Abnormity identification and warning method based on mass health industry aggregation

Abstract

The invention provides an abnormity identification and warning method based on mass health industry gathering, and relates to the technical field of mass health industry gathering, and the method comprises the steps: presetting a mass health industry distribution planning table of a region; obtaining large health industry data distributed in a target area and an adjacent area of the region, and obtaining target data and adjacent data; obtaining large health industry data of the target area and the adjacent area from a large health industry distribution planning table, and obtaining target planning data and adjacent planning data; matching the target data with the target planning data to obtain a first record standing book; matching the adjacent data with the adjacent planning data to obtain a second record standing book; and matching the first recording machine account with the second recording machine account, and when the same index number between the first recording machine account and the second recording machine account is greater than a preset same index threshold value, generating an abnormal warning signal. And the abnormal condition of the target area is judged by analyzing the similarity of the target area and the adjacent area in the aspect of abnormal indexes.

Description

Technical Field

[0001] The present invention relates to the technical field of large health industry clusters, and in particular to an abnormality identification and warning method based on large health industry clusters. Background Art

[0002] Health industry agglomeration refers to the process of highly concentrated and coordinated development of the health industry within a specific geographic region. In the early stages of health industry agglomeration, when concentration has yet to reach a high level, this moderate concentration facilitates more efficient sharing of product-related information among enterprises within the industry, injecting momentum into the positive development of the agglomeration. Specifically, industrial agglomeration can generate economies of scale, promote the rational allocation of resources within a region, and promote the deepening of industrial specialization and division of labor. This division of labor and cooperation model helps optimize production processes, improve production efficiency, and reduce the costs of technology research and development and application, thereby achieving stable regional economic growth and bringing positive development benefits.

[0003] However, when the massive health industry reaches a certain stage of agglomeration, it faces a series of challenges. With the over-concentration of labor and information channels, land resources have become relatively scarce, and a crowding effect has emerged during industrial agglomeration. This crowding effect has led to rising prices of various factors within the region, an imbalance in the internal industrial structure, and thus hindered further improvements in production efficiency, adversely affecting the sustainable development of the regional economy. Due to differences in the development foundations, resources, and policy environments of the massive health industry in different regions, the abnormal manifestations of industrial agglomeration also vary. Developing technical methods that can account for regional differences, achieve personalized anomaly identification, and improve the pertinence and effectiveness of anomaly identification is a key issue that needs to be addressed. Summary of the Invention

[0004] The purpose of the present invention is to provide an abnormality identification and warning method based on the aggregation of the big health industry. The technical problem to be solved is how to eliminate regional differences and realize personalized abnormality identification.

[0005] The present invention is achieved through the following technical solutions:

[0006] The abnormality identification and warning method based on the aggregation of the big health industry includes the following steps:

[0007] Obtaining a big health industry distribution planning table, wherein the big health industry distribution planning table includes big health industry data; wherein the big health industry data includes a number of monitoring indicators and corresponding monitoring indicator values;

[0008] Obtain the big health industry data distributed in the target area and adjacent areas of the above-mentioned region to obtain target data and adjacent data;

[0009] Obtain the big health industry data of the target area and adjacent areas from the above big health industry distribution planning table to obtain target planning data and adjacent planning data;

[0010] Matching the target data with the target planning data, when a monitoring indicator value of a monitoring indicator in the target data exceeds a corresponding monitoring indicator value in the target planning data, recording the monitoring indicator in the first ledger; after traversing each monitoring indicator of the target data, obtaining a first record ledger;

[0011] Matching the above-mentioned neighboring data with the neighboring planning data, when the monitoring indicator value of a monitoring indicator in the above-mentioned neighboring data exceeds the corresponding monitoring indicator value in the neighboring planning data, recording the monitoring indicator in the second ledger; after traversing each monitoring indicator of the above-mentioned neighboring data, obtaining a second record ledger;

[0012] The first record ledger and the second record ledger are matched, and when the number of identical indicators between the first record ledger and the second record ledger is greater than a preset identical indicator threshold, an abnormal warning signal is generated.

