An intelligent investment matching system and method based on a whole life cycle of land

Abstract

This application belongs to the field of land resource management technology, and discloses an intelligent investment promotion matching system and method based on the entire life cycle of land. The method includes: obtaining actual resource occupancy data of public resources provided by shared infrastructure to resident land parcels within a target area; calculating the resource scarcity value of each public resource based on the actual resource occupancy data and the effective design capacity of the shared infrastructure; correcting the initial resource planning parameters of undeveloped land parcels within the target area using the resource scarcity value to obtain actual resource availability parameters of each public resource; obtaining resource demand information of enterprises to be matched, and evaluating the matching degree between the enterprises to be matched and the undeveloped land parcels in combination with the actual resource availability parameters, so as to output investment promotion matching results; thereby improving the accuracy and effectiveness of investment promotion matching, and providing a more scientific and real-time decision-making basis for park investment promotion and land development.

Description

Technical Field

[0001] This application relates to the field of land resource management technology, and more specifically, to an intelligent investment attraction matching system and method based on the entire life cycle of land. Background Technology

[0002] Existing intelligent investment matching systems typically rely on pre-defined, unchanging infrastructure planning information when identifying suitable investment projects for land resources. However, in actual industrial parks or development zones, many plots often share a regional set of shared infrastructure, such as water supply networks and substations. When resident companies consume more resources than initially declared or committed during their daily operations, this additional consumption effectively consumes a portion of the available shared infrastructure across the entire area.

[0003] Existing systems can monitor and record the excessive consumption behavior of individual enterprises, but their data models and matching logic are flawed. They fail to quantify and analyze the actual impact of changes in the operational status of a single plot on shared infrastructure and to adjust this impact in a timely manner to the infrastructure availability parameters of adjacent undeveloped plots. This leads the system to still use outdated resource information based on planning theories when matching new undeveloped plots with potential investors, potentially recommending an enterprise whose resource needs cannot be met by the current actual surplus of infrastructure, resulting in flawed investment decisions.

[0004] Such decision-making errors can lead to difficulties in the implementation of subsequent projects or the need for costly infrastructure remediation. For example, a newly established enterprise may be unable to operate normally due to insufficient supply of public resources such as water and electricity, causing project delays or even stagnation. This not only results in economic losses for the enterprise but also affects the overall image and development of the park. Therefore, establishing a real-time correlation and update mechanism that allows the system to automatically recalculate and adjust the actual available capacity of shared infrastructure in the area based on the real-time operational data of enterprises on a plot of land, and using this updated data as a basis for matching investment opportunities with neighboring plots, is a current technical challenge.

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

[0006] This invention discloses an intelligent investment attraction matching system and method based on the entire life cycle of land, which can improve the accuracy and effectiveness of investment attraction matching and provide a more scientific and real-time decision-making basis for park investment attraction and land development.

[0007] In a first aspect, the present invention provides an intelligent investment attraction matching method based on the entire life cycle of land, used to match undeveloped land parcels in a target area containing shared infrastructure for investment attraction. The steps of the method include: A1. Obtain data on the actual resource occupancy of shared infrastructure provided by the land parcels already occupied within the target area; A2. Based on the actual resource occupancy data and the effective design capacity of the shared infrastructure, calculate the resource scarcity value of each public resource; A3. The initial resource planning parameters of the undeveloped land parcels in the target area are corrected using the resource scarcity value to obtain the actual resource availability parameters of each public resource; A4. Obtain the resource demand information of the enterprises to be matched, and combine it with the actual resource availability parameters to evaluate the matching degree between the enterprises to be matched and the land to be developed, so as to output the investment promotion matching results.

[0008] Secondly, this application provides an intelligent investment matching system based on the entire life cycle of land, used for investment matching of undeveloped land parcels in target areas containing shared infrastructure. The system includes: The data acquisition module is used to acquire data on the actual resource occupancy of the shared infrastructure provided by the land parcels already occupied within the target area; The stress calculation module is used to calculate the stress level of each public resource based on the actual resource occupancy data and the effective design capacity of the shared infrastructure. The parameter correction module is used to correct the initial resource planning parameters of the undeveloped land parcels in the target area using the resource scarcity value, so as to obtain the actual resource availability parameters of various public resources. The matching module is used to obtain the resource demand information of the enterprises to be matched, and combine it with the actual resource availability parameters to evaluate the matching degree between the enterprises to be matched and the land to be developed, so as to output the investment promotion matching results.

[0009] Beneficial Effects: This application provides an intelligent investment attraction matching system and method based on the entire land lifecycle. It acquires real-time data on the actual resource occupancy of shared infrastructure provided by already occupied land parcels within a target area. Based on this data and the effective design capacity of the shared infrastructure, it calculates the resource scarcity value of each public resource. Subsequently, it uses these resource scarcity values ​​to correct the initial resource planning parameters of undeveloped land parcels within the target area, thereby obtaining the actual resource availability parameters of each public resource. Finally, combining the resource demand information of the enterprises to be matched with the actual resource availability parameters, it assesses the matching degree between enterprises and land parcels and outputs the investment attraction matching results. This method effectively solves the problem that existing intelligent investment attraction matching systems fail to quantify and analyze the actual resource occupancy of already occupied land parcels and adjust it in a timely manner to the infrastructure availability parameters of adjacent undeveloped land parcels. By dynamically updating resource availability parameters, this application avoids investment attraction decision-making errors caused by using outdated resource information based on planning theory values, thereby effectively avoiding subsequent project implementation difficulties or the need for expensive infrastructure remediation. It improves the accuracy and effectiveness of investment attraction matching, providing a more scientific and real-time decision-making basis for park investment attraction and land development. Attached Figure Description

[0010] Figure 1 A flowchart illustrating an intelligent investment attraction matching method based on the entire life cycle of land, as provided in this application.

[0011] Figure 2 This application provides a structural diagram of an intelligent investment attraction matching system based on the entire life cycle of land.

