Low-efficiency industrial land transformation strategy optimization method based on industrial chain analysis

Abstract

The invention discloses a low-efficiency industrial land transformation strategy optimization method based on industrial chain analysis. The method comprises the following steps: acquiring related information data in a research area; calculating a target industrial land benefit index based on the preprocessed related information data, and identifying the low-efficiency industrial land according to the industrial land benefit index to obtain a low-efficiency industrial land identification result; the method comprises the following steps: constructing an enterprise association network based on a social network analysis method, calculating network feature values of enterprise nodes, identifying key enterprises in key industrial links from an industrial chain dimension, and carrying out land type division according to low-efficiency industrial lands in low-efficiency industrial land identification results related to the key enterprises, and carrying out transformation strategy optimization according to the division result. The method aims at overcoming the defect that in the prior art, a low-efficiency industrial land transformation strategy lacks consideration of the influence of an industrial chain on land benefits.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a method for optimizing inefficient industrial land transformation strategies based on industrial chain analysis. Background Art

[0002] Industry is the core driving force behind sustainable urban development. As the spatial carrier of industrial development, the efficiency of industrial land directly affects the quality of regional development.

[0003] With the continuous advancement of new urbanization, industrial land has become a key target for tapping the potential of existing resources, facing the dual challenges of rigid land resource constraints and the transition to high-quality development. The identification and redevelopment of inefficient industrial land helps improve land use efficiency and unlock land potential, and is also a key measure for optimizing economic structure and industrial upgrading. Therefore, promoting the identification and transformation of inefficient industrial land has become an inherent requirement for optimizing the pattern of land space development and protection and adapting to economic structural adjustments. Against this backdrop, exploring scientific approaches to identifying and transforming inefficient industrial land will help revitalize and improve the quality of industrial resources, providing more potential for urban and industrial development.

[0004] An industrial chain refers to a value-added, network-like network of interconnected sectors within a region, formed through economic or technological activities. Essentially, it represents an organizational form of industrial division of labor and cooperation. Industrial land is not only the physical space for industrial production but also a geographical hub for the coordination of industrial chain elements. Although inefficient industrial land currently exhibits issues such as low utilization efficiency and low industrial returns, it exists within a complex industrial chain network and may serve as a key connecting node, exerting a significant influence on upstream and downstream industries or other related enterprises within the chain. Therefore, when formulating strategies for the transformation of inefficient industrial land, its position and role within the industrial chain must be fully considered to avoid missing key links in the chain and weakening the resilience of regional industrial networks.

[0005] Existing technical solutions mainly focus on analyzing the benefit dimensions of industrial land itself, focusing on the benefit indicators of economic benefits, social benefits, land use structure and other dimensions. Through expert scoring method, entropy weight method, clustering algorithm and other methods, the performance level of industrial land is measured and evaluated, and industrial land with low performance level is used as the result of industrial inefficient land identification, and the transformation strategy of inefficient industrial land is proposed based on benefit indicators and performance evaluation.

[0006] Existing technical solutions lack consideration of the impact of industrial chains or regional industrial linkages on land efficiency. Most current evaluation technologies are limited to focusing on the benefits of the land itself. A few studies have conducted assessments of industrial potential, but they are also limited to considering whether the industry is consistent with the regional development direction. They have not analyzed the characteristics of the dynamic collaborative network of industries from the perspective of actual data on industrial interaction and cooperation, and have ignored the potential impact of industrial linkages within the industrial chain or region on land efficiency. Therefore, existing inefficient industrial land identification technologies are difficult to reflect the impact of industrial chains on land use efficiency, resulting in a "one-size-fits-all" extensive transformation strategy, which may cause some key industrial land that should be upgraded and improved to be mistakenly classified as vacated and transformed. Summary of the Invention

[0007] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0008] To this end, the present invention proposes a low-efficiency industrial land transformation strategy optimization method based on industrial chain analysis, which uses multi-source spatiotemporal data to carry out low-efficiency industrial land identification and industrial chain analysis, and optimizes the low-efficiency industrial land transformation strategy on this basis.