[0013] There is a certain correlation between adjacent regions in terms of geographical location, economic environment, industrial ecology, etc.; by analyzing the similarity of anomaly indicators between the target area and the adjacent areas, it is possible to determine whether the anomaly in the target area is affected by common factors between regions, rather than simply attributing it to the problems of the target area itself. This helps to more comprehensively and objectively understand the impact of regional differences on anomaly identification, and to explore possible common development problems or trends between regions, indirectly assisting in eliminating identification biases caused by regional differences; matching target data with target planning data, and adjacent data with adjacent planning data, using each region's own planning data as the standard for anomaly judgment; the planning data of each region is formulated based on the actual situation and development goals of the region, so using these planning data as a benchmark for anomaly identification can fully consider the region's personalized development needs and goals, making the anomaly identification results more in line with the region's own development logic, and realizing personalized anomaly identification.

[0014] Furthermore, the target area and the adjacent area are determined as follows:

[0015] Divide the above regions to obtain a set of regions to be analyzed consisting of several regions to be analyzed;

[0016] After receiving the recognition request, the area to be analyzed in the recognition request is obtained, the obtained area to be analyzed is used as the target area, and the areas to be analyzed adjacent to the target area are used as adjacent areas.

[0017] This approach breaks down a larger region into multiple relatively independent yet interconnected regions for analysis. Adjacent regions are geographically close and have strong correlations in economic activity, industrial ecology, and other aspects. This allows the data and conditions of neighboring regions to serve as important references for anomaly identification in the target region, reducing bias caused by isolated regional analysis and more comprehensively considering the impact of regional differences on anomaly identification. The development status of adjacent regions influences each other, and anomalies in the target region are driven or constrained by the development trends of neighboring regions, and vice versa. By incorporating neighboring regions into the analysis, the inter-regional influence is comprehensively considered, avoiding a singular focus on the target region's own data while ignoring the impact of regional differences caused by external factors. This allows for a more accurate assessment of whether anomalies in the target region are part of normal inter-regional differences or due to unique reasons. This helps eliminate misjudgments of differences caused by ignoring inter-regional influences. Allowing users to specify target regions ensures that anomaly identification is focused on specific areas of interest, meeting the personalized analysis needs of different users and making anomaly identification results more targeted and practical.

[0018] Furthermore, the various monitoring indicators of the above-mentioned big health industry data constitute a monitoring indicator set; the above-mentioned monitoring indicator set includes population inflow index, population outflow index, start-up enterprise registration index, enterprise bankruptcy index, price fluctuation index and available land resource change index.

[0019] Furthermore, each monitoring indicator in the above-mentioned big health industry distribution planning table has a preset threshold range; specifically including: population inflow index threshold range, population outflow index threshold range, start-up enterprise registration index threshold range, enterprise bankruptcy index threshold range, price fluctuation index threshold range and available land resource change index threshold range.

[0020] Furthermore, the monitoring indicator values ​​in the above target data and adjacent data are real values; specifically including: population inflow index value, population outflow index value, start-up enterprise registration index value, enterprise bankruptcy index value, price fluctuation index value and available land resource change index value.

[0021] Reflecting the development environment and trends of the big health industry from different dimensions. Different regions naturally differ in these aspects. Comprehensive consideration can avoid the one-sidedness brought about by comparing a single indicator, provide a more comprehensive and objective assessment of the development status of each region, and eliminate the misjudgment of regional differences caused by a single indicator. Preset threshold ranges, rather than fixed values, are set for each monitoring indicator, fully accounting for regional differences. Due to different resource endowments and policy orientations, the reasonable fluctuation range of each indicator varies across regions. Threshold ranges can be flexibly set based on regional characteristics, making anomaly identification standards more tailored to regional realities and avoiding the disparity caused by regional characteristics that is ignored due to unified standards. Using the threshold range as a benchmark, the actual monitoring indicator values ​​of the target area and neighboring areas are compared with them to identify deviations from the normal range in industrial development in each region. Each region sets a threshold range based on its own planning and development goals. When the actual value exceeds this range, it is judged as an anomaly, achieving personalized anomaly identification.