[0012] Labeling Explanation: 1. Data Acquisition Module; 2. Tension Calculation Module; 3. Parameter Correction Module; 4. Matching Module. Detailed Implementation

[0013] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. 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.

[0014] 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.

[0015] Reference Figure 1 This invention proposes an intelligent investment attraction matching method based on the entire life cycle of land, used to match undeveloped land parcels in target areas containing shared infrastructure for investment attraction. The method includes the following steps: A1. Obtain data on the actual resource occupancy of shared infrastructure provided by the land parcels already occupied within the target area; A2. Based on the actual resource occupancy data and the effective design capacity of the shared infrastructure, calculate the resource scarcity value of each public resource; A3. The initial resource planning parameters of the undeveloped land parcels in the target area are corrected using the resource scarcity value to obtain the actual resource availability parameters of each public resource; A4. Obtain the resource demand information of the enterprises to be matched, and combine it with the actual resource availability parameters to evaluate the matching degree between the enterprises to be matched and the land to be developed, so as to output the investment promotion matching results.

[0016] This application dynamically calculates the resource scarcity level of public resources by acquiring real-time data on the actual resource occupancy of occupied land parcels and combining this with the effective design capacity of shared infrastructure. Subsequently, this scarcity level is used to correct the initial resource planning parameters of the land parcels to be developed, yielding actual resource availability parameters. This enables more precise investment attraction matching and effectively avoids investment attraction decision-making errors caused by lagging or inaccurate resource information.

[0017] Shared infrastructure refers to infrastructure shared by multiple plots of land or businesses within a target area, such as water supply systems, power supply systems, sewage treatment systems, and communication networks. This infrastructure has a certain design capacity to provide public resources to all users within the area.

[0018] Public resources refer to resources provided by shared infrastructure that are available for use by businesses within a region, such as water, electricity, and network bandwidth.

[0019] Actual resource usage data refers to the actual consumption or usage of various public resources provided by shared infrastructure during the actual operation of the land parcels. This reflects the real demand for and intensity of use of resources by the companies that have moved in.

[0020] Effective design capacity refers to the maximum amount of public resources that shared infrastructure can stably and reliably provide after considering various internal and external factors (such as risk and redundancy). It differs from initial design capacity and better reflects the actual carrying capacity of the infrastructure.

[0021] The resource scarcity value is an indicator that measures the supply and demand balance of various public resources. The higher the value, the more scarce the resource is and the lower its availability.

[0022] Initial resource planning parameters refer to the expected demand or availability of various public resources for a plot of land before it is put up for investment promotion, based on planning design or theoretical estimation.

[0023] Actual resource availability parameters refer to the actual amount of various public resources that can be obtained from the land to be developed after taking into account the actual operation of shared infrastructure and the degree of resource scarcity.

[0024] Resource demand information refers to the amount of resources that the companies to be matched need for various public resources after they have settled in.

[0025] Matching degree assessment refers to evaluating the degree of matching between the resource demand information of the enterprise to be matched and the actual resource availability parameters of the land to be developed.

[0026] Investment matching results refer to suitable companies recommended to undeveloped land parcels, or suitable land parcels recommended to companies seeking matching, after a matching degree assessment.

[0027] The core of the intelligent investment attraction matching method based on the entire land lifecycle proposed in this application lies in achieving more accurate investment attraction matching by dynamically adjusting resource availability parameters. The main steps of this method will be described in detail below.

[0028] In step A1, it is necessary to obtain the actual resource consumption data of the shared infrastructure provided by the land parcels already established within the target area. There are several ways to obtain this data. For example, sensors and smart metering devices can be deployed at key nodes of the shared infrastructure to monitor the flow and consumption of public resources such as water, electricity, and gas in real time. These devices can then upload the data to a central data platform for storage and processing. Another approach is to interface with the management systems of the enterprises already established within the land parcels to periodically or in real-time obtain their internal resource consumption reports or log data. For instance, for enterprises within an industrial park, data such as electricity and water consumption on their production lines can be recorded through their internal energy management systems and transmitted to the investment matching system via an interface.

[0029] In step A2, based on actual resource occupancy data and the effective design capacity of shared infrastructure, the resource scarcity level of each public resource is calculated. The purpose of this step is to quantify the current supply and demand situation of public resources. For example, the actual total occupancy of each public resource can be simply compared with its effective design capacity to calculate the occupancy ratio. The higher the occupancy ratio, the higher the resource scarcity level. Specifically, a threshold can be set; when the occupancy ratio exceeds this threshold, the resource is considered to be in a state of scarcity.

[0030] In step A3, the initial resource planning parameters of the undeveloped plots within the target area are adjusted using the resource scarcity value to obtain the actual resource availability parameters for each public resource. This step is one of the key innovations of this application. For example, if the resource scarcity value of a public resource (such as electricity) is high, indicating a tight supply, the initial electricity planning parameters for the undeveloped plots need to be adjusted downwards to reflect the actual available electricity resources. Conversely, if the resource scarcity value of a public resource (such as water) is low, indicating a sufficient supply, the initial water resource planning parameters for the undeveloped plots can remain unchanged or be slightly adjusted upwards. This adjustment mechanism ensures that the resource availability parameters of the undeveloped plots can reflect the actual carrying capacity of the shared infrastructure in real time.

[0031] In step A4, the system obtains the resource requirements information of the companies to be matched and, in conjunction with actual resource availability parameters, assesses the matching degree between the companies and the land parcels to be developed, thus outputting the investment attraction matching results. For example, when submitting their application to move in, companies will provide their expected demand for various public resources such as water, electricity, and gas. The system compares these demands with the actual resource availability parameters of the land parcels to be developed. If the company's resource requirements are less than or equal to the actual resource availability parameters of the land parcels, the company and the land parcels are considered to be matched in terms of resources. If the company's resource requirements are greater than the actual resource availability parameters of the land parcels, the company and the land parcels are considered to be mismatched in terms of resources, or additional resource expansion is required to meet the demands. In this way, the system can filter out combinations of companies and land parcels with high resource matching degrees, thereby outputting the investment attraction matching results.

