Regional risk assessment method and device based on regional index, equipment and medium

Abstract

The invention relates to a regional risk assessment method and device based on regional indexes, equipment and a medium. The regional risk assessment method based on the regional index comprises the following steps: collecting and obtaining risk data and basic data of each region under different dimensions; according to the basic data and the risk data, respectively calculating various risk indexes corresponding to different dimensions; calculating an average level and a standard deviation of an overall region under different dimensions according to the various risk indexes, wherein the overall region comprises various regions; and determining the weak risk field corresponding to each region based on the average level and the standard deviation. According to the embodiment of the invention, the risk condition of each region can be accurately and comprehensively reflected, and the weak risk field can be accurately positioned.

Description

Technical Field

[0001] The present disclosure relates to the technical field of regional risk assessment, and particularly to a regional risk assessment method, device, equipment and medium based on regional indices. Background Art

[0002] In the prior art, there has been certain exploration and practice for regional risk assessment. The known technologies mainly focus on risk considerations in a single dimension. For example, they only focus on the statistics and analysis of contradictions and disputes themselves, or simply conduct inspections and assessments on the risks of places, and fail to comprehensively and systematically judge the weak areas of regions by integrating multiple key dimensions such as contradictions and disputes, place risks, and personnel risks.

[0003] Regional risk is a complex system intertwined with multiple factors. Evaluating from only one aspect cannot accurately and comprehensively reflect the actual risk situation. The deficiencies of this single - dimension assessment method are as follows: (1) Only considering the number of contradictions and disputes while ignoring the existence of place risks may lead to an underestimation of the risks in some concentrated areas of special high - risk places; (2) Only focusing on place risks without combining personnel risks makes it difficult to judge the scale of social impact and the difficulty of response that the risks may cause. Summary of the Invention

[0004] To solve the above - mentioned technical problems, the present disclosure provides a regional risk assessment method, device, equipment and medium based on regional indices.

[0005] In a first aspect, the present disclosure provides a regional risk assessment method based on regional indices, including:

[0006] Collect and obtain risk data and basic data for each region under different dimensions;

[0007] According to the basic data and the risk data, calculate various risk indices corresponding to different dimensions respectively;

[0008] Calculate the average level and standard deviation of different dimensions of the overall region, where the overall region includes each region;

[0009] Based on the average level and the standard deviation, determine the weak risk areas corresponding to each region.

[0010] In a second aspect, the present disclosure provides a regional risk assessment device based on regional indices, including:

[0011] A data acquisition module for collecting and obtaining risk data and basic data for each region under different dimensions;

[0012] A first calculation module for calculating various risk indices corresponding to different dimensions respectively according to the basic data and the risk data;

[0013] A second calculation module, configured to calculate the average level and standard deviation of different dimensions of the overall region according to the various risk indexes, where the overall region includes each region;

[0014] A risk determination module, configured to determine the weak risk areas corresponding to each region based on the average level and the standard deviation.

[0015] In a third aspect, the present disclosure provides a regional risk assessment device based on a regional index, including:

[0016] A processor;

[0017] A memory, configured to store executable instructions;

[0018] Wherein, the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the regional risk assessment method based on the regional index in the first aspect.

[0019] In a fourth aspect, the present disclosure provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the processor is caused to implement the regional risk assessment method based on the regional index in the first aspect.

[0020] The technical solutions provided by the embodiments of the present disclosure have the following advantages compared with the prior art:

[0021] The regional risk assessment method, device, equipment and medium based on the regional index in the embodiments of the present disclosure can collect and obtain the risk data and basic data of different dimensions of each region, and then calculate various risk indexes corresponding to different dimensions according to the basic data and the risk data, and then calculate the average level and standard deviation of different dimensions of the overall region according to the various risk indexes, where the overall region includes each region, and finally determine the weak risk areas corresponding to each region based on the average level and the standard deviation. Thus, through comprehensive analysis of multi-dimensional data and accurate calculation of various risk indexes of each region, the risk situation of each region can be accurately and comprehensively reflected, and the weak risk areas can be accurately located. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the original elements and elements are not necessarily drawn to scale.

[0023] Figure 1 It is a schematic flowchart of a regional risk assessment method based on a regional index provided by an embodiment of the present disclosure;

[0024] Figure 2 Flow diagram of a method for collecting personnel risk data provided by an embodiment of the present disclosure;

[0025] Figure 3 Flow diagram of a method for collecting venue risk data provided by an embodiment of the present disclosure;

[0026] Figure 4 Flow diagram of a method for collecting dispute risk data provided by an embodiment of the present disclosure;

[0027] Figure 5 Flow diagram of a method for collecting fire risk data provided by an embodiment of the present disclosure;

[0028] Figure 6 Flow diagram of a method for collecting work safety risk data provided by an embodiment of the present disclosure;

[0029] Figure 7 Flow diagram of a method for collecting basic data provided by an embodiment of the present disclosure;

[0030] Figure 8 Structural diagram of a regional risk assessment device based on regional index provided by an embodiment of the present disclosure;

[0031] Figure 9 Structural diagram of a regional risk assessment device based on regional index provided by an embodiment of the present disclosure. Detailed implementation manners

[0032] Embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0033] It should be understood that the various steps recorded in the method embodiments of the present disclosure can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0034] As used herein, the term "including" and its variations are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0035] It should be noted that the concepts such as "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0036] It should be noted that the modifications of "one" and "a plurality of" mentioned in this disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0037] The names of the messages or information exchanged between multiple devices in the embodiments of this disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0038] To solve the above problems, the embodiments of this disclosure provide a regional risk assessment method, device, equipment and medium based on a regional index. The following combines Figures 1 to 7 A detailed description is given to the regional risk assessment method based on the regional index provided by the embodiments of this disclosure.

[0039] Figure 1 The flowchart of a regional risk assessment method based on a regional index provided by the embodiments of this disclosure is shown.

[0040] In the embodiments of this disclosure, the regional risk assessment method based on the regional index can be executed by an electronic device. Among them, the electronic device can include but is not limited to devices such as computer devices, cloud servers or cloud server clusters.

[0041] As Figure 1 shown, the regional risk assessment method based on the regional index can include the following steps.

[0042] S110. Collect and obtain risk data and basic data in different dimensions for each region.