[0022] Furthermore, the target data is matched with the target planning data. When the monitoring indicator value of a monitoring indicator in the target data exceeds the corresponding monitoring indicator value in the target planning data, the monitoring indicator is recorded in the first ledger. The specific steps include:

[0023] Compare the population inflow index value of the target area with the population inflow index threshold range. When the population inflow index value exceeds the population inflow index threshold range, record the population inflow index in the first ledger.

[0024] Compare the population outflow index value of the target area with the population outflow index threshold range; when the population outflow index value exceeds the population outflow index threshold range, record the population outflow index in the first ledger;

[0025] Compare the startup registration index value of the target area with the startup registration index threshold range. When the startup registration index value exceeds the startup registration index threshold range, record the startup registration index in the first ledger;

[0026] Compare the enterprise failure index value of the target area with the enterprise failure index threshold range; when the enterprise failure index value exceeds the enterprise failure index threshold range, record the enterprise failure index in the first ledger;

[0027] Comparing the price fluctuation index value of the target area with the price fluctuation index threshold range, and when the price fluctuation index value exceeds the price fluctuation index threshold range, recording the price fluctuation index in the first ledger;

[0028] Compare the available land resources change index value of the above-mentioned target area with the available land resources change index threshold range. When the above-mentioned available land resources change index value exceeds the available land resources change index threshold range, the available land resources change index will be recorded in the first ledger.

[0029] Furthermore, the neighboring data is matched with the neighboring planning data. When the monitoring indicator value of a monitoring indicator in the neighboring data exceeds the corresponding monitoring indicator value in the target planning data, the monitoring indicator is recorded in the second ledger. The specific steps include:

[0030] Compare the population inflow index value of the adjacent area with the population inflow index threshold range. When the population inflow index value exceeds the population inflow index threshold range, record the population inflow index in the second ledger.

[0031] Compare the population outflow index value of the target area with the population outflow index threshold range; when the population outflow index value exceeds the population outflow index threshold range, record the population outflow index in the second ledger;

[0032] Compare the startup registration index value of the target area with the startup registration index threshold range. When the startup registration index value exceeds the startup registration index threshold range, record the startup registration index in the second ledger;

[0033] Compare the enterprise failure index value of the target area with the enterprise failure index threshold range; when the enterprise failure index value exceeds the enterprise failure index threshold range, record the enterprise failure index in the second ledger;

[0034] Comparing the price fluctuation index value of the target area with the price fluctuation index threshold range, and when the price fluctuation index value exceeds the price fluctuation index threshold range, recording the price fluctuation index in the second ledger;

[0035] Compare the available land resources change index value of the above-mentioned target area with the available land resources change index threshold range. When the above-mentioned available land resources change index value exceeds the available land resources change index threshold range, the available land resources change index will be recorded in the second ledger.

[0036] Each monitoring indicator is compared with the corresponding threshold interval in the target planning data, taking into account the differences in the development of different indicators in different regions.

[0037] Furthermore, the first record ledger and the second record ledger are matched, and the specific steps include:

[0038] a. Use the second record ledger as the matching ledger;

[0039] b. Match the above matching ledger with the first record ledger. The specific steps are as follows:

[0040] c. Extract a monitoring indicator from the first record ledger, and compare the extracted monitoring indicator with the monitoring indicators in the matching ledger one by one. When the monitoring indicators are consistent, the comparison of the monitoring indicators is completed, and the number of identical indicators is increased by 1; wherein, the initial value of the number of identical indicators is 0;

[0041] d. Traverse the monitoring indicators in the first record ledger, complete the matching of the matching ledger with the first record ledger, and obtain the number of indicators that are the same between the matching ledger and the first record ledger.

[0042] e. When the number of the above-mentioned identical indicators is greater than the preset identical indicator threshold, an abnormal warning signal is generated.