[0032] The intelligent investment attraction matching method based on the entire land lifecycle proposed in this application effectively solves the problems of lagging and inaccurate resource information in traditional investment attraction matching methods by introducing real-time monitoring and dynamic evaluation of the actual resource occupancy data of shared infrastructure. The core innovation of this method lies in its ability to dynamically calculate the resource scarcity value of various public resources based on the actual occupancy of public resources by already occupied plots, and based on this, to adjust the initial resource planning parameters of plots to be developed in real time, thereby obtaining resource availability parameters that are more in line with the actual situation.

[0033] Specifically, this method first obtains actual resource occupancy data for the occupied land parcels, providing a real-time and accurate basis for subsequent resource scarcity calculations. This data reflects the true load situation of the shared infrastructure. Subsequently, combined with the effective design capacity of the shared infrastructure, the resource scarcity value of each public resource is calculated. This scarcity value not only considers the current resource occupancy but may also comprehensively consider the changing trends in resource consumption and external risk factors, thereby providing a more comprehensive assessment of the resource supply and demand situation.

[0034] After obtaining the resource scarcity level, this method applies it to adjust the initial resource planning parameters of the land parcels to be developed. This means that if the supply of a certain public resource is currently tight, the available amount of that resource on the land parcels to be developed will be reduced accordingly, thus avoiding the failure to deliver on resource promises to new entrants. Conversely, if the resource supply is sufficient, the available amount may remain unchanged or be slightly increased. This dynamic adjustment mechanism ensures the accuracy and feasibility of investment matching results.

[0035] Finally, the system evaluates the match between the resource requirements of the companies to be matched and the revised actual resource availability parameters. In this way, the system can filter out companies that are truly suitable for the current resource environment, avoiding problems such as project implementation difficulties and production stagnation caused by insufficient resources.

[0036] Compared to the closest existing technologies, the advantages of this application lie in its dynamism and real-time nature. Existing technologies typically rely on static, pre-set planning data for investment matching, failing to reflect the actual operational status of shared infrastructure and changes in resource carrying capacity in a timely manner. When resident enterprises over-utilize resources, existing systems cannot transmit this impact to the investment matching decisions for undeveloped plots, leading to discrepancies between the matching results and the actual situation. This application, by introducing real-time acquisition of actual resource occupancy data, dynamic calculation of resource scarcity values, and a correction mechanism for resource availability parameters, enables the investment matching process to respond in real-time to actual changes in shared infrastructure, thereby significantly improving the accuracy and effectiveness of investment matching. This method not only avoids investment decision-making errors and reduces project implementation risks but also optimizes the allocation efficiency of land resources, promoting the healthy and sustainable development of the regional economy.

[0037] In some implementations, step A1 includes: A101. Obtain resource consumption data of the various public resources provided by the shared infrastructure for the land parcels that have been occupied within a preset period; A102. Clean and preprocess the resource consumption data to obtain processed resource consumption data; A103. Calculate the actual resource usage data based on the processed resource consumption data.

[0038] First, resource consumption data for various public resources provided by the shared infrastructure to the occupied land parcels within a preset period is obtained. The preset period can be set according to actual needs, such as daily, weekly, monthly, or yearly, with the aim of collecting sufficient time-series data to reflect the dynamic consumption of resources. The shared infrastructure may include, but is not limited to, power systems, water supply systems, sewage treatment systems, and communication networks. The public resources refer to various resources provided by these shared infrastructures for the use of the occupied land parcels, such as electricity, water, and network bandwidth. Resource consumption data can be collected through various methods, including sensors, smart meters, system logs, or manual recording.

[0039] Secondly, the resource consumption data is cleaned and preprocessed to obtain processed resource consumption data. The cleaning and preprocessing aim to eliminate noise, outliers, missing values, or duplicate data that may exist during data collection, ensuring data accuracy and consistency. For example, statistical methods (such as mean imputation, median imputation), machine learning methods (such as anomaly detection algorithms), or domain knowledge rules can be used for data cleaning. Preprocessing may also include operations such as data format standardization, unit conversion, and data aggregation to facilitate subsequent calculations.

[0040] Finally, based on the processed resource consumption data, the actual resource occupancy data is calculated. This actual resource occupancy data reflects the actual use of various public resources by the occupied land parcels within a specific time period. The calculation method can be to accumulate, average, or perform other statistical analyses on the processed resource consumption data to obtain a quantitative indicator that represents the actual occupancy. For example, for electricity resources, the actual resource occupancy data could be the average load or peak load within a certain time period; for water resources, it could be the total water consumption; and for network bandwidth, it could be the average bandwidth utilization rate.

[0041] This application's solution refines the process of obtaining actual resource occupancy data into three stages: data acquisition, cleaning and preprocessing, and calculation. This ensures the accuracy and reliability of the acquired data. By acquiring raw resource consumption data within a preset period, the dynamic demand for public resources from occupied land parcels can be comprehensively reflected. Subsequently, this raw data undergoes cleaning and preprocessing, effectively eliminating interference factors and errors, thereby avoiding deviations in subsequent resource scarcity assessments and investment matching results due to data quality issues. Finally, actual resource occupancy data is calculated based on the high-quality processed data, providing a solid foundation for subsequent resource scarcity value calculations, making the entire investment matching process more scientific and accurate.

[0042] In some implementations, step A2 includes: A201. Obtain the effective design capacity of each of the aforementioned public resources; A202. For each of the aforementioned public resources, obtain the actual total occupancy of the public resource based on the actual resource occupancy data of each of the aforementioned occupied land parcels; A203. Calculate the rate of change of the actual total occupancy based on the actual total occupancy within the preset time window; A204. Calculate the resource scarcity value for each public resource based on the effective design capacity, the actual total occupancy, and the rate of change of the actual total occupancy.