[0009] Another object of the present invention is to propose an optimization system for transformation strategies of inefficient industrial land based on industrial chain analysis.

[0010] To achieve the above objectives, the present invention proposes, on one hand, a method for optimizing the transformation strategy of inefficient industrial land based on industrial chain analysis, comprising:

[0011] Obtain relevant information data within the study area; wherein the relevant information data includes remote sensing image data, mobile phone signaling data, and enterprise information;

[0012] Calculate the efficiency of target industrial land based on the pre-processed relevant information data, and identify inefficient industrial land according to the industrial land efficiency index to obtain the inefficient industrial land identification results;

[0013] Based on the social network analysis method, an enterprise association network is constructed, the network characteristic values of enterprise nodes are calculated, and key enterprises in key industrial links are identified from the industrial chain dimension. The land use types are divided according to the inefficient industrial land identified by the key enterprises, so as to optimize the transformation strategy according to the division results.

[0014] The inefficient industrial land transformation strategy optimization method based on industrial chain analysis according to the embodiment of the present invention may also have the following additional technical features:

[0015] In one embodiment of the present invention, the remote sensing images include multispectral images and night light images; the enterprise information includes multiple types of industrial and commercial information, annual business report information, investment information and patent information; the industrial land efficiency indicators include land development efficiency indicators, land use efficiency indicators and land output efficiency indicators.

[0016] In one embodiment of the present invention, calculating the land development benefit index includes:

[0017] Based on remote sensing image data, bare land and construction site image samples are obtained, and the spatial distribution of unconstructed bare land is extracted through image supervised classification using the random forest algorithm.

[0018] Collect building image samples and obtain the spatial distribution of built building outlines through semantic segmentation algorithms;

[0019] The proportion of undeveloped bare land and building area in each plot unit is counted, and the idleness of the plot and the building density level are calculated based on the statistical results to obtain the land development benefit index.

[0020] In one embodiment of the present invention, calculating the land use efficiency index includes:

[0021] The spatial distribution of surface temperature is obtained through the surface temperature inversion algorithm, and the distribution of nighttime activity clusters is obtained through nighttime light images;

[0022] Analyze the spatial distribution of employed people based on mobile phone signaling data;

[0023] The concentration of human activities on industrial land is calculated based on the spatial distribution of activity concentration and the employed population to obtain the land use efficiency index.

[0024] In one embodiment of the present invention, calculating the land output efficiency index includes:

[0025] Based on the geographical coordinates of the enterprise, the enterprise is matched and associated with the land parcel to establish a land-enterprise information database, and the average land output value and average land tax are calculated based on the enterprise output value and enterprise tax situation;

[0026] Based on the situation where there are several enterprises on the same land, the economic benefits of the land are calculated by superimposing the data of all enterprises;

[0027] The economic output of industrial land is calculated based on the average output value per plot, average tax revenue per plot and economic benefits of the land to obtain the land output benefit index.

[0028] In one embodiment of the present invention, inefficient industrial land is identified according to the industrial land efficiency index to obtain an inefficient industrial land identification result, including:

[0029] The industrial land benefit evaluation index is obtained by integrating the land development benefit index, land use benefit index and land output benefit index;

[0030] The industrial land benefit evaluation index is used as a characteristic parameter for normalization, and the clustering algorithm is used to classify the normalized characteristic parameter results to obtain cluster characteristics.

[0031] According to the cluster characteristics, the land clusters with cluster characteristic values lower than the preset threshold are judged as inefficient industrial land, and the inefficient industrial land identification results are obtained by dividing them according to the dimensions to which the low characteristic values belong; wherein, the inefficient industrial land identification results include development inefficiency type, use inefficiency type, output inefficiency type and comprehensive inefficiency type.

