Associated risk hierarchical management and control and hidden danger investigation and treatment method

Abstract

The invention discloses an associated risk hierarchical management and control and hidden danger troubleshooting and treatment method, which belongs to the field of dual prevention mechanisms, and comprises the following steps of: 1, forming a risk identification list by taking various places, equipment facilities and operation activities in the emergency industry as basic risk units; 2, obtaining previous disaster records of risk units of the same type through big data, and carrying out risk assessment on the risk units on the risk identification list according to the previous disaster records; 3, establishing a security monitoring identification list; step 4, generating a radar map based on the safety monitoring identification list, and constructing a hidden danger troubleshooting priority list based on the radar map; step 5, carrying out hidden danger investigation on the risk units according to the priority list; and 6, formulating accident potential treatment schemes and measures for the risk units with potential hazards. According to the invention, through deep association of risk grading management and control and hidden danger investigation and treatment, risk hidden dangers can be timely and effectively found and treated.

Description

Technical Field

[0001] The present invention relates to the field of dual prevention mechanisms, and specifically to a method for associating risk classification management and control and hidden danger investigation and treatment. Background Art

[0002] The emergency industry refers to the industry that mainly deals with sudden incidents, emergency conditions and other similar situations. Among them, risk classification control and hidden danger investigation and management are the most important prevention mechanisms in the emergency industry.

[0003] Risk classification control refers to the risk control method that determines different levels of control according to factors such as different risk levels, required control resources, control capabilities, complexity and difficulty of control measures. Its core lies in scientifically assessing risks and implementing different levels of control measures based on the assessment results to achieve effective control and reduction of risks. Hidden danger investigation and control refers to the process of conducting a comprehensive inspection of the production system, workplace, equipment and facilities, management links, etc. of the production and operation unit to discover and eliminate various potential dangers and harmful factors that may cause accidents. Its purpose is to prevent and control the occurrence of accidents, protect the lives and property of employees, and promote safe production and sustainable development of enterprises.

[0004] The existing emergency industry prevention mechanism cannot effectively link risk classification control with hidden danger investigation and management, and thus cannot timely and effectively discover risk hazards for management. Therefore, those skilled in the art provide a method for linking risk classification control with hidden danger investigation and management to solve the problems raised in the above background technology. Summary of the invention

[0005] The purpose of the present invention is to provide a method for associating risk classification control and hidden danger investigation and management. By deeply associating risk classification control with hidden danger investigation and management, risk hidden dangers can be discovered and managed in a timely and effective manner to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for associating risk classification control and hidden danger investigation and treatment, comprising the following steps:

[0008] Step 1: Take various places, equipment and facilities, and operating activities in the emergency industry as basic risk units and form a risk identification list;

[0009] Step 2: Obtain previous disaster records of risk units of the same type through big data, and use this to conduct risk assessment on the risk units on the risk identification list;

[0010] Step 3: Establish a safety monitoring identification list, which includes the flow of people in the risk unit, risk assessment information, and the degree of negligence of the responsible party;

[0011] Step 4: Generate a radar chart based on the safety monitoring identification list, and use the radar chart to build a priority list for potential hazards.

[0012] Step 5: Conduct risk inspections on risk units according to the priority list, and classify accident hazards according to pre-established accident hazard determination criteria, and adjust the responsible party's dereliction of duty information based on the classification results;

[0013] Step 6: Develop accident hazard control plans and measures for risk units with hidden dangers, and after the control is completed, inspect and accept the effectiveness of the accident hazard control.

[0014] As a further solution of the present invention: in step 2, the specific process of performing risk assessment on the risk units on the risk identification list is as follows:

[0015] The risk unit to be assessed is marked as A, and the previous disaster records of the same type of risk unit as A are marked as ai, i = 1···n, where n is a positive integer;

[0016] Extract the total number of disaster victims from ai and mark it as B, where the number of deaths caused by the disaster is marked as b1, the number of disabled people caused by the disaster is marked as b2, and the number of non-disabled injured people caused by the disaster is marked as b3, B = b1 + b2 + b3, calculate the weight value of the victims of ai S1 = (3*b1 + 2*b2 + b3) / B;

[0017] Extract the total duration of the disaster from ai and mark it as C, where the time from the occurrence of the disaster to its discovery is marked as c1, the time from the discovery of the disaster to the start of rescue is marked as c2, and the time from the start to the end of rescue is marked as c3, C = c1 + c2 + c3, calculate the disaster time weight value of ai S2 = (3*c3 + 2*c2 + c3) / C, the units of c1, c2, and c3 are all hours;

[0018] Calculate the risk score V of each ai according to the risk assessment calculation formula V = (S1*S2*D), and then select the maximum value Vmax as the risk score of A, and D is the preset value;

[0019] The risk score is output as the risk assessment result.

