching facilities may face can be inferred, thus overcoming the challenges of directly obtaining citizen demand data.

[0129] The aforementioned technical solutions enable a more accurate assessment of the demand from citizens for existing supporting facilities around industrial parks during peak hours, overcoming the shortcomings of traditional methods in obtaining external citizen demand data. This leads to a more comprehensive and precise evaluation of the carrying capacity of existing supporting facilities, providing a more reliable data foundation for subsequent assessments of service activity chain disruptions and the generation of early warning information and planning recommendations. This significantly enhances the scientific rigor and practicality of auxiliary planning for public service facilities in industrial parks.

[0130] In some implementations, step A5 includes:

[0131] A501. Obtain the quantitative values ​​of the actual reachability assessment results and carrying capacity assessment results for each activity node;

[0132] A502. Based on the quantitative values ​​of the actual reachability assessment results and carrying capacity assessment results of each activity node, determine the interruption status and degree of interruption of the service activity chain;

[0133] A503. Generate the early warning information and the planning suggestions based on the interruption status and the interruption degree.

[0134] Specifically, in step A501, the actual reachability assessment results and capacity assessment results of each active node can be quantified into numerical values. For example, actual reachability can be quantified as a reachability score (which can be obtained by dividing the difference between the service reachability threshold in step A402 and the actual traffic data by the service reachability threshold, and the result is used as the reachability score, or by converting the result using a conversion function; in the case of directly using the result as the reachability score, the reachability score is negative when the actual traffic data exceeds the service reachability threshold), and capacity can be quantified as a capacity rate or saturation (for example, the deviation between the total nominal service capacity and the aggregated demand in step A406 can be divided by the total nominal service capacity, and the result is used as the saturation, or by converting the result using a conversion function; in the case of directly using the result as the saturation, the saturation is negative when the aggregated demand exceeds the total nominal service capacity). These quantified values ​​provide an accurate measurement of the service capacity of each active node in the service activity chain, providing a data basis for subsequent judgments.

[0135] In step A502, the interruption status of the service activity chain refers to whether the service activity chain can smoothly provide the target service to the employees of the industrial park. The interruption status can include only two states: "interrupted" and "uninterrupted." For the "interrupted" situation, it can be further subdivided into multiple states such as "minor interruption," "moderate interruption," and "severe interruption." The interruption degree is a quantitative description of the interruption status. For example, an interruption index can be calculated based on the quantitative values ​​of the actual accessibility assessment results and the carrying capacity assessment results through preset rules or models. For example, when the actual accessibility of an activity node is lower than a preset accessibility threshold, or its carrying capacity is lower than a preset carrying capacity threshold, the activity node is determined to have an interruption risk. When one or more activity nodes in the service activity chain are determined to have an interruption risk, the entire service activity chain is considered interrupted; when all activity nodes in the service activity chain do not have an interruption risk, the entire service activity chain is considered uninterrupted. When the service activity chain is uninterrupted, its interruption level can be set to 0. When the service activity chain is interrupted, for each activity node with interruption risk, an interruption index can be calculated using preset rules or models (e.g., p = a*max[k0-k1,0] + b*max[C0-C1,0], where p is the interruption index, a and b are preset weight coefficients, k0 is a preset accessibility threshold, k1 is the quantified value of the actual accessibility assessment result, C0 is a preset carrying capacity threshold, and C1 is the quantified value of the carrying capacity assessment result). Then, the largest interruption index is selected as the interruption level of the entire service activity chain. Furthermore, when the service activity chain is interrupted, the interruption status of the service activity chain can be further determined as a subdivided status corresponding to the preset range (e.g., "minor interruption", "moderate interruption", "severe interruption").

[0136] In step A503, the early warning information may include the specific reasons for the service activity chain interruption, the involved activity nodes, the degree of interruption, and the possible scope of impact. The planning recommendations are specific improvement measures proposed to address the interruption, such as suggesting the addition of new public service facilities, optimization of the layout of existing facilities, adjustment of service hours, and introduction of shared service models. These recommendations aim to solve the specific problems causing the service activity chain interruption, thereby improving the overall efficiency of public services in the industrial park.

[0137] This application's solution quantifies the actual accessibility and carrying capacity assessment results of each activity node, and based on these quantified values, accurately determines the interruption status and degree of the service activity chain, thereby enabling a more detailed and accurate identification of weak links in service supply. Specifically, obtaining quantified values ​​transforms the identification of service bottlenecks from a simple "yes / no" judgment to a "degree" judgment; for example, it can distinguish between minor service deficiencies and severe service interruptions. Therefore, differentiated early warning information and more targeted planning suggestions can be generated based on the degree of interruption, avoiding vague judgments and suggestions, and making subsequent planning adjustments more instructive and operational.