[0043] In the embodiments of this disclosure, the electronic device can collect and obtain risk data and basic data in different dimensions for each region.

[0044] Optionally, each region can represent multiple county-level regions in the whole city, or multiple regional areas divided within a designated area, etc., which is not limited here.

[0045] Optionally, the risk data may be data related to the risk area in different dimensions.

[0046] Optionally, the basic data may be unique resource data for each region. For example, the basic data may include population data, police force allocation data, grid personnel data, professional rescue personnel data, etc., which are not limited herein.

[0047] Specifically, the electronic device may collect risk data and basic data in different dimensions for each region (such as each county-level region).

[0048] S120. Calculate various risk indexes corresponding to different dimensions according to the basic data and the risk data.

[0049] In an embodiment of the present disclosure, the electronic device may calculate various risk indexes corresponding to different dimensions according to the basic data and the risk data.

[0050] Optionally, various risk indexes may be index values obtained by performing quantitative calculation on risk data in different dimensions.

[0051] Specifically, after obtaining the basic data and the risk data, the electronic device calculates various risk indexes for different dimensions of the risk data according to a preset quantitative calculation method.

[0052] S130. Calculate the average level and standard deviation of different dimensions of the overall region according to the various risk indexes, where the overall region includes each region.

[0053] In an embodiment of the present disclosure, the electronic device may calculate the average level and standard deviation of different dimensions of the overall region according to the various risk indexes, where the overall region includes each region.

[0054] Optionally, the overall region includes each region. When each region is a plurality of county-level regions, the overall region is the whole city region; when each region is a plurality of regional regions, the overall region is a designated region including a plurality of regional regions.

[0055] Optionally, the average level may reflect the average risk degree of the overall region.

[0056] Specifically, after obtaining the various risk indexes, the electronic device may calculate the average level and standard deviation of different dimensions of the overall region according to the various risk indexes, such as calculating the average level and standard deviation of the whole city region according to the various risk indexes of each county-level region.

[0057] S140. Determine the weak risk areas corresponding to each region based on the average level and the standard deviation.

[0058] In the embodiments of the present disclosure, the electronic device may determine the weak risk areas corresponding to each region based on the average level and the standard deviation.

[0059] Optionally, the weak risk area may be a risk area that needs to be focused on for management.

[0060] Specifically, the electronic device may judge through various risk indexes of each region based on the average level and the standard deviation, so as to determine the weak risk areas corresponding to each region.

[0061] Through multi-dimensional data collection, covering multiple key areas such as personnel risk, venue risk, dispute risk, fire risk, and work safety risk, and combined with district and county-level basic data for comprehensive analysis and calculation, the one-sidedness of single-dimensional evaluation is avoided. It accurately reflects the actual situation of each county-level region at different risk levels.

[0062] Thus, it is possible to collect and obtain risk data and basic data of each region under different dimensions. Then, according to the basic data and the risk data, calculate various risk indexes corresponding to different dimensions respectively. Then, calculate the average level and standard deviation of different dimensions of the overall region based on the various risk indexes. The overall region includes each region. Finally, based on the average level and the standard deviation, determine the weak risk areas corresponding to each region. Thus, through comprehensive analysis of multi-dimensional data and accurate calculation of various risk indexes of each region, the risk situation of each region can be accurately and comprehensively reflected, and the weak risk areas can be accurately located.

[0063] Optionally, the risk data under different dimensions includes personnel risk data, venue risk data, dispute risk data, fire risk data, and work safety risk data.

[0064] In some embodiments, the electronic device may collect and obtain the personnel risk data of each region.

[0065] Figure 2 The flowchart of a method for collecting personnel risk data provided by the embodiments of the present disclosure is shown.

[0066] As Figure 2 shown, the electronic device may obtain the high-risk population data of each county from multiple channels such as the public management system and the community management system, including but not limited to information of special populations such as persons with a criminal record, drug-related persons, and patients with mental disorders; classify, sort, statistically analyze the collected data, and conduct sub-statistics according to dimensions such as risk level and residential area; collect the population flow data of each district and county, especially the inflow volume, outflow volume, flow frequency of the floating population, and the basic information of the floating population, so as to analyze the potential impact of population flow on regional risk.

[0067] In some embodiments, the electronic device can collect and obtain the venue risk data of each region.

[0068] Figure 3 The flowchart shows a method for collecting venue risk data provided by an embodiment of the present disclosure.

[0069] As Figure 3 shown, the electronic device can conduct a comprehensive investigation and registration of public security venues in each district and county, including entertainment venues (such as bars, KTVs, etc.), hotels, Internet cafes, etc., and record basic information such as their business scale, business hours, and personnel flow; focus on identifying high-risk public security venues, such as venues where public security incidents have occurred, venues with fire hazards, etc., and detailed record relevant event information, hazard types and degrees.

[0070] In some embodiments, the electronic device can collect and obtain the dispute risk data of each region.

[0071] Figure 4 The flowchart shows a method for collecting dispute risk data provided by an embodiment of the present disclosure.

[0072] As Figure 4 shown, the electronic device can integrate dispute data from courts, judicial mediation agencies, petition departments, etc., covering various case information such as civil disputes, administrative disputes, criminal disputes, etc., and count the number, type (such as contract disputes, neighborhood disputes, etc.), and number of people involved in the disputes; establish a time series of dispute data and analyze the development trend of disputes in each district and county, including the monthly and quarterly changes in the number of disputes and the trend of dispute types.

[0073] In some embodiments, the electronic device can collect and obtain the fire risk data of each region.

[0074] Figure 5 The flowchart shows a method for collecting fire risk data provided by an embodiment of the present disclosure.

[0075] As Figure 5 shown, the electronic device can collect the distribution data of fire protection facilities in each district and county, including the number and location of fire hydrants, the configuration of fire extinguishers, etc.; count the fire inspection records of each venue, including whether there are problems such as blocked fire exits and expired fire protection equipment, and the rectification of related problems.

[0076] In some embodiments, the electronic device can collect and obtain the work safety risk data of each region.

[0077] Figure 6 The flowchart shows a method for collecting work safety risk data provided by an embodiment of the present disclosure.

[0078] As Figure 6As shown, the electronic device can collect information such as work safety permit information, historical records of safety accidents, and safety inspection reports for key areas such as industrial enterprises and construction sites; and count the number, scale, and implementation of safety management measures of hazardous chemical production, storage, and transportation enterprises in each district and county.