[0043] Specifically, the effective design capacity obtained in step A201 refers to the maximum amount of public resources that the shared infrastructure can continuously and stably provide after considering various internal and external factors. The effective design capacity can be understood as the upper limit of the actual capacity that the shared infrastructure can bear under actual operating conditions, aiming to provide a more realistic and dynamic benchmark for calculating resource stress.

[0044] In step A202, the actual total occupancy refers to the total consumption of a certain public resource by all occupied plots within a specific point in time or time period. Its purpose is to reflect the actual usage load of the resource. The actual total occupancy can be calculated by summarizing the resource consumption data of various public resources by the occupied plots within a preset period.

[0045] In step A203, the actual total occupancy change rate refers to the rate or trend of change in the actual total occupancy of public resources over time within a preset time window. Its purpose is to capture dynamic changes in resource demand, such as trends of increase or decrease. The actual total occupancy within the preset time window can be fitted with a straight line, and the slope of the fitted line can be extracted as the actual total occupancy change rate. Alternatively, the difference between the actual total occupancy at the end of the preset time window and the actual total occupancy at the beginning of the window can be calculated, and the difference can be divided by the duration of the preset time window to obtain the actual total occupancy change rate. In practical applications, the preset time window can be set according to actual needs and data granularity, such as the past day, week, or month.

[0046] In step A204, the resource stress value is a comprehensive indicator that not only considers the current resource occupancy but also incorporates the changing trends of resource carrying capacity and resource demand. Its purpose is to provide a comprehensive and forward-looking assessment of resource pressure.

[0047] This application's solution incorporates the actual total occupancy change rate, ensuring that the calculation of resource stress not only considers the current actual resource occupancy and the effective design capacity of shared infrastructure, but also incorporates the dynamic trend of resource occupancy changes. Specifically, the effective design capacity obtained in step A201 represents the upper limit of resource carrying capacity; the actual total occupancy obtained in step A202 reflects the current actual resource consumption; and the actual total occupancy change rate calculated in step A203 reveals the growth or decline trend of resource consumption. Therefore, by comprehensively considering these three key indicators, future resource pressure can be predicted more accurately, avoiding misjudgments caused by relying solely on static data.

[0048] In some of the embodiments described above in this application, the effective design capacity of each public resource needs to be obtained when calculating the resource scarcity value of each public resource. However, if the calculation is based solely on the static or preset effective design capacity, it may not fully reflect the impact of external environmental risks on the shared infrastructure during actual operation and the dynamic status of internal redundant facilities. This can lead to a deviation between the calculated effective design capacity and the actual carrying capacity, thereby affecting the accuracy of the resource scarcity value assessment.

[0049] In some implementations, step A201 includes steps performed for each of the aforementioned public resources: Obtain the initial design capacity of the public resource; Obtain external environmental risk information that affects the carrying capacity of the shared infrastructure, and obtain the status information of the internal redundant facilities of the shared infrastructure; Based on the aforementioned external environmental risk information, determine the risk adjustment factor for each external environmental risk item; Based on the status information of the internal redundant facilities, a risk adjustment factor for each internal redundant facility is determined; The initial design capacity is adjusted according to the risk adjustment factor to obtain the effective design capacity of the public resource.

[0050] Specifically, the initial design capacity refers to the maximum public resource carrying capacity that the shared infrastructure can provide under ideal conditions, according to its design standards and specifications. For example, it could be the maximum daily processing capacity of a water treatment plant or the maximum power supply load of a power system.

[0051] The external environmental risk information can be understood as external factors that may negatively impact the normal operation and carrying capacity of shared infrastructure, such as natural disasters like floods, earthquakes, and typhoons. Natural disaster information (including intensity levels of various natural disasters) from a preset historical period (e.g., the past 10 years) can be obtained as the external environmental risk information, with the aim of quantifying the potential weakening of infrastructure carrying capacity by external uncertainties.

[0052] The status information of the internal redundancy facilities refers to the operational status of backup or redundant equipment, systems, or capacities set up within the shared infrastructure to cope with emergencies or ensure service continuity. Examples include the availability of backup generator sets, the operational status of backup water pumps, and the health status of network backup lines. This information can be obtained through sensor data, maintenance records, system self-test reports, etc., and its purpose is to assess the inherent resilience of the infrastructure in responding to risks.

[0053] In practical applications, determining the risk adjustment factor for each external environmental risk item based on external environmental risk information refers to quantifying the potential negative impact of external risks on the carrying capacity of infrastructure. Specifically, this risk adjustment factor can be determined through a lookup table. A pre-set environmental risk adjustment factor lookup table can be created, recording the risk adjustment factors for different external environmental risk items at different intensity levels. In practice, the corresponding risk adjustment factor is obtained by looking up the historical average intensity level of the external environmental risk items occurring locally (obtained through statistical analysis of historical data for a pre-set historical period). For example, different risk adjustment factors can be set based on the degree of impact of earthquake magnitude on the carrying capacity of power facilities, such as 0.05 for minor earthquakes, 0.1 for moderate earthquakes, and 0.2 for severe earthquakes.

[0054] Based on the status information of internal redundancy facilities, the risk adjustment factor for each internal redundancy facility is determined. This risk adjustment factor reflects the facility's role in mitigating risks or enhancing its carrying capacity. For example, the failure rate can be used to calculate the risk adjustment factor. Specifically, a baseline failure rate can be set (e.g., based on industry standards, the average level of similar equipment, or an acceptable risk threshold), and its corresponding risk adjustment factor can be set as the baseline risk adjustment factor (e.g., 0.1). When the actual equipment failure rate is higher than this baseline, its risk adjustment factor should be increased accordingly to reflect the higher risk level (e.g., this risk adjustment factor can be calculated using the following formula: a1=a0+r*(k1-k0) / k0, where a1 is the risk adjustment factor, a0 is the baseline risk adjustment factor, k1 is the failure rate, k0 is the baseline failure rate, and r is the scaling factor).