[0032] In one embodiment of the present invention, building an enterprise association network based on social network analysis includes:

[0033] Based on enterprise investment data, each enterprise is regarded as a node in the network, the investment connection between enterprises is regarded as the edge of the network, and the investment scale between enterprises is regarded as the weight of the edge to construct an enterprise investment association network;

[0034] Based on the upstream and downstream positions of the industry to which the enterprise belongs in the industrial chain, each industry is regarded as a network node, and the upstream and downstream relationships between industries are regarded as network edges to construct an industrial chain association network;

[0035] Based on enterprise patent information data, each enterprise is regarded as a node of the network, the patent relationship between enterprises is regarded as the edge of the network, and the number of patent relationships is used as the weight of the edge to construct an enterprise patent association network.

[0036] In one embodiment of the present invention, an enterprise association network is constructed based on a social network analysis method, network characteristic values of enterprise nodes are calculated, key enterprises in key industrial links are identified from the perspective of the industrial chain, and land use types are classified based on the inefficient industrial land identified by the key enterprises, so as to optimize the transformation strategy according to the classification results, including:

[0037] Calculate the centrality of enterprise nodes in enterprise investment association networks and enterprise patent association networks;

[0038] Calculate the hub degree of enterprise nodes in the enterprise investment association network, enterprise patent association network, and the hub degree of industry nodes in the industry chain association network;

[0039] Based on the centrality and hubness of the enterprise investment association network, the enterprise patent association network, and the industry node hubness of the industrial chain association network, the structural characteristics of the association network of enterprises and their respective industries are comprehensively calculated to identify key enterprises in the industrial chain dimension based on the characteristic analysis results;

[0040] The inefficient industrial land involved in the identification of key enterprises will be divided into inefficient industrial land for retention, improvement and upgrading, and the remaining inefficient industrial land will be divided into inefficient industrial land for vacancy and redevelopment, so as to optimize the renewal and transformation strategy according to the division results.

[0041] In one embodiment of the present invention, the centrality of enterprise nodes in the enterprise investment association network and the enterprise patent association network is calculated using the following formula:

[0042]

[0043] Where C D (x) is the centrality of the network node x, there are V nodes in the network, z x,y is the weight of the edge between network node x and node y;

[0044] The calculation formula for calculating the hub degree of enterprise nodes in the enterprise investment association network and enterprise patent association network, and the hub degree of industry nodes in the industry chain association network is as follows:

[0045]

[0046] Where C B (x) is the hub degree of the network node x. The path length between two nodes in the network is defined as the number of edges connecting the two nodes on the path. Let path w,y is the number of shortest paths between node w and node y, path w,y (x) is the number of shortest paths between node w and node y that pass through node x.

[0047] To achieve the above objectives, the present invention further proposes a system for optimizing the transformation strategy of inefficient industrial land based on industrial chain analysis, comprising:

[0048] A research data acquisition module is used to acquire relevant information data within the research area; wherein the relevant information data includes remote sensing image data, mobile phone signaling data and enterprise information;

[0049] The low-efficiency land identification module is used to calculate the efficiency of the target industrial land based on the pre-processed relevant information data, and identify the low-efficiency industrial land according to the industrial land efficiency index to obtain the low-efficiency industrial land identification results;

[0050] The strategy optimization and transformation module is used to construct an enterprise association network based on the social network analysis method, calculate the network characteristic values of enterprise nodes, identify key enterprises in key industrial links from the industrial chain dimension, and classify the inefficient industrial land involved in the key enterprises according to the inefficient industrial land identification results, so as to optimize the transformation strategy according to the classification results.

[0051] The inefficient industrial land transformation strategy optimization method and system based on industrial chain analysis of the embodiments of the present invention can overcome the deficiency of the inefficient industrial land transformation strategy in the prior art in that the inefficient industrial land transformation strategy lacks consideration of the impact of the industrial chain on land efficiency.

[0052] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0054] Figure 1 is a flow chart of a method for optimizing transformation strategies of inefficient industrial land based on industrial chain analysis according to an embodiment of the present invention;

[0055] Figure 2 It is a structural diagram of an inefficient industrial land transformation strategy optimization system based on industrial chain analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0058] The following describes, with reference to the accompanying drawings, a method and system for optimizing a strategy for transforming inefficient industrial land based on industrial chain analysis according to an embodiment of the present invention.