[0020] As a further solution of the present invention: in step 3, the flow of people information in the security monitoring identification list is specifically:

[0021] The flow of people on that day in the place where the risk unit is located is marked as E;

[0022] If the day is a holiday, the flow of people on the past seven holidays is retrieved from the big data and marked as e1, e2, e3, e4, e5, e6, and e7 respectively;

[0023] If the current day is a working day, the flow of people in the past seven working days is retrieved from the big data and marked as ee1, ee2, ee3, ee4, ee5, ee6, and ee7 respectively;

[0024] Calculate the average flow of people in the risk unit on a holiday day R1 = (e1 + e2 + e3 + e4 + e5 + e6 + e7 + E) / 8;

[0025] Calculate the average flow of people in the risk unit on a working day R2 = (ee1 + ee2 + ee3 + ee4 + ee5 + ee6 + ee7 + E) / 8;

[0026] If the current day is a holiday, the flow score of the risk unit is calculated as Q = (R1 / r) * 100, where r is the preset flow value; if the current day is a working day and the next day is also a working day, the flow score of the risk unit is calculated as Q = (R2 / r) * 100; if the current day is a working day and the next day is a holiday, the flow score of the risk unit is calculated as Q = (1.5*R2 / r) * 100;

[0027] The crowd flow score Q of the place where the risk unit is located is output as the crowd flow information.

[0028] As a further solution of the present invention: in step 3, the dereliction of duty information of the responsible party in the safety monitoring identification list is specifically:

[0029] The responsible party is assigned a dereliction of duty score W, and the dereliction of duty score W in the initial state is 30;

[0030] If the responsible party is found to have hidden dangers in the risk unit it is responsible for during the hidden danger investigation, its dereliction of duty score W will be lowered;

[0031] If the responsible party is found to have no hidden dangers in the risk unit it is responsible for during the hidden danger investigation, its dereliction of duty score W will be increased;

[0032] The dereliction of duty score W is output as the dereliction of duty information of the responsible party.

[0033] As a further solution of the present invention: in step 4, the specific construction process of the hidden danger investigation priority list is:

[0034] Extract the traffic score Q, risk score Vmax, and dereliction of duty score W of the risk unit;

[0035] Generate a radar chart P of the risk unit based on Q, Vmax and W, calculate the area of ​​the radar chart P and mark it as M;

[0036] Sort the radar chart areas M corresponding to each risk unit from large to small to obtain a target list;

[0037] Output the target list as a priority list for potential risks to be investigated.

[0038] As a further solution of the present invention: in step 5, the specific process of grading the accident hazards according to the pre-established accident hazard determination criteria is as follows:

[0039] If there is no accident hazard in the risk unit, the output inspection result is safe and without hidden dangers;

[0040] If there is a potential accident hazard in the risk unit, the output result is that there is a potential danger and proceed to the next step;

[0041] Extract the risk score Vmax corresponding to the risk unit, if 0<Vmax≤10, then the current accident hazard level is output as level one; if 10<Vmax≤20, then the current accident hazard level is output as level two; if 20<Vmax≤30, then the current accident hazard level is output as level three; if 30<Vmax≤40, then the current accident hazard level is output as level four; if 40<Vmax≤50, then the current accident hazard level is output as level five; if 50<Vmax≤60, then the current accident hazard level is output as level six; if 60<Vmax≤70, then the current accident hazard level is output as level seven; if 70<Vmax≤80, then the current accident hazard level is output as level eight; if 80<Vmax≤90, then the current accident hazard level is output as level nine; if 90<Vmax, then the current accident hazard level is output as level nine.

[0042] As a further solution of the present invention: in step 5, the specific process of adjusting the dereliction of duty information of the responsible party according to the classification result is:

[0043] If the result of the investigation is that there are no hidden dangers, the negligence score W of the responsible party will be increased, and the increased negligence score will be (W+1);

[0044] If the inspection result shows that there is a danger or hidden danger, the dereliction of duty score W of the responsible party will be lowered. The dereliction of duty score after the reduction is (W-2*k), where k is the accident hazard level corresponding to the risk unit.