[0055] The effective design capacity of public resources is obtained by adjusting the initial design capacity according to the risk adjustment factor. This means that the initial design capacity is dynamically adjusted by taking into account external environmental risks and the status of internal redundant facilities, so that it is closer to the actual available capacity.

[0056] This application's solution incorporates external environmental risk information and the status information of internal redundant facilities, transforming them into risk adjustment factors to correct the initial design capacity of public resources, thereby obtaining a more accurate and dynamic effective design capacity. Specifically, when external environmental risks are high, the initial design capacity is reduced through the risk adjustment factor to reflect the decline in actual carrying capacity; when internal redundant facilities are in good condition, the initial design capacity is increased through the risk adjustment factor to reflect their additional carrying potential. This correction mechanism allows the effective design capacity to more realistically reflect the actual carrying capacity of shared infrastructure under different external and internal conditions, avoiding the bias caused by relying solely on static design capacity. Therefore, the effective design capacity used in subsequent calculations of resource stress levels will be closer to reality, thus improving the accuracy of resource stress assessment.

[0057] In some implementations, the step of adjusting the initial design capacity according to the risk adjustment factor to obtain the effective design capacity of the public resource includes: Calculate the sum of all the risk adjustment factors and normalize the sum to obtain the elasticity factor; Calculate the difference between 1 and the elasticity factor, and use it as a correction coefficient; The effective design capacity of the public resource is obtained by multiplying the correction factor by the initial design capacity.

[0058] Specifically, the resilience factor can be understood as the combined impact of all risk adjustment factors on the initial design capacity. Each risk adjustment factor reflects the depletion of the shared infrastructure's carrying capacity due to a specific external environmental risk or the state of internal redundant facilities. By summing these risk adjustment factors and then normalizing the sum (e.g., Min-Max normalization or Z-score normalization), a comprehensive resilience factor between 0 and 1 can be obtained, which comprehensively reflects the potential impact of all known factors on the design capacity.

[0059] The correction factor is obtained by subtracting the elasticity factor from 1. This correction factor aims to transform the combined effect represented by the elasticity factor into a multiplicative factor for adjusting the initial design capacity.

[0060] In practical applications, the effective design capacity of public resources is obtained by multiplying the correction factor by the initial design capacity. This multiplication operation provides a direct and quantitative way to adjust the initial design capacity, enabling it to dynamically adapt to changes in the external environment and the state of internal facilities. This method ensures that the public resource carrying capacity data used for investment matching is a more accurate value after risk assessment and redundancy considerations.

[0061] This application's solution quantifies the overall impact of various risks and redundancies on the initial design capacity by summing all risk adjustment factors to form a comprehensive resilience factor. Subsequently, by converting the resilience factor into a correction coefficient and multiplying it by the initial design capacity, a precise adjustment of the initial design capacity is achieved. This method avoids the limitations of subjective judgment or simple addition and subtraction, ensuring that the calculation of effective design capacity fully considers the uncertainties of the external environment and the flexibility of internal facilities, making the assessment of public resource carrying capacity more scientific and rigorous.

[0062] In some preferred embodiments, step A204 includes steps performed for each of the aforementioned public resources: The ratio of the actual total occupancy to the effective design capacity is calculated and used as the first metric. Calculate the product of the actual total occupancy change rate and the preset time constant, and divide the product by the effective design capacity to obtain the second performance indicator; The resource scarcity value of the public resource is obtained by weighting and summing the first and second metrics.

[0063] Specifically, the first metric is the ratio of actual total occupancy to effective design capacity. This ratio directly reflects the current saturation level of public resources. For example, when the ratio is close to 1, it indicates that the resource is operating at near full capacity, and its purpose is to provide a static assessment of the current usage status of the resource.

[0064] The second metric is obtained by multiplying the actual total occupancy change rate by a preset time constant, and then dividing the product by the effective design capacity. The actual total occupancy change rate can be understood as the rate of increase or decrease in the actual total occupancy of public resources within a preset time window. The preset time constant is a parameter used to adjust the weighting of the change rate, aiming to balance the proportion of current occupancy status and future trends in the stress assessment; its specific value can be set according to actual needs. This metric aims to reflect the impact of resource occupancy trends on future stress; that is, the faster the resource consumption rate, the higher the future stress level.

[0065] In practical applications, the first and second performance indicators are weighted and summed to comprehensively consider the current usage status and future trends of public resources, thereby obtaining a more comprehensive and dynamic value of resource scarcity. The weighting coefficients can be set according to actual business needs and expert experience to adjust the contribution ratio of the two indicators to the final scarcity value.

[0066] This application's solution addresses the limitations of assessing resource scarcity based solely on a single indicator (such as actual occupancy) by introducing a first and a second metric and weighting them together. Specifically, the first metric directly reflects the current static occupancy of public resources, allowing the system to understand the current resource saturation level. The second metric incorporates the dynamic trend of resource occupancy; by considering the rate of change in total actual occupancy, it can predict resource scarcity over a future period, thus avoiding the problem of focusing only on the current state while ignoring future risks. By weighting and combining these two indicators, this application's solution can more comprehensively and dynamically assess the resource scarcity level of public resources, enabling investment matching decisions to fully consider both current resource pressure and future trends.

[0067] In practical applications, different industry types exhibit significant differences in their sensitivity to public resources. Failure to differentiate between industry types and apply uniform adjustments may result in inaccurate results that fail to fully reflect the actual resource carrying capacity of the land parcels under specific industry plans, thereby affecting the accuracy and effectiveness of investment attraction matching.

[0068] In some implementations, step A3 includes: A301. Obtain the planned industry type of the land parcel to be developed; A302. Query the preset associated database to obtain the sensitivity information of the planned industry type to each of the public resources; the associated database records the sensitivity information of various planned industry types to various public resources; A303. For each public resource, determine the correction range based on the resource scarcity value and the sensitivity information, and correct the initial resource planning parameters of the land parcel to be developed based on the correction range to obtain the actual resource availability parameters of the public resource.