[0079] In some embodiments, the electronic device can collect and obtain basic data for each region.

[0080] Figure 7 The flowchart of a basic data collection method provided by an embodiment of the present disclosure is shown.

[0081] As Figure 7 As shown, the electronic device can obtain actual population data from census data and household registration systems, including the number of permanent residents and household registered population, as well as population structure information such as age, gender, and occupation; collect police force allocation data for each district and county, such as the number of police officers, police types distribution, and the ratio of police force to population; count grid personnel data, including the number of grid personnel in each department and their division of responsibilities; and count professional rescue personnel data, including the number of rescue personnel of various types and their skills.

[0082] Optionally, S120 may specifically include: calculating the total proportion of risk data and the total proportion of correction indexes corresponding to different dimensions based on the basic data and the risk data. Calculating various risk indexes according to the total proportion of risk data and the total proportion of correction indexes through a preset risk index calculation formula.

[0083] In an embodiment of the present disclosure, the electronic device can calculate the total proportion of risk data and the total proportion of correction indexes corresponding to different dimensions based on the basic data and the risk data.

[0084] Optionally, the total proportion of risk data may be the proportion of risk data in each region to the overall region.

[0085] Optionally, the total proportion of correction indexes may be the proportion of correction indexes in each region to the overall region.

[0086] Specifically, the electronic device can calculate the total proportion of risk data and the total proportion of correction indexes corresponding to different dimensions based on the basic data and the risk data.

[0087] In some embodiments, based on the basic data, calculate the risk values for different dimensions in each region and the total sum of risk values corresponding to the overall region; calculate the total proportion of risk data according to the risk values and the total sum of risk values.

[0088] In the embodiments of the present disclosure, the electronic device may calculate the risk values in different dimensions for each region and the total sum of the risk values corresponding to the overall region based on the basic data.

[0089] For example, taking the risk data as the personnel risk data, the electronic device may first calculate the personnel risk values for each region according to the basic data. Specifically, the number of high-risk people in the current district or county is H (the high-risk population here can be statistically calculated according to different risk classifications and weights, such as people with a criminal record, drug-related people, etc., and then summarized after assigning corresponding weights); the quantified value of the population flow risk index is F (which can be quantified according to certain rules through factors such as the inflow volume, outflow volume, and flow frequency of the population. For example, the larger the inflow volume and the more frequent the flow, the higher this value); using the weighted summation method, if the weight of the number of high-risk people is set as ω1 and the weight of the population flow risk index is ω2, the corresponding calculation formula is:

[0090] P = ω1 * H + ω2 * F.

[0091] Wherein, P is the personnel risk value, H is the number of high-risk people, and F is the quantified value of the population flow risk index.

[0092] Next, calculate the total sum of the personnel risk values corresponding to the overall region. Specifically, according to the same quantification method, calculate the personnel risk values of all districts and counties in the city respectively (assuming there are n districts and counties in the city, and the personnel risk value of the i-th district and county is Pi), and then sum them up:

[0093]

[0094] Wherein, PA is the total sum of the personnel risk values, and Pi is the personnel risk value of the i-th district and county among the n districts and counties in the city.

[0095] Furthermore, calculate the total proportion of the personnel risk data based on the personnel risk value and the total sum of the personnel risk values. Specifically, divide the personnel risk value of the current district or county by the total sum of the personnel risk values of all districts and counties in the city to obtain the proportion of the personnel risk value in the whole city. The calculation formula is as follows:

[0096] R = P / PA.

[0097] Wherein, R is the total proportion of the personnel risk data, P is the personnel risk value, and PA is the total sum of the personnel risk values.

[0098] In some other embodiments, based on the risk data, calculate the correction index for each region and the total sum of the correction indexes corresponding to the overall region respectively; calculate the total proportion of the correction index based on the correction index and the total sum of the correction indexes.

[0099] In the embodiments of the present disclosure, the electronic device may calculate the correction index for each region and the total sum of the correction indexes corresponding to the overall region based on the risk data.

[0100] For example, taking the personnel risk data as an example of the risk data, the electronic device calculates the correction index for each region according to the personnel risk data. Specifically, calculate the index D related to population density. Assume that the actual population quantity of this district or county is P, and the territorial area of this district or county is S. Then the calculation formula for population density is: D = P / S. Calculate the index C of population structure complexity. The age ratio (the ratio of 0 - 6 years old is A1, the ratio of 7 - 12 years old is A2, the ratio of 13 - 17 years old is A3, the ratio of 18 - 45 years old is A4, the ratio of 46 - 69 years old is A5, and the ratio of over 69 years old is A6), and the gender structure (the ratio of males is Gn, and the ratio of females is Gw). The structure complexity is reflected by weighted average as shown in Table 1 below:

[0101] Table 1

[0102] Ratio type A1 A2 A3 A4 A5 A6 Gn Weight ωA1 ωA2 ωA3 ωA4 ωA5 ωA6 ωG

[0103] The corresponding calculation formula for the population structure complexity index C is: C = ωA1×A1 + ωA2×A2 + ωA3×A3 + ωA4×A4 + ωA5×A5 + ωA6×A6 + ωG×(Gn - Gw).

[0104] Next, calculate the police force data, grid personnel data, and professional rescue personnel data. Police force data (L): Measured by the number of police officers per 10,000 people. Assume that the number of police officers in this district or county is N 警察 , then the calculation formula for the number of police officers per 10,000 people is: L = N 警察 / (P * 10000).

[0105] Grid personnel data (G): Measured by the proportion of grid personnel involved in social management. Assume that the number of grid civil servants involved in social management in this district or county is N 网格 , then its proportion calculation formula is: G = N 网格 / P. Professional rescue personnel data (J): For example, reflected by the proportion of professional personnel involved in emergency rescue. Assume that the number of professional personnel involved in emergency rescue in this district or county is N 救援 , then the corresponding proportion calculation formula is: J = N 救援 / P. Determine the corresponding weights as shown in Table 2 below:

[0106] Table 2

[0107] Population density Complexity of population structure Police force data Grid personnel data Professional rescue personnel data Value D C L G J Weight ωD ωC ωL ωG ωJ

[0108] The calculation formula for the correction index M of each corresponding region 区县 is: M 区县= ωD×D + ωC×C + ωL×L + ωG×G + ωJ×J。

[0109] Next, calculate the correction indices of all n districts and counties in the whole city (the correction index of the i-th district and county is Mi) in sequence according to the same calculation method, and then sum them up:

[0110]

[0111] Among them, M 人员总和 is the total sum of the correction indices corresponding to the overall region.