[0059] Figure 1 is a flow chart of a method for optimizing transformation strategies of inefficient industrial land based on industrial chain analysis according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] S1, obtaining relevant information data within the study area; wherein the relevant information data includes remote sensing image data, mobile phone signaling data and enterprise information.

[0061] It is understood that the present invention obtains remote sensing images, mobile phone signaling, enterprise information and other data within the study area and performs pre-processing. Specifically, it includes the following sub-steps:

[0062] S11. Convert the multispectral images and night light images in the remote sensing images to the same plane coordinate system and perform corresponding preprocessing (radiometric calibration and geometric correction can be used). Correct the abnormal negative value problem of the night light images (threshold truncation or noise filtering can be performed) to eliminate potential errors in the sensor or data acquisition process.

[0063] S12. Extract information about the working population based on mobile phone signaling data. Mobile phone signaling data contains rich information about urban activities, which can support the mining of spatiotemporal characteristics of crowd trajectories. First, the location where the user stays the longest from 21:00 to 8:00 the next day is counted as the user's residence, and the users whose average daily residence time in the residence in that month is greater than 3 hours are screened to extract the residential population and their residence. Secondly, people aged 19-60 are screened in terms of user age, and the location where the user stays the longest from 9:00 to 17:00 on weekdays is counted as the user's workplace, and the users whose average daily residence time in the workplace in that month is greater than 4 hours are screened. At the same time, the user's workplace location does not overlap with the residence location, so as to extract the working population and their workplace.

[0064] S13. Obtain the company's business information, annual operating report information, investment information, patent information, and other data, and perform pre-processing such as data cleaning and sorting. First, based on the company's address text information, match geographic coordinates through methods such as address resolution. Next, perform data cleaning, including removing duplicate records and eliminating data that does not meet requirements. Attribute fields such as company name, industry, output value and tax, authorized patents, and investment information are standardized.

[0065] S2, calculates the efficiency of the target industrial land based on the preprocessed relevant information data, and identifies the inefficient industrial land according to the industrial land efficiency index to obtain the inefficient industrial land identification result.

[0066] Specifically, the present invention conducts a benefit assessment on the target industrial land, analyzing basic indicators such as land development benefit, land use benefit, and land output benefit. The specific implementation includes the following sub-steps:

[0067] S21. Regarding land development benefits, we collected samples of bare land and construction sites based on remote sensing imagery data. We then used the random forest algorithm for supervised image classification to determine the spatial distribution of undeveloped bare land. We also collected samples of building images and used the DeepLab V3+ algorithm for semantic segmentation to determine the spatial distribution of completed building outlines. We calculated the ratio of undeveloped bare land to building area within each plot unit, using this information to assess land vacancy and building density, supporting land development benefit analysis.

[0068] The calculation formula for the proportion of undeveloped bare land and the proportion of building land is as follows:

[0069] F A_Bare =A Bare / A

[0070] F A_Building =A Building / A

[0071] Where, F A_Bare A is the proportion of bare land area that has not been constructed yet. Bare is the area of bare land that has not been constructed, A is the total area of the land, F A_Building is the proportion of the building area of the plot, A Building The building area of the plot.

[0072] S22. Regarding land use efficiency, we used remote sensing imagery data and a surface temperature inversion algorithm to determine the spatial distribution of surface temperature. We also used nighttime light imagery to determine the concentration of nighttime activities. We also analyzed the spatial distribution of employed people based on mobile phone signaling data. We calculated the mean surface temperature, mean nighttime light brightness, and employment density for each plot unit to support land use efficiency analysis.

[0073] Taking the inversion of surface temperature from Landsat8 images as an example, after the radiometric brightness conversion, the single-channel split-window algorithm can be used to invert the surface temperature. The formula is:

[0074]

[0075] Where T is the inverted surface temperature (usually in Kelvin, which needs to be further converted to Celsius), K1 and K2 are sensor calibration coefficients, and is the radiance of Band 10 and Band 11, and C is the empirical correction coefficient.