[0069] Obtaining the planned industry type of a plot of land to be developed refers to acquiring the pre-set industry positioning information of the plot through system interfaces, manual input, or by retrieving it from the land management database. Specifically, this can be achieved in the following ways: The system can interface with the urban planning department's database to automatically obtain the planned use of the plot, such as "high-tech industry," "light industry," or "commercial services." Alternatively, in the investment promotion management platform, the administrator can manually input or select the planned industry type of the plot to be developed.

[0070] Querying a pre-built relational database to obtain information on the sensitivity of planned industry types to various public resources involves the system accessing a pre-established database that stores data on the dependence and capacity of different industry types to public resources such as water, electricity, gas, and network bandwidth. Specifically, this can be achieved as follows: The relational database can contain a "Industry Type" table and a "Resource Sensitivity" table, linked by industry type ID. Sensitivity information can be quantified, for example, using a score from 0 to 100. A higher score indicates a higher sensitivity of the industry to a particular resource, meaning a greater impact from resource shortages. For example, the semiconductor manufacturing industry is highly sensitive to electricity and pure water resources.

[0071] In step A303, after obtaining the resource scarcity value and industry sensitivity information, the system calculates the adjustment amount to the initial resource planning parameters based on a preset correction model or algorithm. Specifically, this can be achieved in the following ways: The correction magnitude can be a percentage value. For example, if a resource has a high scarcity level and the planned industry is also highly sensitive to it, the correction magnitude will be larger, resulting in a greater downward adjustment of the initial planning parameters. The correction model can be a linear function, for example: H=K*J*m, where H is the correction magnitude, K is the adjustment coefficient (where K can be set based on expert experience, or by collecting and analyzing historical data from similar projects, and based on the actual correction magnitudes taken under different resource scarcity and sensitivity levels, using statistical analysis methods such as regression analysis to establish an empirical relationship model between H, J, and m, thereby deriving a reasonable value for K), J is the resource scarcity value, and m is the sensitivity information. The corrected actual resource availability parameters will more accurately reflect the actual amount of resources that the land parcel can provide to a specific industry under the current resource environment.

[0072] This solution improves the accuracy of actual resource availability parameters by incorporating information on the sensitivity of planned industry types to public resources, thus enabling more refined adjustments to initial resource planning parameters and providing a more reliable basis for subsequent investment attraction matching. Specifically, firstly, the planned industry type of the land parcel to be developed is obtained through A301. This is the foundation for precise adjustments, as different industries have varying degrees of dependence on and capacity to bear public resources such as water and electricity. Next, A302 queries a pre-set associated database to obtain the sensitivity information of planned industry types to various public resources. This database pre-stores sensitivity data for various planned industry types to different public resources, allowing the system to understand the specific needs and capacity thresholds of different public resources based on the land parcel's specific industry positioning, providing a quantitative basis for subsequent adjustments. Finally, in A303, for each public resource, the adjustment range is determined based on the resource scarcity value and sensitivity information. The initial resource planning parameters of the land parcel to be developed are then adjusted according to this adjustment range to obtain the actual resource availability parameters of the public resources. This step is crucial. It comprehensively considers the current resource shortage and the sensitivity of future industries to resources, ensuring that the revised actual resource availability parameters reflect both the true supply capacity of resources and the actual demand characteristics of industries. This avoids a "one-size-fits-all" approach to revision, making the investment matching results more scientific and reasonable.

[0073] In some implementations, step A4 includes: A401. Obtain the first non-resource matching element of the land parcel to be developed; the first non-resource matching element includes the planned industry type and site attributes; A402. Obtain the second non-resource matching element of the enterprise to be matched; the second non-resource matching element includes industry category and site requirements; A403. Calculate the resource matching score based on the resource demand information of the enterprise to be matched and the actual resource availability parameters; A404. Calculate the non-resource matching score based on the first non-resource matching element and the second non-resource matching element; A405. The resource matching score and the non-resource matching score are weighted and combined to obtain the comprehensive matching score between the enterprise to be matched and the land to be developed; A406. Sort the companies to be matched according to the comprehensive matching score to determine the final investment promotion matching result.

[0074] In step A401, the first non-resource-based matching element can be obtained by directly reading from the land parcel management database, querying through a Geographic Information System (GIS), or manually entering the data. The planned industry type can include, but is not limited to, high-tech industries, light industry, and commercial services. Site attributes can include area (e.g., square meters, mu), plot ratio range (e.g., 1.0-2.5), and building density range (e.g., 30%-50%).

[0075] In step A402, the second non-resource-based matching element can be obtained by extracting information from company registration information, industry reports, company application forms, or through market research and expert interviews. Industry categories can include, but are not limited to, electronics and information technology, biomedicine, and new energy vehicles. Site requirements can include area requirements (e.g., required factory or office space), floor area ratio requirements (e.g., desired floor area ratio), and building density requirements (e.g., desired building density).

[0076] In step A403, resource demand information refers to the specific demand of enterprises for public resources such as water, electricity, gas, and network bandwidth. Actual resource availability parameters refer to the actual capacity of the land parcel to provide various public resources to newly established enterprises, after considering the actual occupancy of shared infrastructure within the area. The resource matching score can be calculated using various methods. For example, by comparing the enterprise's resource demand with the land parcel's actual resource availability parameters, the difference or ratio between the two can be calculated and converted into a score. Specifically, if the resource demand is less than or equal to the actual resource availability parameters, a positive matching score is calculated based on resource utilization; if the resource demand is greater than the actual resource availability parameters, a negative matching score is calculated based on excess resource demand. The matching scores for each public resource can be weighted and summed to obtain the final resource matching score.