[0043] The reason why the neighboring areas are used as matching ledgers to match with the target area ledger is that the neighboring areas are related in terms of geography, economy, industrial ecology, etc. This connection may lead to similarities or mutual influences in abnormal situations between regions. Through matching analysis, it is possible to determine whether the abnormalities in the target area are affected by common factors between regions, avoid misjudging normal fluctuations caused by regional associations as abnormalities, and reduce the risk of misjudgment caused by regional differences; by counting the number of identical indicators in the first record ledger and the second record ledger, the commonalities in abnormal indicators between the target area and the neighboring areas can be accurately identified. Different regions have different industrial characteristics and development priorities, and abnormal situations should have personalized characteristics. However, if there are many identical abnormal indicators, it may indicate the existence of common regional problems, which will help to deeply explore the potential risks in the personalized development of the region and achieve more accurate abnormality identification.

[0044] Furthermore, if there are multiple second record ledgers, the second record ledgers that have completed matching are marked;

[0045] Select any second record ledger from the unmarked second record ledgers as a matching ledger, and return to step b to continue executing until all second record ledgers are traversed.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] There is a certain correlation between adjacent regions in terms of geographical location, economic environment, industrial ecology, etc.; by analyzing the similarity of anomaly indicators between the target area and the adjacent areas, it is possible to determine whether the anomaly in the target area is affected by common factors between regions, rather than simply attributing it to the problems of the target area itself. This helps to more comprehensively and objectively understand the impact of regional differences on anomaly identification, and to explore possible common development problems or trends between regions, indirectly assisting in eliminating identification biases caused by regional differences; matching target data with target planning data, and adjacent data with adjacent planning data, using each region's own planning data as the standard for anomaly judgment; the planning data of each region is formulated based on the actual situation and development goals of the region, so using these planning data as a benchmark for anomaly identification can fully consider the region's personalized development needs and goals, making the anomaly identification results more in line with the region's own development logic, and realizing personalized anomaly identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0049] Figure 1 Main flow chart. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0051] First embodiment:

[0052] Combine Figure 1 ,The abnormal identification and warning method based on the cluster of the big health industry includes the following steps:

[0053] Preset the big health industry data distributed in a region to obtain a big health industry distribution planning table; wherein the big health industry data includes several monitoring indicators and corresponding monitoring indicator values;

[0054] Obtain the big health industry data distributed in the target area and adjacent areas of the above-mentioned region to obtain target data and adjacent data;

[0055] Obtain the big health industry data of the target area and adjacent areas from the above big health industry distribution planning table to obtain target planning data and adjacent planning data;

[0056] Matching the target data with the target planning data, when a monitoring indicator value of a monitoring indicator in the target data exceeds a corresponding monitoring indicator value in the target planning data, recording the monitoring indicator in the first ledger; after traversing each monitoring indicator of the target data, obtaining a first record ledger;

[0057] Matching the above-mentioned neighboring data with the neighboring planning data, when the monitoring indicator value of a monitoring indicator in the above-mentioned neighboring data exceeds the corresponding monitoring indicator value in the neighboring planning data, recording the monitoring indicator in the second ledger; after traversing each monitoring indicator of the above-mentioned neighboring data, obtaining a second record ledger;

[0058] The first record ledger and the second record ledger are matched, and when the number of identical indicators between the first record ledger and the second record ledger is greater than a preset identical indicator threshold, an abnormal warning signal is generated.

[0059] There is a certain correlation between adjacent regions in terms of geographical location, economic environment, industrial ecology, etc.; by analyzing the similarity of anomaly indicators between the target area and the adjacent areas, it is possible to determine whether the anomaly in the target area is affected by common factors between regions, rather than simply attributing it to the problems of the target area itself. This helps to more comprehensively and objectively understand the impact of regional differences on anomaly identification, and to explore possible common development problems or trends between regions, indirectly assisting in eliminating identification biases caused by regional differences; matching target data with target planning data, and adjacent data with adjacent planning data, using each region's own planning data as the standard for anomaly judgment; the planning data of each region is formulated based on the actual situation and development goals of the region, so using these planning data as a benchmark for anomaly identification can fully consider the region's personalized development needs and goals, making the anomaly identification results more in line with the region's own development logic, and realizing personalized anomaly identification.