[0076] The calculation formulas for the mean surface temperature, mean nighttime light brightness, and employment population density are:

[0077]

[0078] Where, F B_Temp is the average surface temperature of the plot, T i is the surface temperature of the i-th pixel in the plot, N is the total number of pixels in the plot, F B_Light is the average nighttime light brightness of the plot, L i is the night light brightness value of the i-th pixel in the plot, F B_Work is the employment population density of the plot, W iis the number of employed people in the i-th pixel within the plot.

[0079] S23. Regarding land output benefits, based on the company's geographic coordinates, the company is matched to the land parcel, and a land-enterprise information database is established. The average output value and tax revenue per parcel are calculated based on the company's output value and tax revenue. In the case of multiple companies operating on the same land (i.e., "one land parcel with multiple companies"), the economic benefits of the land are calculated by combining the data of all companies to reduce the performance evaluation errors caused by "one land parcel with multiple companies." This data is used to calculate the economic output of industrial land and support land output benefit analysis.

[0080] The calculation formulas for the average land output value and average land tax are as follows:

[0081]

[0082] Where, F C_Output is the average land output value of the plot, O j is the output value scale of the jth enterprise in the plot, S is the number of enterprises in the plot, A is the plot area, F C_Tax Tax is the land tax per parcel. j is the tax scale of the jth enterprise in the plot.

[0083] S24. Based on the industrial land benefit indicators in three dimensions, namely land development benefit, land use benefit and land output benefit, the K-means clustering algorithm is used to identify inefficient industrial land.

[0084] Specifically, the industrial land benefit evaluation index is obtained by integrating the land development benefit index, land use benefit index and land output benefit index; the industrial land benefit evaluation index is normalized as the characteristic parameter, and the clustering algorithm is used to classify the normalized characteristic parameter results to obtain cluster characteristics; according to the cluster characteristics, the land clusters with cluster characteristic values lower than the preset threshold are judged as inefficient industrial land, and the low-efficiency industrial land identification results are obtained by dividing them according to the dimensions to which the low characteristic values belong; among them, the inefficient industrial land identification results include development inefficiency type, use inefficiency type, output inefficiency type and comprehensive inefficiency type.

[0085] First, the industrial land benefit evaluation index based on the comprehensive land development benefit, land use benefit, and land output benefit is used as a characteristic parameter and normalized to eliminate dimensional differences. The normalization formula is:

[0086]

[0087] k1∈{F A_Bare}, k2∈{F A_Building ,F B_Temp ,F B_Light ,FB_Work ,F C_Output ,F C_Tax}, I norm(k) is the normalized result of the k-th category index, I (k) is the original value of the k-th indicator, I min(k) is the minimum value of the k-th indicator, I max(k) is the maximum value of the k-th indicator. After normalization, the value range of each indicator is [0,1].

[0088] Secondly, the K-means clustering algorithm is used to classify the normalized results of the characteristic parameters into several clusters, so that the cohesion of each sample is high and the separation between different categories is good. The clustering effect is measured by indicators such as the silhouette coefficient. The mean of the land development benefit, land use benefit, and land output benefit of all plots in the study area in the three dimensions is calculated. The mean of each cluster in the three dimensions is also calculated as the benchmark for inefficiency identification and classification. The calculation formula is as follows:

[0089]

[0090]

[0091] Where μ A 、μ B 、μ C are the means of all plots in terms of land development benefit, land use benefit, and land output benefit, respectively. M is the number of plots, μ Cluster_A 、μ Cluster_B 、μ Cluster_C are the mean values of the land parcels in a certain cluster in terms of land development benefit, land use benefit, and land output benefit, respectively. O is the number of land parcels in the cluster. According to the cluster characteristics, at least μ Cluster_B <0.8×μ B 、μ Cluster_C <0.8×μ C Clusters that meet one of the three conditions are identified as inefficient industrial land. Clusters that meet only one of the three conditions are identified as development-inefficient, use-inefficient, or output-inefficient, respectively. Clusters that meet two or three of the three conditions are identified as comprehensive-inefficiency. Based on this, four types of inefficient industrial land are identified: development-inefficient, use-inefficient, output-inefficient, and comprehensive-inefficiency.