[0112] Furthermore, the electronic device can calculate the total proportion of the correction index according to the correction index and the total sum of the correction indices. Specifically, divide the correction index of this district and county by the total sum of the correction indices of all districts and counties in the whole city to obtain the proportion of the correction index in the whole city. The corresponding calculation formula is:

[0113] R 修正 = M 区县 / M 人员总和 .

[0114] Among them, M 人员总和 is the total sum of the correction indices corresponding to the overall region, M 区县 is the correction index of each region, and R 修正 is the total proportion of the correction index.

[0115] Furthermore, the electronic device can calculate various risk indices according to the total proportion of the risk data and the total proportion of the correction index through a preset risk index calculation formula.

[0116] Specifically, the electronic device first calculates the average value of the personnel risk numbers in the previous 12 months, denoted as AvgP. Then, it is obtained by averaging the personnel risk values in the past 12 months of this district and county. Assume that the personnel risk values for each month in the past 12 months are P1, P2....P12 respectively. Then the calculation formula is:

[0117] AvgP = (P1 + P2 + P3 +... + P12) / 12.

[0118] Next, the preset risk index calculation formula (such as the preset personnel risk index calculation formula) is:

[0119] I 人员风险 = (R / R 修正 ) / (P / AvgP).

[0120] Among them, I 人员风险 is the personnel risk index, R 修正 is the total proportion of the correction index, R is the total proportion of the personnel risk data, P is the personnel risk value, and AvgP is the average value of the personnel risk numbers in the previous 12 months.

[0121] In some embodiments, taking the venue risk data as an example of the risk data, the electronic device may first calculate the venue risk values of each region according to the basic data. Specifically, the security venue data of the current district or county is quantified to determine the venue risk value. Different scores are set according to the venue types (assuming different types of venues are T1 (entertainment venue), T2 (hotel), T3 (Internet cafe), and the corresponding type weights are ωT1, ωT2, ωT3), and then the venue risk value is comprehensively calculated by means of weighted summation or the like. An example of the calculation formula is as follows:

[0122] Q 本区县 = ωT1×T1 + ωT2×T2 + ωT3×T3.

[0123] Where Q 本区县 is the venue risk value.

[0124] Next, calculate the sum Q 总和 of the venue risk values corresponding to the overall region. Specifically, according to the same quantification method, the venue risk values of all n districts or counties in the whole city (the venue risk value of the i-th district or county is Qi) are calculated respectively, and then summed up. The corresponding formula is:

[0125]

[0126] Furthermore, according to the venue risk value Q 本区县 and the sum Q 总和 of the venue risk values, calculate the total proportion R 场所占比 of the venue risk data. The corresponding formula is:

[0127] R 场所占比 = Q 本区县 / Q 总和 .

[0128] Furthermore, the electronic device calculates the correction index of each region and the sum of the correction indexes corresponding to the overall region.

[0129] For example, calculate the index D related to population density. Assuming the actual population number of the current district or county is P and the regional area of the current district or county is S, the calculation formula for population density is: D = P / S. Calculate the police force data, grid personnel data, and professional rescue personnel data. Police force data (L): Measured by the number of police officers per ten thousand people. Assuming the number of police officers in the current district or county is N 警察 , then the calculation formula for the number of police officers per ten thousand people is: L = N 警察 / (P * 10000). Grid personnel data (G): If measured by the proportion of grid personnel involved in social management, assuming the number of grid public servants involved in social management in the current district or county is N 网格 , then its proportion calculation formula is: G = N网格 / P. Professional rescuer data (J): For example, it is reflected by the proportion of professional rescuers involved in emergency rescue. Let the number of professional rescuers involved in emergency rescue in this district or county be N 救援 , then the corresponding proportion calculation formula is: J = N 救援 / P.

[0130] Continuing to refer to Table 2, the correction index M for each corresponding region 区县 The calculation formula is: M 区县 = ωD×D + ωC×C + ωL×L + ωG×G + ωJ×J.

[0131] Next, calculate according to the same calculation method, calculate the correction indexes of all n districts or counties in the whole city in turn (the correction index of the i-th district or county is Mi), and then sum them up:

[0132]

[0133] Among them, M 场所总和 is the total sum of the correction indexes corresponding to the overall region.

[0134] Furthermore, the electronic device can calculate the total proportion of the correction index according to the correction index and the total sum of the correction indexes. Specifically, divide the correction index of this district or county by the total sum of the correction indexes of all districts or counties in the whole city to obtain the proportion of the correction index in the whole city. The corresponding calculation formula is:

[0135] R 修正 = M 区县 / M 场所总和 .

[0136] Among them, M 场所总和 is the total sum of the correction indexes corresponding to the overall region, M is the correction index of each region in the district or county, and R 修正 is the total proportion of the correction index.

[0137] Furthermore, the electronic device can calculate various risk indexes according to the preset risk index calculation formula, based on the total proportion of the risk data and the total proportion of the correction index.

[0138] Specifically, the electronic device first calculates the average value of the venue risk numbers in the previous 12 months, denoted as AvgQ. Then, it is obtained by averaging the venue risk values in the past 12 months of this district or county. Assuming that the venue risk values for each month in the past 12 months are Q1, Q2....Q12 respectively, then its calculation formula is:

[0139] AvgQ = (Q1 + Q2 + Q3 +... + Q12) / 12.

[0140] Next, the preset risk index calculation formula (such as the preset venue risk index calculation formula) is:

[0141] I 场所风险 = (R 场所占比 / R correction) / (Q 本区县 / AvgQ).

[0142] Wherein, I 场所风险 is the site risk index, R 修正 is the total proportion of the correction index, R 场所占比 is the total proportion of the site risk data, Q 本区县 is the site risk value, and AvgQ is the average value of the site risk numbers in the previous 12 months.