[0060] In a specific embodiment, the target area and the adjacent area are determined as follows:

[0061] Divide the above regions to obtain a set of regions to be analyzed consisting of several regions to be analyzed;

[0062] After receiving the recognition request, the area to be analyzed in the recognition request is obtained, the obtained area to be analyzed is used as the target area, and the areas to be analyzed adjacent to the target area are used as adjacent areas.

[0063] This approach breaks down a larger region into multiple relatively independent yet interconnected regions for analysis. Adjacent regions are geographically close and have strong correlations in economic activity, industrial ecology, and other aspects. This allows the data and conditions of neighboring regions to serve as important references for anomaly identification in the target region, reducing bias caused by isolated regional analysis and more comprehensively considering the impact of regional differences on anomaly identification. The development status of adjacent regions influences each other, and anomalies in the target region are driven or constrained by the development trends of neighboring regions, and vice versa. By incorporating neighboring regions into the analysis, the inter-regional influence is comprehensively considered, avoiding a singular focus on the target region's own data while ignoring the impact of regional differences caused by external factors. This allows for a more accurate assessment of whether anomalies in the target region are part of normal inter-regional differences or due to unique reasons. This helps eliminate misjudgments of differences caused by ignoring inter-regional influences. Allowing users to specify target regions ensures that anomaly identification is focused on specific areas of interest, meeting the personalized analysis needs of different users and making anomaly identification results more targeted and practical.

[0064] A reference usage scenario assumes that the target area is a health industry park in a certain city, A, surrounded by three neighboring industrial parks, B, C, and D. Each park records health industry-related data and forms a record ledger. After comparing the data, Park A records that the population inflow index and startup registration index exceed the threshold range in the first record ledger. Parks B, C, and D also generate second record ledgers to record abnormal indicators. First, Park B's second record ledger is compared with Park A's first record ledger. It is found that Park A's population inflow index is abnormal, and Park B's enterprise closure index is abnormal, with the number of identical indicators being 0. Then, Park C's second record ledger is compared with Park A's first record ledger. It is found that Park A's population inflow index and startup registration index are abnormal, and Park C also has these two abnormalities, with the number of identical indicators being 2. Then, Park D's second record ledger is compared with Park A's first record ledger. It is found that Park A's population inflow index and Park D's population inflow index are abnormal, with the number of identical indicators being 1. If the preset identical indicator threshold is 2, then because the number of identical indicators 2 reaches the threshold when matching Park C, an abnormal warning signal is generated.

[0065] Second embodiment:

[0066] Based on the first embodiment, the various monitoring indicators of the above-mentioned big health industry data constitute a monitoring indicator set; the above-mentioned monitoring indicator set includes a population inflow index, a population outflow index, a start-up enterprise registration index, a business closure index, a price fluctuation index, and an available land resource change index.

[0067] In a specific embodiment, each monitoring indicator in the above-mentioned big health industry distribution planning table has a preset threshold interval; specifically including: population inflow index threshold interval, population outflow index threshold interval, start-up enterprise registration index threshold interval, enterprise bankruptcy index threshold interval, price fluctuation index threshold interval and available land resource change index threshold interval.

[0068] In a specific embodiment, the monitoring indicator values ​​in the above-mentioned target data and neighboring data are real values; specifically including: population inflow index value, population outflow index value, start-up enterprise registration index value, enterprise bankruptcy index value, price fluctuation index value and available land resource change index value.