[0092] S3, based on the social network analysis method, constructs an enterprise association network, calculates the network characteristic values of enterprise nodes, identifies key enterprises in key industrial links from the industrial chain dimension, and divides the land use types according to the inefficient industrial land identified by the key enterprises, so as to optimize the transformation strategy according to the division results.

[0093] Specifically, this invention uses social network analysis to mine the structure and characteristics of enterprise association networks, constructing association networks such as enterprise investment, upstream and downstream industries, and enterprise patents. From the perspective of the industrial chain, it identifies enterprises in key industrial links and the land parcels they belong to, and proposes targeted optimization and transformation strategies based on this. The specific implementation includes the following sub-steps:

[0094] S31. In terms of enterprise investment associations, based on enterprise investment data, each enterprise is regarded as a node in the network, the investment links between enterprises are regarded as the edges of the network, and the investment scale between enterprises is regarded as the weight of the edge, thereby constructing an enterprise investment association network to explore enterprises that are closely related through investment, acquisition, equity, etc.

[0095] S32. In terms of industrial chain linkage, based on the upstream and downstream positions of the industry to which the enterprise belongs in the industrial chain, each industry is regarded as a network node, and the upstream and downstream relationships between industries are regarded as the edges of the network, and then an industrial chain linkage network is constructed to explore enterprises in key industrial links.

[0096] S33. In terms of enterprise patent association, based on enterprise patent information data, each enterprise is regarded as a node in the network, patent relationships such as patent authorization and joint research and development between enterprises are regarded as edges in the network, and the number of patent relationships is regarded as the weight of the edge, thereby constructing an enterprise patent association network to explore enterprises that undertake technological innovation or key technology roles in the industrial chain.

[0097] S34. Calculate the centrality (i.e., degree centrality) of enterprise nodes in the enterprise investment association network and enterprise patent association network. The calculation formula is as follows:

[0098]

[0099] Where C D (x) is the centrality of the network node x, there are V nodes in the network, z x,y is the weight of the edge between network node x and node y.

[0100] S35. Calculate the hubness (i.e., betweenness centrality) of enterprise nodes in the enterprise investment association network and enterprise patent association network, and the hubness of industry nodes in the industry chain association network. The calculation formula is as follows:

[0101]

[0102] Where C B (x) is the hub degree of the network node x. The path length between two nodes in the network is defined as the number of edges connecting the two nodes on the path. Let path w,y is the number of shortest paths between node w and node y, path w,y(x) is the number of shortest paths between node w and node y that pass through node x.

[0103] S36. Based on the industrial chain analysis, comprehensively measure the structural characteristics of the association network of enterprises and their industries. Taking enterprises as statistical units, calculate the average of the following network characteristic values of all enterprises in the research area as the value of the enterprise industrial chain: the centrality and hub degree of the enterprise investment association network, the centrality and hub degree of the enterprise patent association network, and the industry node hub degree of the industrial chain association network. Take the top 50% of the enterprises in the research area in terms of industrial chain value as the identified key enterprises.

[0104] S37. Classify the inefficient industrial land associated with the aforementioned key enterprises as "inefficient industrial land for preservation, improvement, and upgrading," and the remaining inefficient industrial land as "inefficient industrial land for vacant and redevelopment." Based on this, further refine and optimize the renewal strategy to facilitate the precise management and value regeneration of inefficient industrial land. Examples are shown in Table 1.

[0105] Table 1

[0106]

[0107]

[0108]

[0109] The inefficient industrial land transformation strategy optimization method based on industrial chain analysis according to an embodiment of the present invention can overcome the deficiency of the existing technology in the inefficient industrial land transformation strategy that lacks consideration of the impact of the industrial chain on land efficiency. By introducing industrial chain analysis, the present invention can optimize the existing method in the process of promoting the formulation of inefficient industrial land transformation strategy, fully considering its position and role in the industrial chain, so as to avoid problems such as the loss of key links in the industrial chain and the weakening of the resilience of the regional industrial network.