[0143] In some embodiments, taking the dispute risk data as an example of the risk data, the electronic device can first calculate the dispute risk values of each region according to the basic data. Specifically, to count the dispute risk value of this district or county, it is necessary to set weights for quantification according to factors such as the dispute type (assuming different dispute types are T1 (neighborhood disputes), T2 (marriage disputes), T3 (labor disputes), and the corresponding type weights are ωT1, ωT2, ωT3), the number of people involved (assuming the quantified value of the number of people involved is N, and the weight is ωN), and the severity of the dispute (assuming the quantified value of the severity of the dispute is S, and the weight is ωs). The dispute risk value F 本区县 is calculated through weighted summation and other methods, and the formula example is as follows: F 本区县 = ωT1 × T1 + ωT2 × T2 + ωT3 × T3 + N × ωN + ωs × S.

[0144] Then, according to the same quantification method, calculate the dispute risk values of all n districts or counties in the whole city (the site risk value of the i-th district or county is Fi), and then sum them up to obtain the total sum of the dispute risk values F 总和 :

[0145]

[0146] Furthermore, divide the dispute risk value of this district or county by the total sum of the dispute risk values of the whole city to obtain the proportion of the dispute risk value in the total sum of the dispute risk values of the whole city, that is, the total proportion of the dispute risk data R 纠纷占比 , and the calculation formula is as follows:

[0147] R 纠纷占比 = F 本区县 / F 总和 .

[0148] Furthermore, calculate the population quantity P population. The population data can reflect the influence of the population quantity on the dispute base. The more the population, the relatively greater the possibility of disputes occurring, and the larger the dispute base. The actual population quantity of this district or county is P; measured by the proportion of grid personnel involved in social management, assuming the number of grid personnel involved in social management in this district or county is N网格 Then its ratio calculation formula is: G = N 网格 / P, where G is the grid personnel data, and the corresponding weights are shown in Table 3 below:

[0149] Table 3

[0150] Population quantity Grid personnel data Value P G Weight ωP ωG

[0151] The corresponding correction index M 区县 The calculation formula is: M 区县 = ωP × P + ωG × G.

[0152] Then, according to the same calculation method, calculate the correction indexes of all n districts and counties in the whole city (the correction index of the i-th district and county is Mi) in turn, and then sum them up to get the total correction index M 纠纷总和 :

[0153]

[0154] Furthermore, divide the correction index of this district and county by the total sum of the correction indexes of all districts and counties in the whole city to obtain the proportion of the correction index in the whole city. The corresponding calculation formula is:

[0155] R 修正 = M 区县 / M 纠纷总和 .

[0156] Among them, M 纠纷总和 is the total sum of the correction indexes corresponding to the overall region, M 区县 is the correction index of each region, and R 修正 is the total proportion of the correction index.

[0157] Furthermore, the electronic device can calculate various risk indexes according to the preset risk index calculation formula based on the total proportion of the risk data and the total proportion of the correction index.

[0158] Specifically, the electronic device first calculates the average value of the dispute risk numbers in the previous 12 months, denoted as AvgF. Then, it is obtained by averaging the dispute risks in the past 12 months of this district and county. Assuming that the dispute risk values in each of the past 12 months are F1, F2....F12, then its calculation formula is:

[0159] AvgF = (F1 + F2 + F3 +... + F12) / 12.

[0160] Then, the preset risk index calculation formula (such as the preset dispute risk index calculation formula) is:

[0161] I 纠纷风险 = (R 纠纷占比 / R 修正 ) / (F本区县 / (AvgF).

[0162] Among them, I 场所风险 is the place risk index, R 修正 is the total proportion of the correction index, R 场所占比 is the total proportion of the dispute risk data, F 本区县 is the dispute risk value, and AvgF is the average value of the dispute risk numbers in the previous 12 months.

[0163] In some embodiments, taking the fire risk data as an example of the risk data, the electronic device can first calculate the fire risk values of each region according to the basic data. Specifically, based on the fire facility data including the number of fire hydrants, the number of fire extinguishers equipped and the intact rate, etc., and the severity of the fire inspection problem data including the blockage of the fire passage, the expiration of the fire equipment, the number of faults, etc., the fire risk is quantified. After setting certain quantification rules for the fire facility data, the quantified value is F (the higher the number of sufficient fire hydrants and the intact rate, the higher the F value, and vice versa), and after quantification, the fire inspection problem data is P (the more serious the problem, the lower the P value). Then, set the corresponding weights (let the weight of the fire facility data be ωF and the weight of the fire inspection problem data be ωP), and comprehensively calculate the fire risk value of this district or county through weighted summation and other methods. An example of the calculation formula is as follows:

[0164] X 本区县 = ωF × F + ωP × P.

[0165] Among them, X 本区县 is the fire risk value.

[0166] Next, according to the same quantification method, calculate the fire risk values of all n districts or counties in the whole city (the fire risk value of the i-th district or county is Xi), and then sum them up to obtain the total fire risk value X 总和 :

[0167] Furthermore, according to the fire risk value X 本区县 and the total fire risk value X 总和 , calculate the total proportion R 消防占比 of the fire risk data, and the corresponding formula is:

[0168] R 消防占比 = Q 本区县 / Q 总和 .

[0169] Furthermore, the electronic device calculates the correction index of each region and the total sum of the correction indexes corresponding to the whole region.

[0170] For example, to calculate the index D related to population density, assuming the actual population of this district or county is P and the territorial area of this district or county is S, the calculation formula for population density is: D = P / S. The impact of grid personnel data on fire risks is measured by the proportion of grid personnel involved in work related to fire policy publicity and implementation, etc. Let the number of grid personnel involved in social management in this district or county be N 网格 , then its proportion calculation formula is: G = N 网格 / P. The corresponding correction index M 区县 The calculation formula is: M 区县 = ωD×D + ωG×G.

[0171] Then, according to the same calculation method, calculate the correction indexes of all n districts or counties in the whole city (the correction index of the i-th district or county is Mi) in turn, and then sum them up to obtain the total correction index M 消防总和 :

[0172] Furthermore, divide the correction index of this district or county by the total sum of the correction indexes of all districts or counties in the whole city to obtain the proportion of the correction index in the whole city. The corresponding calculation formula is:

[0173] R 修正 = M 区县 / M 消防总和 .