[0069] Reflecting the development environment and trends of the big health industry from different dimensions. Different regions naturally differ in these aspects. Comprehensive consideration can avoid the one-sidedness brought about by comparing a single indicator, provide a more comprehensive and objective assessment of the development status of each region, and eliminate the misjudgment of regional differences caused by a single indicator. Preset threshold ranges, rather than fixed values, are set for each monitoring indicator, fully accounting for regional differences. Due to different resource endowments and policy orientations, the reasonable fluctuation range of each indicator varies across regions. Threshold ranges can be flexibly set based on regional characteristics, making anomaly identification standards more tailored to regional realities and avoiding the disparity caused by regional characteristics that is ignored due to unified standards. Using the threshold range as a benchmark, the actual monitoring indicator values ​​of the target area and neighboring areas are compared with them to identify deviations from the normal range in industrial development in each region. Each region sets a threshold range based on its own planning and development goals. When the actual value exceeds this range, it is judged as an anomaly, achieving personalized anomaly identification.

[0070] Third embodiment:

[0071] Based on any of the above embodiments, the target data is matched with the target planning data. When the monitoring indicator value of a monitoring indicator in the target data exceeds the corresponding monitoring indicator value in the target planning data, the monitoring indicator is recorded in the first ledger. The specific steps include:

[0072] Compare the population inflow index value of the target area with the population inflow index threshold range. When the population inflow index value exceeds the population inflow index threshold range, record the population inflow index in the first ledger.

[0073] Compare the population outflow index value of the target area with the population outflow index threshold range; when the population outflow index value exceeds the population outflow index threshold range, record the population outflow index in the first ledger;

[0074] Compare the startup registration index value of the target area with the startup registration index threshold range. When the startup registration index value exceeds the startup registration index threshold range, record the startup registration index in the first ledger;

[0075] Compare the enterprise failure index value of the target area with the enterprise failure index threshold range; when the enterprise failure index value exceeds the enterprise failure index threshold range, record the enterprise failure index in the first ledger;

[0076] Comparing the price fluctuation index value of the target area with the price fluctuation index threshold range, and when the price fluctuation index value exceeds the price fluctuation index threshold range, recording the price fluctuation index in the first ledger;

[0077] Compare the available land resources change index value of the above-mentioned target area with the available land resources change index threshold range. When the above-mentioned available land resources change index value exceeds the available land resources change index threshold range, the available land resources change index will be recorded in the first ledger.

[0078] In a specific embodiment, the above-mentioned neighboring data is matched with the neighboring planning data. When the monitoring indicator value of a monitoring indicator in the above-mentioned neighboring data exceeds the corresponding monitoring indicator value in the target planning data, the monitoring indicator is recorded in the second ledger. The specific steps include:

[0079] Compare the population inflow index value of the adjacent area with the population inflow index threshold range. When the population inflow index value exceeds the population inflow index threshold range, record the population inflow index in the second ledger.

[0080] Compare the population outflow index value of the target area with the population outflow index threshold range; when the population outflow index value exceeds the population outflow index threshold range, record the population outflow index in the second ledger;

[0081] Compare the startup registration index value of the target area with the startup registration index threshold range. When the startup registration index value exceeds the startup registration index threshold range, record the startup registration index in the second ledger;

[0082] Compare the enterprise failure index value of the target area with the enterprise failure index threshold range; when the enterprise failure index value exceeds the enterprise failure index threshold range, record the enterprise failure index in the second ledger;

[0083] Comparing the price fluctuation index value of the target area with the price fluctuation index threshold range, and when the price fluctuation index value exceeds the price fluctuation index threshold range, recording the price fluctuation index in the second ledger;

[0084] Compare the available land resources change index value of the above-mentioned target area with the available land resources change index threshold range. When the above-mentioned available land resources change index value exceeds the available land resources change index threshold range, the available land resources change index will be recorded in the second ledger.

[0085] Each monitoring indicator is compared with the corresponding threshold interval in the target planning data, taking into account the differences in the development of different indicators in different regions.

[0086] Fourth embodiment:

[0087] Based on the third embodiment, the first record ledger and the second record ledger are matched. The specific steps include:

[0088] a. Use the second record ledger as the matching ledger;

[0089] b. Match the above matching ledger with the first record ledger. The specific steps are as follows:

[0090] c. Extract a monitoring indicator from the first record ledger, and compare the extracted monitoring indicator with the monitoring indicators in the matching ledger one by one. When the monitoring indicators are consistent, the comparison of the monitoring indicators is completed, and the number of identical indicators is increased by 1; wherein, the initial value of the number of identical indicators is 0;

[0091] d. Traverse the monitoring indicators in the first record ledger, complete the matching of the matching ledger with the first record ledger, and obtain the number of indicators that are the same between the matching ledger and the first record ledger.