[0110] In order to implement the above embodiment, Figure 2 As shown, this embodiment also provides an inefficient industrial land transformation strategy optimization system 10 based on industrial chain analysis, including:

[0111] The research data acquisition module 100 is used to acquire relevant information data within the research area; wherein the relevant information data includes remote sensing image data, mobile phone signaling data and enterprise information;

[0112] The low-efficiency land identification module 200 is used to calculate the efficiency of the target industrial land based on the pre-processed relevant information data, and identify the low-efficiency industrial land according to the industrial land efficiency index to obtain the low-efficiency industrial land identification result;

[0113] The strategy optimization and transformation module 300 is used to construct an enterprise association network based on the social network analysis method, calculate the network characteristic values of enterprise nodes, identify key enterprises in key industrial links from the industrial chain dimension, and classify the inefficient industrial land involved in the key enterprises according to the inefficient industrial land identification results, so as to optimize the transformation strategy according to the classification results.

[0114] The inefficient industrial land transformation strategy optimization system based on industrial chain analysis according to an embodiment of the present invention can overcome the deficiency of the existing technology in the inefficient industrial land transformation strategy that lacks consideration of the impact of the industrial chain on land efficiency. By introducing industrial chain analysis, the present invention can optimize the existing method and fully consider its position and role in the industrial chain in the process of promoting the formulation of inefficient industrial land transformation strategies, thereby avoiding problems such as the loss of key links in the industrial chain and the weakening of the resilience of regional industrial networks.

[0115] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0116] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Claims

1. A method for optimizing the transformation strategy of inefficient industrial land based on industrial chain analysis, characterized in that: include: Obtain relevant information data within the study area; wherein the relevant information data includes remote sensing image data, mobile phone signaling data, and enterprise information; Calculate the efficiency of target industrial land based on the pre-processed relevant information data, and identify inefficient industrial land according to the industrial land efficiency index to obtain the inefficient industrial land identification results; Based on the social network analysis method, an enterprise association network is constructed, the network characteristic values of enterprise nodes are calculated, and key enterprises in key industrial links are identified from the industrial chain dimension. The land use types are divided according to the inefficient industrial land identified by the key enterprises, so as to optimize the transformation strategy according to the division results.

2. The method according to claim 1, characterized in that The remote sensing images include multispectral images and night light images; the enterprise information includes a variety of industrial and commercial information, annual business report information, investment information and patent information; the industrial land benefit indicators include land development benefit indicators, land use benefit indicators and land output benefit indicators.

3. The method according to claim 2, characterized in that Calculate land development benefit indicators, including: Based on remote sensing image data, bare land and construction site image samples are obtained, and the spatial distribution of unconstructed bare land is extracted through image supervised classification using the random forest algorithm. Collect building image samples and obtain the spatial distribution of built building outlines through semantic segmentation algorithms; The proportion of undeveloped bare land and building area in each plot unit is counted, and the idleness of the plot and the building density level are calculated based on the statistical results to obtain the land development benefit index.

4. The method according to claim 2, characterized in that Calculate land use efficiency indicators, including: The spatial distribution of surface temperature is obtained through the surface temperature inversion algorithm, and the distribution of nighttime activity clusters is obtained through nighttime light images; Analyze the spatial distribution of employed people based on mobile phone signaling data; The concentration of human activities on industrial land is calculated based on the spatial distribution of activity concentration and the employed population to obtain the land use efficiency index.

5. The method according to claim 2, characterized in that Calculate land output efficiency indicators, including: Based on the geographical coordinates of the enterprise, the enterprise is matched and associated with the land parcel to establish a land-enterprise information database, and the average land output value and average land tax are calculated based on the enterprise output value and enterprise tax situation; Based on the situation where there are several enterprises on the same land, the economic benefits of the land are calculated by superimposing the data of all enterprises; The economic output of industrial land is calculated based on the average output value per plot, average tax revenue per plot and economic benefits of the land to obtain the land output benefit index.