[0045] As a further solution of the present invention: in step 6, the specific process of formulating the accident hazard treatment plan measures is as follows:

[0046] Mark the risk unit that needs to be treated for potential accidents as X, and then mark the type of potential accidents in X as Y;

[0047] The previous treatment plans with the same risk unit as X and the same accident hazard as Y are marked as zi, i = 1···n, where n is a positive integer;

[0048] Select the 100 solutions with the shortest governance time from zi and sort them in ascending order of governance time to form the first sequence;

[0049] Select the 100 plans with the least governance cost from zi and sort them in order of governance cost from small to large to form the second sequence;

[0050] Match the governance solutions in the first sequence with those in the second sequence, retain those that are successfully matched, and remove those that are not successfully matched, to form an adjusted first sequence and second sequence;

[0051] Assign weight values ​​to the control plans in the adjusted first and second sequences, and select the final accident hazard control plan based on the weight values.

[0052] As a further solution of the present invention: the specific process of selecting the final accident hazard treatment solution according to the weight value is:

[0053] The weight value of the governance scheme in the first sequence is marked as G1, where if the governance scheme ranks first in the first sequence, G1=1; if the governance scheme ranks second in the first sequence, G1=2; the value of G1 is equal to the ranking of the governance scheme in the first sequence;

[0054] The weight value of the governance scheme in the second sequence is marked as G2, where if the governance scheme ranks first in the second sequence, G2=1; if the governance scheme ranks second in the second sequence, G2=2; the value of G2 is equal to the ranking of the governance scheme in the second sequence;

[0055] Calculate the total weight value of the governance plan Gtotal = G1 + G2, and output the governance plan with the smallest Gtotal as the final accident hazard governance plan.

[0056] As a further solution of the present invention: when the effectiveness of the accident hazard management is accepted, the scores of all acceptance personnel are first counted, and after removing the smallest and largest scores, the average score is calculated, and this is used as the final score for the acceptance. The higher the score, the more effective the accident hazard management is.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. This application deeply links risk classification management with hidden danger investigation and treatment, so as to timely and effectively discover and treat risks and hidden dangers.

[0059] 2. This application collects the number of people affected by the disaster and the disaster situation for differential calculation, and collects the time from the occurrence to the end of the disaster for differential calculation, so that the risk score obtained is more representative.

[0060] 3. This application makes adaptive adjustments to the responsible party's dereliction of duty score based on the hidden danger inspection results, thereby making the responsible party's dereliction of duty score more representative. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The present invention is a flow chart of a method for associating risk classification control and hidden danger investigation and management. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] As mentioned in the background technology of this application, research has found that the existing emergency industry prevention mechanism cannot effectively link risk classification control with hidden danger investigation and management, and thus cannot timely and effectively discover risk hazards for management, and has certain defects.

[0064] In order to solve the above-mentioned defects, the present application discloses a method for associating risk classification control and hidden danger investigation and management. By deeply associating risk classification control with hidden danger investigation and management, risk hidden dangers can be discovered and managed in a timely and effective manner.

[0065] The following will describe in detail how the solution of the present application solves the above technical problems in conjunction with the accompanying drawings.

[0066] See also Figure 1In an embodiment of the present invention, a method for associating risk classification control and hidden danger investigation and treatment includes the following steps: Step 1: Take various places, equipment facilities, and operating activities in the emergency industry as basic risk units to form a risk identification list; Step 2: Obtain previous disaster records of risk units of the same type through big data, and use this to conduct risk assessment on the risk units on the risk identification list; Step 3: Establish a safety monitoring identification list, which includes the flow of people information, risk assessment information, and dereliction of duty information of the responsible party at the place where the risk unit is located; Step 4: Generate a radar chart based on the safety monitoring identification list, and use the radar chart to construct a hidden danger investigation priority list; Step 5: According to the priority list, the risk unit is investigated for hidden dangers, and the accident hidden dangers are graded according to the pre-established accident hidden danger judgment standard, and the responsible party dereliction of duty information is adjusted according to the classification results; Step 6: Formulate accident hidden danger treatment plans and measures for risk units with hidden dangers, and after the treatment is completed, the effectiveness of the accident hidden danger treatment is inspected and accepted. This application can timely and effectively discover risk hidden dangers and treat them by deeply associating risk classification control with hidden danger investigation and treatment.