[0174] Among them, M 消防总和 is the total sum of the correction indexes corresponding to the whole region, M 区县 is the correction index of each region, and R 修正 is the total proportion of the correction index.

[0175] Furthermore, the electronic device can calculate various risk indexes according to the preset risk index calculation formula based on the total proportion of the risk data and the total proportion of the correction index.

[0176] Specifically, the electronic device first calculates the average value of the fire risk numbers in the previous 12 months, denoted as AvgX. Then, it is obtained by averaging the fire risks in this district or county in the past 12 months. Assuming that the dispute risk values for each of the past 12 months are X1, X2....X12, then its calculation formula is:

[0177] AvgX = (X1 + X2 + X3 +... + X12) / 12.

[0178] Then, the preset risk index calculation formula (such as the preset fire risk index calculation formula) is:

[0179] I 消防风险 = (R 消防占比 / R 修正 ) / (X 本区县 / AvgX).

[0180] Among them, I 消防风险 is the fire risk index, R 修正 is the total proportion of the correction index, R 场所占比 is the total proportion of the fire risk data, X 本区县 is the fire risk value, and AvgX is the average value of the fire risk numbers in the previous 12 months.

[0181] In some embodiments, taking the production safety risk data as an example of the risk data, the electronic device can first calculate the dispute risk values of each region according to the basic data. Specifically, for the production safety license information, quantization rules can be set according to factors such as the compliance situation of the enterprise in obtaining the license and the coverage degree of the license scope to obtain a quantization value (denoted as L. If the enterprise's license is complete and the compliance degree is high, the L value is relatively high, otherwise it is low). The number of accidents (assuming the number of accidents in a certain past time period is N), the severity of the accident (assuming the quantization value of the accident severity is S, which can be graded and quantified according to factors such as the number of casualties and economic losses. The more serious the accident, the higher the S value). Assuming the weight of the production safety license information is ωL, the weights of the safety accident data are ωN and ωS, etc., and the production safety risk value A of this district and county is comprehensively calculated by means of weighted summation and other methods. The formula example is as follows: A of this district and county = ωL×L + ωN×N + ωS×S.

[0182] Then, according to the same quantization method, the production safety risk values of all n districts and counties in the city (the site risk value of the i-th district and county is Ai) are calculated respectively, and then summed up to obtain the total production safety risk value A total:

[0183]

[0184] Furthermore, divide the production safety risk value of this district and county by the total production safety risk value of all districts and counties in the city to obtain the proportion of the production safety risk value in the total production safety risk value of the city, that is, the total proportion of the production safety risk data R 安全占比 , and the calculation formula is as follows:

[0185] R safety proportion = A of this district and county / A total.

[0186] Furthermore, there is a correlation between the number and scale of industrial enterprises and population data. In densely populated areas, the number of industrial enterprises is often relatively large, with varying scales, which can affect work safety risks. By quantifying the number and scale factors of industrial enterprises, the total number of industrial enterprises in this district or county (denoted as n) and the average output value of each enterprise (denoted as v) are statistically calculated, and the scale situation is comprehensively reflected by the following formula: E = n × v; the inspection intensity is measured by factors such as the frequency, coverage, and the situation of discovering and urging the rectification of potential hazards of grid personnel conducting work safety inspections on industrial enterprises within the jurisdiction. Statistically calculate the average number of enterprises inspected by grid personnel per month (denoted as f), the proportion of the number of potential hazards discovered and successfully urged to be rectified in the total number of potential hazards to be rectified (denoted as r), etc. By setting weights (the weight of inspection frequency is ωf, and the weight of rectification proportion is ωr), the quantified value of the inspection intensity is comprehensively calculated, and the calculation formula is: P = ωf × f + ωr × r. The corresponding weights are shown in Table 4 below:

[0187] Table 4

[0188] Number and scale of industrial enterprises Intensity of grid personnel's safety production inspection Value E P Weight ωE ωP

[0189] The calculation formula for the corresponding correction index M of the district or county is: M district or county = ωE × E + ωP × P.

[0190] Then, according to the same calculation method, the correction indices of all n districts or counties in the city are calculated in turn (the correction index of the i-th district or county is Mi), and then summed to obtain the total correction index M 安全总和 :

[0191]

[0192] Furthermore, divide the correction index of this district or county by the total sum of the correction indices of all districts or counties in the city to obtain the proportion of the correction index in the city. The corresponding calculation formula is:

[0193] R 修正 = M 区县 / M 安全总和 .

[0194] Among them, M total disputes is the total sum of the correction indices corresponding to the overall region, M district or county is the correction index of each region, and R correction is the total proportion of the correction index.

[0195] Furthermore, the electronic device can calculate various risk indices according to the preset risk index calculation formula based on the total proportion of the risk data and the total proportion of the correction index.

[0196] Specifically, the electronic device first calculates the average value of the safety risk numbers in the previous 12 months, denoted as AvgA. Then, it is obtained by averaging the safety risks in the past 12 months in this district or county. Assuming that the safety risk values for each month in the past 12 months are A1, A2....A12 respectively, then its calculation formula is:

[0197] AvgA = (A1 + A2 + A3 +... + A12) / 12.

[0198] Next, the preset risk index calculation formula (such as the preset safety risk index calculation formula) is:

[0199] I safety risk = (R safety proportion / R correction) / (A this district or county / AvgF).

[0200] Among them, I place risk is the safety risk index, R correction is the total proportion of the correction index, R place proportion is the total proportion of the safety risk data, A this district or county is the safety risk value, and AvgA is the average value of the safety risk numbers in the previous 12 months.

[0201] Through multi-dimensional data collection, covering multiple key areas such as personnel risk, place risk, dispute risk, fire risk, and work safety risk, and combined with district or county-level basic data for comprehensive analysis and calculation, the one-sidedness of single-dimensional assessment is avoided. This comprehensive and systematic assessment method can accurately reflect the actual situation of each district or county at different risk levels, providing a solid foundation for accurately positioning weak areas, helping the district or county government to comprehensively understand the overall picture of regional risks, and formulating more perfect and targeted risk prevention and control strategies.