6. The method according to claim 5, characterized in that Identify inefficient industrial land based on industrial land efficiency indicators and obtain the following identification results: The industrial land benefit evaluation index is obtained by integrating the land development benefit index, land use benefit index and land output benefit index; The industrial land benefit evaluation index is used as a characteristic parameter for normalization, and the clustering algorithm is used to classify the normalized characteristic parameter results to obtain cluster characteristics. According to the cluster characteristics, the land clusters with cluster characteristic values lower than the preset threshold are judged as inefficient industrial land, and the inefficient industrial land identification results are obtained by dividing them according to the dimensions to which the low characteristic values belong; wherein, the inefficient industrial land identification results include development inefficiency type, use inefficiency type, output inefficiency type and comprehensive inefficiency type.

7. The method according to claim 1, characterized in that Construct enterprise association networks based on social network analysis, including: Based on enterprise investment data, each enterprise is regarded as a node in the network, the investment connection between enterprises is regarded as the edge of the network, and the investment scale between enterprises is regarded as the weight of the edge to construct an enterprise investment association network; Based on the upstream and downstream positions of the industry to which the enterprise belongs in the industrial chain, each industry is regarded as a network node, and the upstream and downstream relationships between industries are regarded as network edges to construct an industrial chain association network; Based on enterprise patent information data, each enterprise is regarded as a node of the network, the patent relationship between enterprises is regarded as the edge of the network, and the number of patent relationships is used as the weight of the edge to construct an enterprise patent association network.

8. The method according to claim 7, characterized in that Based on the social network analysis method, an enterprise association network is constructed, the network characteristic values of enterprise nodes are calculated, and key enterprises in key industrial links are identified from the perspective of the industrial chain. The inefficient industrial land identified by the key enterprises is then classified into land types, and the transformation strategy is optimized based on the classification results, including: Calculate the centrality of enterprise nodes in enterprise investment association networks and enterprise patent association networks; Calculate the hub degree of enterprise nodes in the enterprise investment association network, enterprise patent association network, and the hub degree of industry nodes in the industry chain association network; Based on the centrality and hubness of the enterprise investment association network, the enterprise patent association network, and the industry node hubness of the industrial chain association network, the structural characteristics of the association network of enterprises and their respective industries are comprehensively calculated to identify key enterprises in the industrial chain dimension based on the characteristic analysis results; The inefficient industrial land involved in the identification of key enterprises will be divided into inefficient industrial land for retention, improvement and upgrading, and the remaining inefficient industrial land will be divided into inefficient industrial land for vacancy and redevelopment, so as to optimize the renewal and transformation strategy according to the division results.

9. The method according to claim 8, characterized in that Calculate the centrality of enterprise nodes in the enterprise investment association network and enterprise patent association network. The calculation formula is as follows: Where C D (x) is the centrality of the network node x, there are V nodes in the network, z x,y is the weight of the edge between network node x and node y; The calculation formula for calculating the hub degree of enterprise nodes in the enterprise investment association network and enterprise patent association network, and the hub degree of industry nodes in the industry chain association network is as follows: Where C B (x) is the hub degree of the network node x. The path length between two nodes in the network is defined as the number of edges connecting the two nodes on the path. Let path w,y is the number of shortest paths between node w and node y, path w,y (x) is the number of shortest paths between node w and node y that pass through node x.

10. A low-efficiency industrial land transformation strategy optimization system based on industrial chain analysis, characterized by: include: A research data acquisition module is used to acquire relevant information data within the research area; wherein the relevant information data includes remote sensing image data, mobile phone signaling data and enterprise information; The low-efficiency land identification module is used to calculate the efficiency of the target industrial land based on the pre-processed relevant information data, and identify the low-efficiency industrial land according to the industrial land efficiency index to obtain the low-efficiency industrial land identification results; The transformation strategy optimization module is used to construct an enterprise association network based on the social network analysis method, calculate the network characteristic values of enterprise nodes, identify key enterprises in key industrial links from the industrial chain dimension, and classify the inefficient industrial land involved in the key enterprises into land types according to the identification results of the inefficient industrial land, so as to optimize the transformation strategy according to the classification results.

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