[0067] In this embodiment, in step 2, the specific process of risk assessment for the risk unit on the risk identification list is as follows: mark the risk unit to be assessed as A, and then mark the previous disaster records of the same type of risk unit as A as ai, i=1···n, where n is a positive integer; extract the total number of disaster victims from ai and mark it as B, where the number of deaths caused by the disaster is marked as b1, the number of disabled people caused by the disaster is marked as b2, and the number of non-disabled injured people caused by the disaster is marked as b3, B=b1+b2+b3, and calculate the weight value of the victims of ai S1=(3*b1+2*b2+b3) / B; extract the total number of disaster victims from ai Extract the total duration of the disaster and mark it as C, where the time from the occurrence of the disaster to its discovery is marked as c1, the time from the discovery of the disaster to the start of rescue is marked as c2, and the time from the start of rescue to the end of the disaster is marked as c3, C = c1 + c2 + c3, calculate the disaster time weight value S2 = (3*c3 + 2*c2 + c3) / C of ai, and the units of c1, c2, and c3 are all hours; calculate the risk score V of each ai according to the risk assessment calculation formula V = (S1*S2*D), and then select the maximum value Vmax as the risk score of A, and D is the preset value; output the risk score as the risk assessment result. When calculating the risk score, the more serious the disaster, the greater the weight value S1 of the victims, and the longer the disaster rescue time, the greater the disaster time weight value S2. By collecting the number of disaster victims and the disaster situation for differential calculation, and collecting the time from the occurrence to the end of the disaster for differential calculation, the risk score obtained is more representative.

[0068] In this embodiment, in step three, the flow of people information of the security monitoring identification list is specifically as follows: the flow of people on the day of the risk unit is marked as E; if the day is a holiday, the flow of people on the past seven holidays is retrieved from the big data, and they are marked as e1, e2, e3, e4, e5, e6, and e7 respectively; if the day is a working day, the flow of people on the past seven working days is retrieved from the big data, and they are marked as ee1, ee2, ee3, ee4, ee5, ee6, and ee7 respectively; the average flow of people on the day of the holiday at the risk unit is calculated as R1 = (e1+e2+e3+e4+e5+e6+e7+E) / 8; the risk unit is calculated as The average flow of people at the place where the unit is located on a working day is R2 = (ee1 + ee2 + ee3 + ee4 + ee5 + ee6 + ee7 + E) / 8; if the day is a holiday, the flow score Q = (R1 / r) * 100 of the place where the risk unit is located is calculated, where r is the preset flow value; if the day is a working day and the next day is also a working day, the flow score Q = (R2 / r) * 100 of the place where the risk unit is located is calculated; if the day is a working day and the next day is a holiday, the flow score Q = (1.5*R2 / r) * 100 of the place where the risk unit is located is calculated; the flow score Q of the place where the risk unit is located is output as the flow information. This application adopts different calculation methods for the flow of people on working days and holidays. This setting can quickly and accurately analyze the flow of people at the place where the risk unit is located.

[0069] In this embodiment, in step 3, the dereliction of duty information of the responsible party in the safety monitoring identification list is specifically as follows: assigning a dereliction of duty score W to the responsible party, and the dereliction of duty score W in the initial state = 30; if the responsible party is found to have hidden dangers in the risk unit it is responsible for during the hidden danger investigation, then its dereliction of duty score W is adjusted down; if the responsible party is found to have no hidden dangers in the risk unit it is responsible for during the hidden danger investigation, then its dereliction of duty score W is adjusted up; and the dereliction of duty score W is output as the dereliction of duty information of the responsible party. This setting can make the dereliction of duty score W more representative.

[0070] In this embodiment, in step 4, the specific construction process of the hidden danger investigation priority list is: extract the human flow score Q, risk score Vmax, and dereliction of duty score W of the place where the risk unit is located; generate the radar chart P of the risk unit based on Q, Vmax and W, calculate the area of ​​the radar chart P and mark it as M; sort the radar chart area M corresponding to each risk unit from large to small to obtain a target list; output the target list as a hidden danger investigation priority list. The larger the human flow score Q, risk score Vmax, and dereliction of duty score W of the place where the risk unit is located, the greater the potential hidden danger of the risk unit. This setting can accurately prioritize the risk units with greater potential hidden dangers.