[0202] Based on a scientific and reasonable index calculation method, comprehensively considering factors such as the city-wide proportion of the index value and the city-wide proportion of the correction index, as well as the regional field value and the field average value in the previous 12 months, it can accurately quantify various risk indexes of each district or county. Thus, accurately identifying the weak area risks of each district or county makes the subsequent risk prevention and control guidance highly targeted. For example, for districts or counties with high personnel risks, measures specifically targeting the control of high-risk populations and population flow management can be implemented; for districts or counties with prominent place risks, key efforts can be made to carry out rectification and supervision of public security places, effectively improving the utilization efficiency of risk prevention and control resources and reducing the possibility of risks occurring and the potential harm they may cause.

[0203] During the data collection process, emphasis is placed on the accumulation and utilization of time series data, such as calculating the average value of the field in the previous 12 months. This enables this solution to not only reflect the current regional risk status but also gain insights into the development trend of risks through the analysis of historical data. Meanwhile, different data processing algorithms can be adopted or more influencing factors can be incorporated in the index calculation link to adjust and correct the index, which has good dynamic adaptability and can timely adjust the evaluation model and prevention and control strategies according to the actual situation of different regions and social development changes, preventing the accumulation and outbreak of potential risks in advance and providing strong guarantee for the long-term stable development of districts and counties.

[0204] Optionally, the regional risk assessment method based on the regional index may further include: sorting and comparing various risk indexes in different dimensions of each region to obtain the corresponding risk field sorting results.

[0205] In the embodiments of the present disclosure, the electronic device can sort and compare various risk indexes in different dimensions of each region to obtain the corresponding risk field sorting results.

[0206] Optionally, S140 may specifically include: for various risk indexes in different dimensions, based on the average level, the standard deviation, and a preset deviation multiple, determining the target risk indexes exceeding the threshold in each region; and determining the corresponding weak risk fields in each region based on the target risk indexes.

[0207] In the embodiments of the present disclosure, the electronic device can, for various risk indexes in different dimensions, based on the average level, the standard deviation, and a preset deviation multiple, determine the target risk indexes exceeding the threshold in each region.

[0208] Specifically, the electronic device can calculate the average level, and the corresponding formula is as follows:

[0209]

[0210] Among them, AvgIj represents the city-wide average level of the j-th risk type, that is, after summing up the indexes of the j-th risk of all n districts and counties and then dividing by the total number of districts and counties n.

[0211] Next, for personnel risk (j = 1), the city-wide average level AvgI1 of the personnel risk index can be calculated according to the above formula; for venue risk (j = 2), the city-wide average level AvgI2 of the venue risk index can be calculated according to the above formula; for dispute risk (j = 3), the city-wide average level AvgI3 of the dispute risk index can be calculated according to the above formula; for fire risk (j = 4), the city-wide average level AvgI4 of the fire risk index can be calculated according to the above formula; for work safety risk (j = 5), the city-wide average level AvgI5 of the work safety risk index can be calculated according to the above formula.

[0212] Further, the electronic device can calculate the standard deviation, which is used to measure the degree of dispersion of the risk indices of each district or county relative to the average level. The calculation formula is as follows:

[0213]

[0214] Among them, σj represents the standard deviation of the j-th type of risk. Specifically, when calculating, first calculate the square of the difference between the index of each district or county for this type of risk and the average level of the whole city, accumulate these squared values and then divide by n, and finally take the square root.

[0215] Next, for personnel risk (j = 1), the standard deviation σ1 of the personnel risk index can be calculated according to the above formula; for venue risk (j = 2), the standard deviation σ2 of the venue risk index can be calculated according to the above formula; for dispute risk (j = 3), the standard deviation σ3 of the dispute risk index can be calculated according to the above formula; for fire risk (j = 4), the standard deviation σ4 of the fire risk index can be calculated according to the above formula; for work safety risk (j = 5), the standard deviation σ5 of the work safety risk index can be calculated according to the above formula.

[0216] Further, the electronic device determines a preset deviation multiple. For example, according to the empirical value, the preset deviation multiple m can be set to 1.5, and it can be adjusted according to the actual situation. The larger the m value, the larger the deviation value.

[0217] Specifically, the electronic device can determine the target risk indices exceeding the threshold in each region based on the average level, the standard deviation, and the preset deviation multiple. For example, for the i-th district or county and the j-th type of risk, if it satisfies: Iij > AvgIj + m × σj, that is, when the index of a certain district or county for a certain type of risk is higher than the average level of the whole city plus m times the standard deviation of this type of risk, it is determined that this district or county belongs to the risk of a weak area in this type of risk (field).

[0218] Figure 8 Fig. shows a schematic structural diagram of a regional risk assessment device based on regional indices provided by an embodiment of the present disclosure.

[0219] As Figure 8 shown, the regional risk assessment device 800 based on regional indices may include a data acquisition module 810, a first calculation module 820, a second calculation module 830, and a risk determination module 840.

[0220] The data acquisition module 810 can be used to collect and obtain risk data and basic data of each region in different dimensions.

[0221] The first calculation module 820 can be used to calculate various risk indices corresponding to different dimensions respectively according to the basic data and the risk data.

[0222] The second calculation module 830 can be used to calculate the average level and standard deviation in different dimensions of the overall region according to the various risk indexes, where the overall region includes each region.

[0223] The risk determination module 840 can be used to determine the weak risk areas corresponding to each region based on the average level and the standard deviation.

[0224] Thus, in the embodiments of the present disclosure, it is possible to collect and obtain the risk data and basic data in different dimensions of each region, then calculate various risk indexes corresponding to different dimensions according to the basic data and the risk data, and then calculate the average level and standard deviation in different dimensions of the overall region according to the various risk indexes, where the overall region includes each region, and finally determine the weak risk areas corresponding to each region based on the average level and the standard deviation. Thus, through comprehensive analysis of multi-dimensional data and accurate calculation of various risk indexes of each region, the risk situation of each region can be accurately and comprehensively reflected, and the weak risk areas can be accurately positioned.

[0225] In some embodiments of the present disclosure, the risk data in different dimensions includes personnel risk data, venue risk data, dispute risk data, fire risk data, and work safety risk data.

[0226] In some embodiments of the present disclosure, the first calculation module 820 may specifically include a first calculation unit and a second calculation unit.

[0227] The first calculation unit can be used to calculate the total proportion of risk data and the total proportion of correction indexes corresponding to different dimensions based on the basic data and the risk data.

[0228] The second calculation unit can be used to calculate various risk indexes according to the total proportion of risk data and the total proportion of correction indexes through a preset risk index calculation formula.