[0092] e. When the number of the above-mentioned identical indicators is greater than the preset identical indicator threshold, an abnormal warning signal is generated.

[0093] The reason why the neighboring areas are used as matching ledgers to match with the target area ledger is that the neighboring areas are related in terms of geography, economy, industrial ecology, etc. This connection may lead to similarities or mutual influences in abnormal situations between regions. Through matching analysis, it is possible to determine whether the abnormalities in the target area are affected by common factors between regions, avoid misjudging normal fluctuations caused by regional associations as abnormalities, and reduce the risk of misjudgment caused by regional differences; by counting the number of identical indicators in the first record ledger and the second record ledger, the commonalities in abnormal indicators between the target area and the neighboring areas can be accurately identified. Different regions have different industrial characteristics and development priorities, and abnormal situations should have personalized characteristics. However, if there are many identical abnormal indicators, it may indicate the existence of common regional problems, which will help to deeply explore the potential risks in the personalized development of the region and achieve more accurate abnormality identification.

[0094] In a specific embodiment, there are multiple second record ledgers, and the second record ledgers that have completed matching are marked;

[0095] Select any one of the unmarked second record ledgers as the matching ledger, and return to step b to continue executing until all second record ledgers are traversed; when any of the above identical indicators is greater than the preset identical indicator threshold, an abnormal warning signal is generated.

[0096] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The abnormality identification and warning method based on the aggregation of the big health industry is characterized by: The following steps are involved: Obtaining a big health industry distribution planning table, wherein the big health industry distribution planning table includes big health industry data; wherein the big health industry data includes a number of monitoring indicators and corresponding monitoring indicator values; Obtaining big health industry data distributed in the target area and adjacent areas of the region to obtain target data and adjacent data; Obtaining the big health industry data of the target area and the adjacent areas from the big health industry distribution planning table to obtain target planning data and adjacent planning data; Matching the target data with the target planning data, and when a monitoring indicator value of a monitoring indicator in the target data exceeds a corresponding monitoring indicator value in the target planning data, recording the monitoring indicator in the first ledger; after traversing each monitoring indicator of the target data, obtaining a first record ledger; Matching the neighboring data with the neighboring planning data, and when a monitoring indicator value of a monitoring indicator in the neighboring data exceeds a corresponding monitoring indicator value in the neighboring planning data, recording the monitoring indicator in a second ledger; after traversing each monitoring indicator of the neighboring data, obtaining a second record ledger; The first record ledger is matched with the second record ledger, and when the number of identical indicators between the first record ledger and the second record ledger is greater than a preset identical indicator threshold, an abnormal warning signal is generated.

2. The abnormality identification and warning method according to claim 1, characterized in that: The target area and the adjacent area are determined as follows: Dividing the region to obtain a region set to be analyzed consisting of a plurality of regions to be analyzed; After receiving the identification request, the area to be analyzed in the identification request is obtained, the obtained area to be analyzed is used as the target area, and the areas to be analyzed adjacent to the target area are used as adjacent areas.

3. The abnormality identification and warning method according to claim 1, characterized in that: The various monitoring indicators of the big health industry data constitute a monitoring indicator set; the monitoring indicator set includes a population inflow index, a population outflow index, a start-up enterprise registration index, a business closure index, a price fluctuation index, and an available land resource change index.

4. The abnormality identification and warning method according to claim 3, characterized in that: Each monitoring indicator in the big health industry distribution planning table has a preset threshold range, specifically including: population inflow index threshold range, population outflow index threshold range, start-up enterprise registration index threshold range, enterprise bankruptcy index threshold range, price fluctuation index threshold range and available land resource change index threshold range.