[0138] The aforementioned technical solutions enable refined management of auxiliary planning for public service facilities in industrial parks. Specifically, by acquiring quantitative values ​​and determining the status and extent of disruptions, weak links and potential risks in the service activity chain can be identified more accurately. This results in more instructive early warning information and more targeted planning recommendations. This helps avoid resource waste, improves the utilization efficiency of public service facilities, and ultimately enhances the service experience and satisfaction of employees in the industrial park.

[0139] refer to Figure 2 This application provides an auxiliary planning system for public service facilities in industrial parks, the system comprising:

[0140] Query module 1 is used to query the service activity chain required to complete the target service based on the type of target service needed by the employees of the industrial park; the service activity chain includes an ordered sequence of activity nodes and the corresponding service type required for each activity node (for details, please refer to step A1 above).

[0141] Matching module 2 is used to determine the existing matching facilities located around the industrial park for each activity node in the service activity chain, based on the supporting service type (for details, please refer to step A2 above).

[0142] The time period identification module 3 is used to obtain the peak usage time of each activity node in the service activity chain of the industrial park employees based on the industry type information of the industrial park (for details, please refer to step A3 above).

[0143] Evaluation module 4 is used to evaluate the actual accessibility of the corresponding existing matching facilities and their carrying capacity during peak usage periods for each activity node in the service activity chain (for details, please refer to step A4 above).

[0144] The auxiliary planning module 5 is used to determine whether the service activity chain is interrupted based on the actual reachability and carrying capacity assessment results of each activity node, and to generate early warning information and corresponding planning suggestions when the service activity chain is interrupted (the specific process can be referred to step A5 above).

[0145] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for assisting in the planning of public service facilities in industrial parks, characterized in that, Includes the following steps: A1. Based on the type of target service needed by employees in the industrial park, query the service activity chain required to complete the target service; the service activity chain contains an ordered sequence of activity nodes and the type of supporting service required for each activity node; A2. For each activity node in the service activity chain, determine the existing matching facilities located around the industrial park according to the supporting service type; A3. Based on the industry type information of the industrial park, obtain the peak usage time of each activity node in the service activity chain for employees of the industrial park; A4. For each activity node in the service activity chain, assess the actual accessibility of the corresponding existing matching facilities and their carrying capacity during peak usage periods; A5. Based on the assessment results of the actual reachability and carrying capacity of each activity node, determine whether the service activity chain is interrupted, and generate early warning information and corresponding planning suggestions when the service activity chain is interrupted; Step A2 includes the steps performed for each activity node in the service activity chain: A201. Based on the types of supporting services required for this activity node, obtain the service accessibility threshold corresponding to the types of supporting services; the service accessibility threshold is a time threshold or a distance threshold; A202. Based on the service accessibility threshold, define the dynamic accessibility range of supporting service facilities; A203. Within the dynamically accessible range, identify existing matching facilities located around the industrial park; Step A201 includes: Based on the types of supporting services required for this activity node, obtain the preset baseline service accessibility threshold for the supporting service type; the baseline service accessibility threshold is a time threshold or a distance threshold. Obtain the average traffic congestion index and the average percentage of severe weather days in the area where the industrial park is located; the average percentage of severe weather days is the average proportion of days with a preset type of severe weather in the total number of days in the year. Based on the average traffic congestion index and the average proportion of severe weather days, calculate the traffic impact correction coefficient and the weather impact correction coefficient; The baseline service accessibility threshold is corrected using the traffic impact correction coefficient and the weather impact correction coefficient to obtain the final service accessibility threshold corresponding to the supporting service type. Step A3 includes: A301. Based on the industry type information of each enterprise in the industrial park, identify multiple enterprise groups contained in the industrial park; A302. For each of the enterprise groups, obtain the working and rest patterns of the enterprise group and the preferred time periods and preference degrees for each activity node in the service activity chain; A303. For each of the aforementioned enterprise groups, obtain the number of employees in that enterprise group; A304. Based on the number of employees, work-rest patterns, preferred time periods, and preference levels of each enterprise group, the peak usage time of employees in the industrial park for each activity node in the service activity chain is calculated by superimposing these factors.

2. The method for auxiliary planning of public service facilities in industrial parks according to claim 1, characterized in that, Step A202 includes: The area surrounding the industrial park is divided into multiple grid units; Calculate the actual travel time or actual travel distance from the center point of each grid cell to the industrial park, and use it as the actual travel parameter; The set of grid cells whose actual access parameters do not exceed the service accessibility threshold is determined as the dynamic accessibility range of the supporting service facilities.

3. The method for auxiliary planning of public service facilities in industrial parks according to claim 1, characterized in that, Step A304 includes: Based on the work and rest patterns of each of the aforementioned enterprise groups, the time periods to be identified are determined; The time period to be identified is divided into multiple time windows; For each time window, based on the number of employees of the enterprise group, the preferred time period, and the preference degree, calculate the number of employees required by each enterprise group for each activity node within that time window; For each activity node, the employee demand of all the enterprise groups within the same time window is summed to obtain the total employee demand for that time window; For each activity node, the peak usage period corresponding to that activity node is identified based on the total demand of employees.

