A dynamic inspection method for goods carried by administrative vehicles in a special customs supervision zone

Abstract

The application discloses a dynamic inspection method for goods smuggling of administrative vehicles in a special customs supervision zone, comprising the following steps: 1. determining a target sampling ratio per day and a sampling probability of the first administrative vehicle to be passed per day; 2. setting an administrative vehicle inspection probability correction decision; 3. selecting the sampling probability correction decision and determining a sampling probability of the i-th administrative vehicle to be passed per day; 4. sampling the i-th administrative vehicle to be passed; and 5. cyclically executing steps 3-4. The application can dynamically adjust the sampling probability, so that the actual sampling ratio converges to the target sampling ratio; the sampling probability of high-risk administrative vehicles can be adjusted; the sampling probability can be dynamically adjusted according to the traffic intensity of the vehicles, so that the influence of the goods smuggling inspection on the vehicle traffic is minimized, the efficiency of the goods smuggling sampling is improved, and illegal goods smuggling behaviors are effectively deterred.

Description

Technical Field

[0001] This invention belongs to the field of customs vehicle concealment inspection technology, specifically relating to a dynamic inspection method for concealed goods in administrative vehicles in special customs supervision zones. Background Technology

[0002] Special customs supervision zones are closed zones designated by customs authorities, comprised of various types of enterprises, such as comprehensive bonded zones and free trade zones. According to relevant policies and regulations, vehicles carrying goods illegally smuggled into these zones must be inspected at their entrances and exits, and vehicles found illegally smuggling goods must be recorded. Currently, the methods for inspecting vehicles carrying goods illegally smuggled into special customs supervision zones have the following shortcomings:

[0003] (1) The inspection ratio is difficult to control precisely: Existing methods generally involve randomizing vehicles according to a certain ratio. However, the actual inspection ratio is difficult to match the expected ratio because the number of vehicles passing through each park entrance and exit in a day is a random variable, and it is impossible to predict how many vehicles need to be inspected at each park entrance and exit in a day. For example, it is expected that 1% of the passing vehicles will be inspected, but the number of passing vehicles N in a day cannot be predicted. Therefore, each vehicle can only be inspected with a probability of 1%. According to Bernoulli's law of large numbers, only when the number of passing vehicles N approaches infinity can the actual number of vehicles inspected be 1%×N. In the actual environment, the number of passing vehicles at each park entrance and exit cannot approach infinity, resulting in a large difference between the actual number of vehicles inspected when the vehicle is inspected with a fixed probability of 1% and 1%×N.

[0004] (2) Random inspection of vehicles with a fixed probability is prone to missed inspections: The existing system randomly inspects vehicles with a fixed probability, and the inspection probability is easily perceived by the vehicles, which may lead to the vehicles consciously avoiding inspection; vehicles that have violated regulations in the past are more likely to violate regulations again. If they are randomly inspected with a fixed probability, their subsequent violations may not be detected.

[0005] (3) Random checks of vehicles with a fixed probability have a significant impact on vehicle traffic: The existing system generally conducts random checks of vehicles at the park entrances and exits. During peak hours, vehicles frequently enter and exit the park. If random checks of vehicles are conducted with a fixed probability, a large number of checks will be conducted during peak hours, resulting in a decrease in vehicle traffic efficiency and even long waiting times.

[0006] Therefore, in order to address the above problems, it is necessary to design a scientific method for inspecting vehicles carrying concealed goods, which can effectively deter illegal concealment without affecting the normal passage of vehicles. Summary of the Invention

[0007] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a dynamic inspection method for smuggling goods by administrative vehicles in special customs supervision zones. This method can dynamically adjust the sampling probability to make the actual sampling ratio converge to the target sampling ratio; it can adjust the sampling probability for high-risk administrative vehicles; and it can dynamically adjust the sampling probability according to the traffic volume of vehicles, so as to minimize the impact of goods smuggling inspection on vehicle traffic, while improving the efficiency of goods smuggling inspection and effectively deterring illegal goods smuggling.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a dynamic inspection method for goods concealed in administrative vehicles in special customs supervision zones, characterized in that the method includes the following steps:

[0009] Step 1: Determine the target sampling rate P for administrative vehicles passing through the gate of the special customs supervision zone each day; and set the sampling probability p1 = P for the first administrative vehicle waiting to pass through the gate each day.

[0010] Step 2: Set the probability adjustment decision for administrative vehicle inspection:

[0011] The administrative vehicle inspection probability correction decision includes a first inspection probability correction decision, a second inspection probability correction decision, and a third inspection probability correction decision;

[0012] The first inspection probability correction decision is as follows:

[0013] a1. According to formula p t =n / N, calculate the actual sampling rate p of the administrative vehicles that have passed through the gate after the (i-1)th administrative vehicle has passed through. t Where n is the number of administrative vehicles being inspected, N is the total number of administrative vehicles that have passed through the gate; i = 2, 3, ..., I, I is the total number of administrative vehicles passing through the gate each day;

[0014] a2. Adjust the sampling probability according to the actual sampling ratio, and the specific rules are as follows:

[0015] When p t >P and p min <p i-1 -Δp<p max At that time, the first verification probability correction decision takes effect, and the first correction value p is set. a = -Δp, where Δp is the adjustment increment for the sampling probability; p min p is the minimum sampling probability control value. max This is the maximum sampling probability control value;

[0016] When p t <P and p min <pi-1 +Δp<p max At that time, the first verification probability correction decision takes effect, and the first correction value p is set. a =Δp;

[0017] When p t =P or p i-1 +p a ≤p min or p i-1 +p a ≥p max In this case, the first inspection probability correction decision does not take effect;

[0018] The second verification probability correction decision is as follows:

[0019] b1. The gate scans the information of the i-th administrative vehicle waiting to pass through, and classifies the i-th administrative vehicle waiting to pass through into a low-risk administrative vehicle or a high-risk administrative vehicle based on the information in the database of administrative vehicles passing through the special customs supervision zone.