[0071] In this embodiment, in step five, the specific process of grading the accident hazards according to the pre-established accident hazard determination standard is as follows: if there is no accident hazard in the risk unit, the screening result is output as safe and without hazard; if there is an accident hazard in the risk unit, the screening result is output as dangerous and with hazard and proceed to the next step; the risk score Vmax corresponding to the risk unit is extracted, and if 0<Vmax≤10, the current accident hazard level is output as level one; if 10<Vmax≤20, the current accident hazard level is output as level two; if 20<Vmax≤30, the current accident hazard level is output as level one. The hidden danger level is level three; if 30<Vmax≤40, the current hidden danger level is output as level four; if 40<Vmax≤50, the current hidden danger level is output as level five; if 50<Vmax≤60, the current hidden danger level is output as level six; if 60<Vmax≤70, the current hidden danger level is output as level seven; if 70<Vmax≤80, the current hidden danger level is output as level eight; if 80<Vmax≤90, the current hidden danger level is output as level nine; if 90<Vmax, the current hidden danger level is output as level nine. This setting can effectively determine the level of hidden dangers.

[0072] In this embodiment, in step five, the specific process of adjusting the responsible party's dereliction of duty information according to the classification results is: if the investigation result is safe and without hidden dangers, the responsible party's dereliction of duty score W is adjusted upward, and the increased dereliction of duty score is (W+1); if the investigation result is dangerous and has hidden dangers, the responsible party's dereliction of duty score W is adjusted downward, and the downward dereliction of duty score is (W-2*k), where k is the accident hazard level corresponding to the risk unit (for example, if the current accident hazard level of the risk unit is level nine, the corresponding k=9). This application adaptively adjusts the responsible party's dereliction of duty score according to the hidden danger investigation results, so that the responsible party's dereliction of duty score is more representative.

[0073] In this embodiment, in step six, the specific process of formulating accident hazard treatment plan measures is as follows: mark the risk unit that needs to treat the accident hazard at present as X, and then mark the accident hazard type of X as Y; mark the previous treatment plans that are the same type of risk unit as X and the same type of accident hazard as Y as zi, i=1···n, where n is a positive integer; select the 100 plans with the shortest treatment time from zi and sort them in the order of treatment time from small to large to form a first sequence; select the 100 plans with the least treatment cost from zi and sort them in the order of treatment cost from small to large to form a second sequence; match the treatment plans in the first sequence with the second sequence, retain the successfully matched ones, and remove the unmatched ones (for example, if a plan is in both the first sequence and the second sequence, then this plan is retained, if a plan is only in one of the first sequence and the second sequence, then this plan is not retained), to form the adjusted first sequence and second sequence; assign weight values ​​to the treatment plans in the adjusted first sequence and second sequence, and select the final accident hazard treatment plan according to the weight values. This setting can quickly select suitable accident hazard treatment plan measures.

[0074] In this embodiment, the specific process of selecting the final accident hazard treatment plan according to the weight value is: mark the weight value of the treatment plan in the first sequence as G1, wherein, if the treatment plan ranks first in the first sequence, then G1=1; if the treatment plan ranks second in the first sequence, then G1=2; the value of G1 is equal to the ranking of the treatment plan in the first sequence; mark the weight value of the treatment plan in the second sequence as G2, wherein, if the treatment plan ranks first in the second sequence, then G2=1; if the treatment plan ranks second in the second sequence, then G2=2; the value of G2 is equal to the ranking of the treatment plan in the second sequence; calculate the total weight value of the treatment plan Gtotal=G1+G2, and output the treatment plan with the smallest Gtotal as the final accident hazard treatment plan. The treatment plan selected by this application comprehensively considers the treatment time and treatment expenses, so that the final selected treatment plan not only saves more time, but also saves more money.

[0075] In this embodiment, when accepting the effectiveness of accident hazard management, the scores of all acceptance personnel are first counted, and the average score is calculated after removing the smallest and largest scores. This is used as the final score for the acceptance. The higher the score, the more effective the accident hazard management is.