5. The abnormality identification and warning method according to claim 4, characterized in that: The monitoring indicator values ​​in the target data and adjacent data are real values, including: population inflow index value, population outflow index value, start-up enterprise registration index value, enterprise bankruptcy index value, price fluctuation index value and available land resource change index value.

6. The abnormality identification and warning method according to claim 5, characterized in that: The target data is matched with the target planning data. When a monitoring indicator value of a monitoring indicator in the target data exceeds a corresponding monitoring indicator value in the target planning data, the monitoring indicator is recorded in the first ledger. The specific steps include: Comparing the population inflow index value of the target area with the population inflow index threshold range, and when the population inflow index value exceeds the population inflow index threshold range, recording the population inflow index in the first ledger; Comparing the population outflow index value of the target area with the population outflow index threshold interval, and when the population outflow index value exceeds the population outflow index threshold interval, recording the population outflow index in the first ledger; comparing the startup registration index value of the target area with the startup registration index threshold range, and when the startup registration index value exceeds the startup registration index threshold range, recording the startup registration index in the first ledger; Comparing the enterprise failure index value of the target area with the enterprise failure index threshold range, and when the enterprise failure index value exceeds the enterprise failure index threshold range, recording the enterprise failure index in the first ledger; Comparing the price fluctuation index value of the target area with a price fluctuation index threshold range, and when the price fluctuation index value exceeds the price fluctuation index threshold range, recording the price fluctuation index in the first ledger; The available land resource change index value of the target area is compared with the available land resource change index threshold range. When the available land resource change index value exceeds the available land resource change index threshold range, the available land resource change index is recorded in the first ledger.

7. The abnormality identification and warning method according to claim 5, characterized in that: Matching the adjacent data with the adjacent planning data, when a monitoring indicator value of a monitoring indicator in the adjacent data exceeds a corresponding monitoring indicator value in the target planning data, recording the monitoring indicator in the second ledger, specifically comprising: comparing the population inflow index value of the adjacent area with a population inflow index threshold interval, and when the population inflow index value exceeds the population inflow index threshold interval, recording the population inflow index in the second ledger; Comparing the population outflow index value of the target area with the population outflow index threshold interval, and when the population outflow index value exceeds the population outflow index threshold interval, recording the population outflow index in the second ledger; comparing the startup registration index value of the target area with the startup registration index threshold range, and when the startup registration index value exceeds the startup registration index threshold range, recording the startup registration index in the second ledger; Comparing the enterprise failure index value of the target area with the enterprise failure index threshold range, and when the enterprise failure index value exceeds the enterprise failure index threshold range, recording the enterprise failure index in the second ledger; comparing the price fluctuation index value of the target area with a price fluctuation index threshold range, and when the price fluctuation index value exceeds the price fluctuation index threshold range, recording the price fluctuation index in a second ledger; The available land resource change index value of the target area is compared with the available land resource change index threshold range. When the available land resource change index value exceeds the available land resource change index threshold range, the available land resource change index is recorded in the second ledger.

8. The abnormality identification and warning method according to claim 1, characterized in that: Matching the first record ledger with the second record ledger, specifically comprising: a. Using the second record ledger as a matching ledger; b. The matching ledger is matched with the first record ledger. The specific steps are as follows: c. Extract a monitoring indicator from the first record ledger, and compare the extracted monitoring indicator with the monitoring indicators in the matching ledger one by one. When the monitoring indicators are consistent, the comparison of the monitoring indicators is completed, and the number of identical indicators is increased by 1; wherein, the initial value of the number of identical indicators is 0; d. Traverse the monitoring indicators in the first record ledger, complete the matching of the matching ledger with the first record ledger, and obtain the number of indicators that are the same between the matching ledger and the first record ledger. 9.e. When the number of identical indicators is greater than a preset identical indicator threshold, an abnormal warning signal is generated.

10. The abnormality identification and warning method according to claim 8, characterized in that: There are multiple second record ledgers, and the second record ledgers that have completed matching are marked; Select any second record ledger from the unmarked second record ledgers as a matching ledger, and return to step b to continue executing until all second record ledgers are traversed.

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