[0077] In step A404, the calculation of the non-resource matching score can employ various methods. For instance, by establishing matching rules or models, the planned industry type and industry category, as well as site attributes and site requirements, can be compared and evaluated one by one. For example, regarding the planned industry type and industry category, a pre-set comparison table can be used to determine whether the industry category matches the planned industry type. If they match, a pre-set matching score is assigned; otherwise, the matching score is set to zero. Regarding site attributes and site requirements, the deviation rate between each site requirement and its corresponding site attribute can be calculated (e.g., for area requirements and area, the difference between the area and the area requirement can be calculated and then divided by the area requirement to obtain the deviation rate; for plot ratio requirements and plot ratio range, the difference between the center value of the plot ratio range and the plot ratio requirement can be calculated and then divided by the plot ratio requirement to obtain the deviation rate). The deviation rate is calculated by dividing the difference between the center value of the building density range and the building density requirement by the building density requirement. Then, a corresponding preset conversion function (e.g., F=u / (1+k*p^2), where F is the matching score, p is the deviation rate, u is the proportional coefficient (u can be different for different site requirements), and k is a positive adjustment coefficient used to control the sensitivity of the matching score F to the deviation rate p. The tolerance for deviation can be set according to the specific application scenario) is used to convert the deviation rate into a matching score. These matching scores can be weighted and summed to obtain the non-resource matching score.

[0078] In step A405, weighted aggregation refers to the linear combination or other forms of fusion of resource matching scores and non-resource matching scores according to preset weights. The weights can be adjusted according to investment promotion strategies, land characteristics, or industry needs. For example, in some cases, resource matching may be more important, while in others, non-resource matching may be more decisive.

[0079] In step A406, the sorting can be done in descending order. Companies with higher overall matching scores have a higher degree of compatibility with the land parcels to be developed, and thus rank higher in the investment matching results. The final investment matching results can be output in a list, chart, or other visual format for the investment promotion parties to refer to.

[0080] This solution addresses the problem of existing technologies that only consider resource matching while neglecting non-resource factors by introducing non-resource matching elements and weighting them together with resource matching elements. By evaluating non-resource factors such as planned industry type, site attributes, industry category, and site requirements, the matching results are more comprehensive and accurate. Furthermore, by ranking the comprehensive matching scores, a clear decision-making basis is provided for investment promotion parties, avoiding investment promotion decision-making errors caused by incomplete information or inaccurate assessments.

[0081] In some implementations, the resource demand information includes the resource demand of the enterprise to be matched for each of the public resources; Step A403 includes: For each of the resource requirements, the resource requirement is compared with the corresponding actual resource availability parameter; If the resource demand is less than or equal to the corresponding actual resource availability parameter, the matching score of the corresponding public resource is calculated in a positive direction based on the resource utilization rate. If the resource demand exceeds the corresponding actual resource availability parameter, the matching score of the corresponding public resource is calculated negatively based on the excess resource demand. The matching scores of the various public resources are weighted and summed to obtain the resource matching score.

[0082] The specific needs of each public resource for the companies to be matched can be obtained through the companies' application forms, business plans, or interviews. This information lays the foundation for accurate comparisons in the future.

[0083] Among them, comparing the resource demand with the corresponding actual resource availability parameter means comparing the enterprise's demand for a certain public resource with the amount of that resource that the land can actually provide.

[0084] Resource utilization rate refers to the degree to which a company utilizes resources when the resource demand is less than or equal to the actual resource availability parameter. Specifically, it can be calculated as the ratio of demand to availability. For example, if a company demands 500kW and has 800kW available, the utilization rate is 500 / 800 = 0.625. The matching score can be positively calculated based on this utilization rate; for example, the higher the utilization rate, the higher the score, encouraging full utilization of resources (this can be calculated using a linear function, such as y = a*x + b, where y is the matching score, x is the utilization rate, and a and b are constant coefficients).

[0085] The excess resource demand refers to the portion of a company's demand that exceeds the actual available resources on a land parcel when the demand exceeds the available resource parameter. Specifically, it can be calculated by subtracting the availability parameter from the demand. For example, if a company demands 1000kW and has 880kW available, the excess demand is 120kW. The matching score can be negatively calculated based on this excess demand; for example, the larger the excess demand, the lower the score, reflecting the negative impact of resource shortages (this can be calculated using a linear function, such as y = -c*v + d, where v is the excess resource demand, and c and d are constant coefficients).

[0086] The weighted aggregation of matching scores for various public resources involves assigning different weights to the matching scores of different public resources according to their importance, and then summing them. For example, for a high-energy-consuming enterprise, the matching score for electricity resources may be given a higher weight. Through weighted aggregation, the matching status of enterprises with different public resources can be comprehensively considered, and a comprehensive and representative resource matching score can be obtained according to their importance.

[0087] This application's solution, through a detailed comparison of resource demand and actual resource availability parameters, and employing different calculation logics based on the comparison results (positive calculation of resource utilization matching score or negative calculation of resource excess demand matching score), can more comprehensively and precisely quantify the degree of resource matching between enterprises to be matched and land parcels to be developed. This mechanism avoids simple "yes / no" judgments, instead providing a continuous and discriminative matching score, thereby accurately identifying matching situations with high resource utilization efficiency or risks of resource overload. Through weighted aggregation, the relative importance of different public resources in investment matching is further considered, ensuring that the final resource matching score more accurately reflects the overall resource suitability.

[0088] refer to Figure 2 This application provides an intelligent investment attraction matching system based on the entire life cycle of land, used to match undeveloped land parcels in target areas containing shared infrastructure for investment attraction. The system includes: Data acquisition module 1 is used to acquire the actual resource occupancy data of the public resources provided by the shared infrastructure to the land parcels already settled in the target area (for details, please refer to step A1 above). The stress calculation module 2 is used to calculate the stress value of each public resource based on the actual resource occupancy data and the effective design capacity of the shared infrastructure (for details, please refer to step A2 above). The parameter correction module 3 is used to correct the initial resource planning parameters of the undeveloped land plots in the target area using the resource scarcity value, so as to obtain the actual resource availability parameters of each public resource (for details, please refer to step A3 above). Matching module 4 is used to obtain the resource demand information of the enterprises to be matched, and combine the actual resource availability parameters to evaluate the matching degree between the enterprises to be matched and the land to be developed, so as to output the investment matching results (for the specific process, please refer to step A4 above).