[0076] The present invention deeply links risk classification control with hidden danger investigation and management, and can timely and effectively discover risk hazards and manage them. This application collects the number of people affected by the disaster and the disaster situation for differential calculation, and collects the time from the occurrence to the end of the disaster for differential calculation, so that the risk score obtained is more representative. This application adaptively adjusts the responsible party's dereliction of duty score based on the hidden danger investigation results, so that the responsible party's dereliction of duty score is more representative.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0078] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for associating risk classification control and hidden danger investigation and management, characterized in that: The following steps are involved: Step 1: Take various places, equipment and facilities, and operating activities in the emergency industry as basic risk units and form a risk identification list; Step 2: Obtain previous disaster records of risk units of the same type through big data, and use this to conduct risk assessment on the risk units on the risk identification list; Step 3: Establish a safety monitoring identification list, which includes the flow of people in the risk unit, risk assessment information, and the degree of negligence of the responsible party; Step 4: Generate a radar chart based on the safety monitoring identification list, and use the radar chart to build a priority list for potential hazards. Step 5: Conduct risk inspections on risk units according to the priority list, and classify accident hazards according to pre-established accident hazard determination criteria, and adjust the responsible party's dereliction of duty information based on the classification results; Step 6: Develop accident hazard control plans and measures for risk units with hidden dangers, and after the control is completed, inspect and accept the effectiveness of the accident hazard control.

2. A method for associating risk classification control and hidden danger investigation and treatment according to claim 1, characterized in that: In step 2, the specific process of conducting risk assessment on the risk units on the risk identification list is as follows: The risk unit to be assessed is marked as A, and the previous disaster records of the same type of risk unit as A are marked as ai, i = 1···n, where n is a positive integer; Extract the total number of disaster victims from ai and mark it as B, where the number of deaths caused by the disaster is marked as b1, the number of disabled people caused by the disaster is marked as b2, and the number of non-disabled injured people caused by the disaster is marked as b3, B = b1 + b2 + b3, calculate the weight value of the victims of ai S1 = (3*b1 + 2*b2 + b3) / B; Extract the total duration of the disaster from ai and mark it as C, where the time from the occurrence of the disaster to its discovery is marked as c1, the time from the discovery of the disaster to the start of rescue is marked as c2, and the time from the start to the end of rescue is marked as c3, C = c1 + c2 + c3, calculate the disaster time weight value of ai S2 = (3*c3 + 2*c2 + c3) / C, the units of c1, c2, and c3 are all hours; Calculate the risk score V of each ai according to the risk assessment calculation formula V = (S1*S2*D), and then select the maximum value Vmax as the risk score of A, and D is the preset value; The risk score is output as the risk assessment result.

3. A method for associating risk classification control and hidden danger investigation and treatment according to claim 2, characterized in that: In step 3, the traffic flow information of the security monitoring identification list is specifically: The flow of people on that day in the place where the risk unit is located is marked as E; If the day is a holiday, the flow of people on the past seven holidays is retrieved from the big data and marked as e1, e2, e3, e4, e5, e6, and e7 respectively; If the current day is a working day, the flow of people in the past seven working days is retrieved from the big data and marked as ee1, ee2, ee3, ee4, ee5, ee6, and ee7 respectively; Calculate the average flow of people in the risk unit on a holiday day R1 = (e1 + e2 + e3 + e4 + e5 + e6 + e7 + E) / 8; Calculate the average flow of people in the risk unit on a working day R2 = (ee1 + ee2 + ee3 + ee4 + ee5 + ee6 + ee7 + E) / 8; If the day is a holiday, the flow score of the risk unit is calculated as Q = (R1 / r) * 100, where r is the preset flow value; if the day is a working day and the next day is also a working day, the flow score of the risk unit is calculated as Q = (R2 / r) * 100; if the day is a working day and the next day is a holiday, the flow score of the risk unit is calculated as Q = (1.5*R2 / r) * 100; The crowd flow score Q of the place where the risk unit is located is output as the crowd flow information.

4. A method for associating risk classification control and hidden danger investigation and treatment according to claim 3, characterized in that: In step 3, the responsible party negligence information of the safety monitoring identification list is specifically: The responsible party is assigned a dereliction of duty score W, and the dereliction of duty score W in the initial state is 30; If the responsible party is found to have hidden dangers in the risk unit it is responsible for during the hidden danger investigation, its dereliction of duty score W will be lowered; If the responsible party is found to have no hidden dangers in the risk unit it is responsible for during the hidden danger investigation, its dereliction of duty score W will be increased; The dereliction of duty score W is output as the dereliction of duty information of the responsible party.