4. The method for auxiliary planning of public service facilities in industrial parks according to claim 1, characterized in that, Step A4 includes performing the following for each activity node in the service activity chain: A401. Obtain the actual passage data of the existing matching facilities from the industrial park to this activity node; A402. Compare the actual passage data with the service accessibility threshold to assess the actual accessibility of the existing matching facilities; A403. Obtain the total nominal service capacity of the existing matching facilities of this activity node; A404. Obtain the total demand of employees and the total demand of citizens during the peak usage period; the total demand of employees is the total demand of employees in the industrial park for services at this event node, and the total demand of citizens is the total demand of citizens outside the industrial park for services at the existing matching facilities of this event node. A405. The total demand of employees and the total demand of citizens during the peak usage period are added together to obtain the total demand of existing matching facilities for this activity node during the peak usage period; A406. Compare the total nominal service capacity with the aggregated demand to assess the capacity of the existing matching facilities during the peak usage period.

5. The method for auxiliary planning of public service facilities in industrial parks according to claim 4, characterized in that, Step A404, the step of obtaining the total demand of citizens during the peak usage period, includes: For each existing matched facility of this activity node, a reference neighbor facility located outside the corresponding dynamic reachability range of the existing matched facility is determined based on spatial distance; Obtain the actual load of each of the aforementioned reference neighboring facilities during the peak usage period; For each of the existing matched facilities in this activity node, the single-point citizen demand of the existing matched facility during the peak usage period is assessed based on the actual load of the corresponding reference neighboring facility during the peak usage period. The total demand of citizens at each of the existing matching facilities for this activity node is calculated to obtain the total demand of citizens during the peak usage period.

6. The method for auxiliary planning of public service facilities in industrial parks according to claim 1, characterized in that, Step A5 includes: A501. Obtain the quantitative values ​​of the actual reachability assessment results and carrying capacity assessment results for each activity node; A502. Based on the quantitative values ​​of the actual reachability assessment results and carrying capacity assessment results of each activity node, determine the interruption status and degree of interruption of the service activity chain; A503. Generate the early warning information and the planning suggestions based on the interruption status and the interruption degree.

7. A public service facility auxiliary planning system for industrial parks, characterized in that, The system includes: The query module is used to retrieve the service activity chain required to complete the target service based on the type of target service needed by the employees of the industrial park; the service activity chain includes an ordered sequence of activity nodes and the type of supporting service required for each activity node. The matching module is used to determine the existing matching facilities located around the industrial park for each activity node in the service activity chain, based on the type of supporting service. The time period identification module is used to obtain the peak usage time of each activity node in the service activity chain by employees of the industrial park based on the industry type information of the industrial park; The evaluation module is used to evaluate the actual accessibility of the corresponding existing matching facilities and their carrying capacity during peak usage periods for each activity node in the service activity chain. The auxiliary planning module is used to determine whether the service activity chain is interrupted based on the assessment results of the actual reachability and carrying capacity of each activity node, and to generate early warning information and corresponding planning suggestions when the service activity chain is interrupted. When the matching module determines the existing matching facilities located around the industrial park for each activity node in the service activity chain based on the supporting service type, it performs the following steps for each activity node in the service activity chain: A201. Based on the types of supporting services required for this activity node, obtain the service accessibility threshold corresponding to the types of supporting services; the service accessibility threshold is a time threshold or a distance threshold; A202. Based on the service accessibility threshold, define the dynamic accessibility range of supporting service facilities; A203. Within the dynamically accessible range, identify existing matching facilities located around the industrial park; Step A201 includes: Based on the types of supporting services required for this activity node, obtain the preset baseline service accessibility threshold for the supporting service type; the baseline service accessibility threshold is a time threshold or a distance threshold. Obtain the average traffic congestion index and the average percentage of severe weather days in the area where the industrial park is located; the average percentage of severe weather days is the average proportion of days with a preset type of severe weather in the total number of days in the year. Based on the average traffic congestion index and the average proportion of severe weather days, calculate the traffic impact correction coefficient and the weather impact correction coefficient; The baseline service accessibility threshold is corrected using the traffic impact correction coefficient and the weather impact correction coefficient to obtain the final service accessibility threshold corresponding to the supporting service type. When the time period identification module obtains the peak usage time of each activity node in the service activity chain by employees of the industrial park based on the industry type information of the industrial park, it performs the following steps: A301. Based on the industry type information of each enterprise in the industrial park, identify multiple enterprise groups contained in the industrial park; A302. For each of the enterprise groups, obtain the working and rest patterns of the enterprise group and the preferred time periods and preference degrees for each activity node in the service activity chain; A303. For each of the aforementioned enterprise groups, obtain the number of employees in that enterprise group; A304. Based on the number of employees, work-rest patterns, preferred time periods, and preference levels of each enterprise group, the peak usage time of employees in the industrial park for each activity node in the service activity chain is calculated by superimposing these factors.

Images