[0089] The above descriptions are merely some embodiments of the present invention. Those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A smart investment attraction matching method based on the entire life cycle of land, used for investment attraction matching of undeveloped land parcels in target areas containing shared infrastructure, characterized in that, The steps of this method include: A1. Obtain data on the actual resource occupancy of shared infrastructure provided by the land parcels already occupied within the target area; A2. Based on the actual resource occupancy data and the effective design capacity of the shared infrastructure, calculate the resource scarcity value of each public resource; A3. The initial resource planning parameters of the undeveloped land parcels in the target area are corrected using the resource scarcity value to obtain the actual resource availability parameters of each public resource; A4. Obtain the resource demand information of the enterprises to be matched, and combine it with the actual resource availability parameters to evaluate the matching degree between the enterprises to be matched and the land to be developed, so as to output the investment promotion matching results.

2. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 1, characterized in that, Step A1 includes: A101. Obtain resource consumption data of the various public resources provided by the shared infrastructure for the land parcels that have been occupied within a preset period; A102. Clean and preprocess the resource consumption data to obtain processed resource consumption data; A103. Calculate the actual resource usage data based on the processed resource consumption data.

3. The intelligent investment attraction matching method based on the entire life cycle of land as described in claim 1, characterized in that, Step A2 includes: A201. Obtain the effective design capacity of each of the aforementioned public resources; A202. For each of the aforementioned public resources, obtain the actual total occupancy of the public resource based on the actual resource occupancy data of each of the aforementioned occupied land parcels; A203. Calculate the rate of change of the actual total occupancy based on the actual total occupancy within the preset time window; A204. Calculate the resource scarcity value for each public resource based on the effective design capacity, the actual total occupancy, and the rate of change of the actual total occupancy.

4. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 3, characterized in that, Step A201 includes the steps to be performed for each of the aforementioned public resources: Obtain the initial design capacity of the public resource; Obtain external environmental risk information that affects the carrying capacity of the shared infrastructure, and obtain the status information of the internal redundant facilities of the shared infrastructure; Based on the aforementioned external environmental risk information, determine the risk adjustment factor for each external environmental risk item; Based on the status information of the internal redundant facilities, a risk adjustment factor for each internal redundant facility is determined; The initial design capacity is adjusted according to the risk adjustment factor to obtain the effective design capacity of the public resource.

5. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 4, characterized in that, The step of adjusting the initial design capacity according to the risk adjustment factor to obtain the effective design capacity of the public resource includes: Calculate the sum of all the risk adjustment factors and normalize the sum to obtain the elasticity factor; Calculate the difference between 1 and the elasticity factor, and use it as a correction coefficient; The effective design capacity of the public resource is obtained by multiplying the correction factor by the initial design capacity.

6. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 3, characterized in that, Step A204 includes the steps to be performed for each of the aforementioned public resources: The ratio of the actual total occupancy to the effective design capacity is calculated and used as the first metric. Calculate the product of the actual total occupancy change rate and the preset time constant, and divide the product by the effective design capacity to obtain the second performance indicator; The resource scarcity value of the public resource is obtained by weighting and summing the first and second metrics.

7. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 1, characterized in that, Step A3 includes: A301. Obtain the planned industry type of the land parcel to be developed; A302. Query the preset associated database to obtain the sensitivity information of the planned industry type to each of the public resources; the associated database records the sensitivity information of various planned industry types to various public resources; A303. For each public resource, determine the correction range based on the resource scarcity value and the sensitivity information, and correct the initial resource planning parameters of the land parcel to be developed based on the correction range to obtain the actual resource availability parameters of the public resource.

8. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 1, characterized in that, Step A4 includes: A401. Obtain the first non-resource matching element of the land parcel to be developed; the first non-resource matching element includes the planned industry type and site attributes; A402. Obtain the second non-resource matching element of the enterprise to be matched; the second non-resource matching element includes industry category and site requirements; A403. Calculate the resource matching score based on the resource demand information of the enterprise to be matched and the actual resource availability parameters; A404. Calculate the non-resource matching score based on the first non-resource matching element and the second non-resource matching element; A405. The resource matching score and the non-resource matching score are weighted and combined to obtain the comprehensive matching score between the enterprise to be matched and the land to be developed; A406. Sort the companies to be matched according to the comprehensive matching score to determine the final investment promotion matching result.

9. The intelligent investment promotion matching method based on the entire life cycle of land as described in claim 8, characterized in that, The resource demand information includes the resource demand of the enterprise to be matched for each of the public resources; Step A403 includes: For each of the resource requirements, the resource requirement is compared with the corresponding actual resource availability parameter; If the resource demand is less than or equal to the corresponding actual resource availability parameter, the matching score of the corresponding public resource is calculated in a positive direction based on the resource utilization rate. If the resource demand exceeds the corresponding actual resource availability parameter, the matching score of the corresponding public resource is calculated negatively based on the excess resource demand. The matching scores of the various public resources are weighted and summed to obtain the resource matching score.

10. A smart investment attraction matching system based on the entire life cycle of land, used for investment attraction matching of undeveloped land parcels in target areas containing shared infrastructure, characterized in that, The system includes: The data acquisition module is used to acquire data on the actual resource occupancy of the shared infrastructure provided by the land parcels already occupied within the target area; The stress calculation module is used to calculate the stress level of each public resource based on the actual resource occupancy data and the effective design capacity of the shared infrastructure. The parameter correction module is used to correct the initial resource planning parameters of the undeveloped land parcels in the target area using the resource scarcity value, so as to obtain the actual resource availability parameters of various public resources. The matching module is used to obtain the resource demand information of the enterprises to be matched, and combine it with the actual resource availability parameters to evaluate the matching degree between the enterprises to be matched and the land to be developed, so as to output the investment promotion matching results.

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