5. A method for associating risk classification control and hidden danger investigation and treatment according to claim 4, characterized in that: In step 4, the specific process of constructing the hidden danger investigation priority list is as follows: Extract the traffic score Q, risk score Vmax, and dereliction of duty score W of the risk unit; Generate a radar chart P of the risk unit based on Q, Vmax and W, calculate the area of ​​the radar chart P and mark it as M; Sort the radar chart areas M corresponding to each risk unit from large to small to obtain a target list; Output the target list as a priority list for potential risks to be investigated.

6. A method for associating risk classification control and hidden danger investigation and treatment according to claim 5, characterized in that: In step 5, the specific process of grading the accident hazards according to the pre-established accident hazard determination criteria is as follows: If there is no accident hazard in the risk unit, the output inspection result is safe and without hidden dangers; If there is a potential accident hazard in the risk unit, the output result is that there is a potential danger and proceed to the next step; Extract the risk score Vmax corresponding to the risk unit, if 0<Vmax≤10, then the current accident hazard level is output as level one; if 10<Vmax≤20, then the current accident hazard level is output as level two; if 20<Vmax≤30, then the current accident hazard level is output as level three; if 30<Vmax≤40, then the current accident hazard level is output as level four; if 40<Vmax≤50, then the current accident hazard level is output as level five; if 50<Vmax≤60, then the current accident hazard level is output as level six; if 60<Vmax≤70, then the current accident hazard level is output as level seven; if 70<Vmax≤80, then the current accident hazard level is output as level eight; if 80<Vmax≤90, then the current accident hazard level is output as level nine; if 90<Vmax, then the current accident hazard level is output as level nine.

7. A method for associating risk classification control and hidden danger investigation and treatment according to claim 6, characterized in that: In step 5, the specific process of adjusting the responsible party's dereliction of duty information according to the classification results is as follows: If the result of the investigation is that there are no hidden dangers, the negligence score W of the responsible party will be increased, and the increased negligence score will be (W+1); If the inspection result shows that there is a danger or hidden danger, the dereliction of duty score W of the responsible party will be lowered. The dereliction of duty score after the reduction is (W-2*k), where k is the accident hazard level corresponding to the risk unit.

8. A method for associating risk classification control and hidden danger investigation and treatment according to claim 7, characterized in that: In step 6, the specific process of formulating accident hazard control measures is as follows: Mark the risk unit that needs to be treated for potential accidents as X, and then mark the type of potential accidents in X as Y; The previous treatment plans with the same risk unit as X and the same accident hazard as Y are marked as zi, i = 1···n, where n is a positive integer; Select the 100 solutions with the shortest governance time from zi and sort them in ascending order of governance time to form the first sequence; Select the 100 plans with the least governance cost from zi and sort them in order of governance cost from small to large to form the second sequence; Match the governance solutions in the first sequence with those in the second sequence, retain those that are successfully matched, and remove those that are not successfully matched, to form an adjusted first sequence and second sequence; Assign weight values ​​to the control plans in the adjusted first and second sequences, and select the final accident hazard control plan based on the weight values.

9. A method for associating risk classification control and hidden danger investigation and treatment according to claim 8, characterized in that: The specific process of selecting the final accident hazard treatment plan according to the weight value is as follows: The weight value of the governance scheme in the first sequence is marked as G1, where if the governance scheme ranks first in the first sequence, G1=1; if the governance scheme ranks second in the first sequence, G1=2; the value of G1 is equal to the ranking of the governance scheme in the first sequence; The weight value of the governance scheme in the second sequence is marked as G2, where if the governance scheme ranks first in the second sequence, G2=1; if the governance scheme ranks second in the second sequence, G2=2; the value of G2 is equal to the ranking of the governance scheme in the second sequence; Calculate the total weight value of the governance plan Gtotal = G1 + G2, and output the governance plan with the smallest Gtotal as the final accident hazard governance plan.

10. A method for associating risk classification control and hidden danger investigation and treatment according to claim 9, characterized in that: When the effectiveness of accident hazard management is accepted, the scores of all acceptance personnel are first counted, and the average score is calculated after removing the smallest and largest scores. This is used as the final score for the acceptance. The higher the score, the more effective the accident hazard management is.

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