[0229] In some embodiments of the present disclosure, the first calculation module 820 may also specifically include a third calculation unit and a fourth calculation unit.

[0230] The third calculation unit can be used to calculate the risk values in different dimensions of each region and the total sum of risk values corresponding to the overall region based on the basic data.

[0231] The fourth calculation unit can be used to calculate the total proportion of the risk data according to the risk values and the total sum of risk values.

[0232] In some embodiments of the present disclosure, the first calculation module 820 may also specifically include a fifth calculation unit and a sixth calculation unit.

[0233] The fifth calculation unit can be used to calculate the correction index for each region and the total sum of the correction indices corresponding to the overall region respectively based on the risk data.

[0234] The sixth calculation unit can be used to calculate the total proportion of the correction index according to the correction index and the total sum of the correction indices.

[0235] In some embodiments of the present disclosure, the regional risk assessment device 800 based on the regional index may include a sorting and comparison module.

[0236] The sorting and comparison module can be used to sort and compare various risk indices in different dimensions for each region to obtain the corresponding risk area sorting result.

[0237] In some embodiments of the present disclosure, the risk determination module 840 may specifically include a first determination unit and a second determination unit.

[0238] The first determination unit can be used to determine the target risk indices exceeding the threshold in each region for various risk indices in different dimensions based on the average level, the standard deviation, and a preset deviation multiple.

[0239] The second determination unit can be used to determine the corresponding weak risk areas for each region based on the target risk indices.

[0240] It should be noted that Figure 8 the shown regional risk assessment device 800 based on the regional index can execute Figures 1 to 7 each step in the method embodiments shown and achieve Figures 1 to 7 each process and effect in the method embodiments shown, which will not be elaborated here.

[0241] Figure 9 The structural schematic diagram of a regional risk assessment device based on the regional index provided by the embodiments of the present disclosure is shown.

[0242] In some embodiments of the present disclosure, Figure 9 the shown regional risk assessment device based on the regional index can be an electronic device. Specifically, the electronic device may include, but is not limited to, devices such as computer devices, cloud servers, or cloud server clusters.

[0243] As Figure 9 shown, the regional risk assessment device based on the regional index may include a processor 901 and a memory 902 storing computer program instructions.

[0244] Specifically, the above-mentioned processor 901 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present application.

[0245] The memory 902 may include a mass memory for information or instructions. By way of example and not limitation, the memory 902 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 902 may include removable or non-removable (or fixed) media. In a suitable case, the memory 902 may be internal or external to the integrated gateway device. In a specific embodiment, the memory 902 is a non-volatile solid-state memory. In a specific embodiment, the memory 902 includes a read-only memory (ROM). In a suitable case, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0246] The processor 901 reads and executes the computer program instructions stored in the memory 902 to perform the steps of the method for region risk assessment based on region index provided by the embodiments of the present disclosure.

[0247] In one example, the region risk assessment device based on region index may further include a transceiver 903 and a bus 904. Among them, as Figure 9 shown, the processor 901, the memory 902, and the transceiver 903 are connected through the bus 904 and complete communication with each other.

[0248] The bus 904 includes hardware, software, or both. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side BUS (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, the bus 904 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0249] Embodiments of the present disclosure also provide a computer-readable storage medium that may store a computer program, which, when executed by a processor, causes the processor to implement the region risk assessment method based on a region index provided by the embodiments of the present disclosure.

[0250] The above storage medium may include, for example, a memory 902 storing computer program instructions, and the above instructions may be executed by a processor 901 of a region risk assessment device based on a region index to complete the region risk assessment method based on a region index provided by the embodiments of the present disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be a ROM, a Random Access Memory (RAM), a Compact Disc ROM (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0251] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0252] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A regional risk assessment method based on regional index, characterized in that: include: Collect and obtain risk data and basic data in different dimensions in various regions; Calculate various risk indexes corresponding to different dimensions according to the basic data and the risk data; Calculate the average level and standard deviation of the overall region in different dimensions according to the various risk indices, where the overall region includes various regions; Based on the average level and the standard deviation, the weak risk areas corresponding to each region are determined.

2. The method according to claim 1, characterized in that The risk data under different dimensions include personnel risk data, site risk data, dispute risk data, fire risk data and production safety risk data.

3. The method according to claim 1, characterized in that The various risk indexes corresponding to different dimensions are calculated respectively according to the basic data and the risk data, including: Based on the basic data and the risk data, respectively calculate the total proportion of risk data and the total proportion of correction index corresponding to different dimensions; By using the preset risk index calculation formula, various risk indexes are calculated according to the total proportion of the risk data and the total proportion of the correction index.

4. The method according to claim 3, characterized in that The method further comprises: Based on the basic data, the risk values ​​of different dimensions of each region and the total risk value of the entire region are calculated respectively; The total risk data ratio is calculated according to the risk value and the total of the risk values.

5. The method according to claim 3, characterized in that: The method further comprises: Based on the risk data, respectively calculate the correction index of each region and the sum of the correction index corresponding to the entire region; The total proportion of the correction index is calculated according to the correction index and the sum of the correction indexes.

6. The method according to claim 1, characterized in that The method further comprises: Various risk indexes under different dimensions in each region are ranked and compared to obtain the corresponding risk area ranking results.

7. The method according to claim 1, characterized in that Based on the average level and the standard deviation, the corresponding weak risk areas of each region are determined, including: For various risk indices under different dimensions, based on the average level, the standard deviation and the preset deviation multiple, determine the target risk index exceeding the threshold in each region; Based on the target risk index, the weak risk areas corresponding to each region are determined.

8. A regional risk assessment device based on regional index, characterized in that: include: Data acquisition module, used to collect and obtain risk data and basic data in different dimensions in various regions; A first calculation module, used to calculate various risk indexes corresponding to different dimensions according to the basic data and the risk data; A second calculation module is used to calculate the average level and standard deviation of the overall region in different dimensions according to the various risk indices, wherein the overall region includes various regions; The risk determination module is used to determine the weak risk areas corresponding to each region based on the average level and the standard deviation.

9. A regional risk assessment device based on regional index, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the regional risk assessment method based on regional index described in any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the regional risk assessment method based on regional index as described in any one of claims 1 to 7.

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