[0020] b2. When the i-th administrative vehicle waiting to pass is a low-risk administrative vehicle, the second inspection probability correction decision does not take effect.

[0021] When the i-th administrative vehicle waiting to pass is a high-risk administrative vehicle, the second inspection probability correction decision takes effect, and the second correction value p is set. b =λp, where λp is the punitive probability increment;

[0022] The third verification probability correction decision is specifically as follows:

[0023] c1. Divide a day into M time periods with time T as the interval, and mark each time period of the day as an idle time period, a moderately busy time period, and a busy time period according to the number of vehicles passing through the same time period of the previous workday.

[0024] c2. When the time for the i-th administrative vehicle to pass through the gate is within an idle period, the third verification probability correction decision takes effect, and the third correction value p is set. c =ωp, where ωp is the increment of the idle time period;

[0025] When the i-th administrative vehicle waiting to pass through the gate is in a busy period, the third verification probability correction decision takes effect, and the third correction value p is set. c =-ωp;

[0026] When the time for the i-th administrative vehicle to pass through the gate is during a moderately busy period, the third inspection probability correction decision will not take effect.

[0027] Step 3: Make a decision on the sampling probability adjustment and determine the sampling probability p of the i-th administrative vehicle passing through the gate each day.i :

[0028] When the first verification probability correction decision takes effect, the first verification probability correction decision is executed. At this time, p i =p i-1 +p a ;

[0029] When the third verification probability correction decision takes effect and the first verification probability correction decision does not take effect, the third verification probability correction decision is executed. In this case, p i =P+p c ;

[0030] When the second verification probability correction decision takes effect and both the first and third verification probability correction decisions take effect, the second verification probability correction decision is executed. In this case, p i =P+p b ;

[0031] When none of the three verification probability correction decisions take effect, let p i =p i-1 ;

[0032] Step 4: Apply the sampling probability to p i A random inspection will be conducted on the i-th administrative vehicle waiting to pass.

[0033] Step 5: Repeat steps 3 and 4 until the daily random inspection of administrative vehicles passing through the gate is completed. At this point, the actual inspection ratio converges to the target inspection ratio.

[0034] The above-mentioned dynamic inspection method for cargo concealed in administrative vehicles in special customs supervision zones is characterized in that: in step a2, p min =0.9P, p max =1.1P, Δp =0.02P.

[0035] The above-mentioned dynamic inspection method for goods concealed in administrative vehicles in special customs supervision zones is characterized in that: in step b2, λp = 0.3P.

[0036] The above-mentioned dynamic inspection method for cargo concealed in administrative vehicles in special customs supervision zones is characterized in that: in step c2, ωp=0.2P.

[0037] The beneficial effects of this invention are as follows: by probing and gradually increasing or decreasing the sampling probability, the sampling probability can be dynamically adjusted so that the actual sampling ratio converges to the target sampling ratio; the sampling probability of high-risk administrative vehicles can be adjusted to achieve key sampling of high-risk administrative vehicles; the sampling probability can be dynamically adjusted according to the traffic volume, so that the impact of cargo smuggling inspection on vehicle traffic is minimized, while improving the efficiency of cargo smuggling inspection and effectively deterring illegal cargo smuggling behavior.

[0038] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0040] like Figure 1 As shown, the present invention provides a dynamic inspection method for goods concealed in administrative vehicles in special customs supervision zones. The method includes the following steps:

[0041] Step 1: Determine the target sampling rate P for administrative vehicles passing through the gate of the special customs supervision zone each day; and set the sampling probability p1 = P for the first administrative vehicle waiting to pass through the gate each day.

[0042] It should be noted that the specific value of the target sampling ratio P is determined by comprehensively considering the number of vehicles passing through, the potential scale of vehicles violating regulations, and the number of staff, in order to ensure the deterrent effect of the sampling while avoiding excessive workload for the staff.

[0043] In this embodiment, the value of P ranges from 1% to 30%;

[0044] Step 2: Set the probability adjustment decision for administrative vehicle inspection:

[0045] The administrative vehicle inspection probability correction decision includes a first inspection probability correction decision, a second inspection probability correction decision, and a third inspection probability correction decision;

[0046] The first inspection probability correction decision is as follows:

[0047] a1. According to formula p t =n / N, calculate the actual sampling rate p of the administrative vehicles that have passed through the gate after the (i-1)th administrative vehicle has passed through. t Where n is the number of administrative vehicles being inspected, N is the total number of administrative vehicles that have passed through the gate; i = 2, 3, ..., I, I is the total number of administrative vehicles passing through the gate each day;

[0048] a2. Adjust the sampling probability according to the actual sampling ratio, and the specific rules are as follows:

[0049] When p t >P and p min <p i-1 -Δp<p max At that time, the first verification probability correction decision takes effect, and the first correction value p is set. a = -Δp, where Δp is the adjustment increment for the sampling probability; p min p is the minimum sampling probability control value. max This is the maximum sampling probability control value;

[0050] When p t <P and p min <p i-1 +Δp<p max At that time, the first verification probability correction decision takes effect, and the first correction value p is set. a =Δp;

[0051] When p t =P or p i-1 +p a ≤p min or p i-1 +p a ≥p max In this case, the first inspection probability correction decision does not take effect;

[0052] It should be noted that the first inspection probability correction decision is to continuously adjust the actual vehicle inspection probability based on the feedback from the current gate terminal on the actual inspection ratio of the administrative vehicles that have passed through. This allows the inspection probability to be adjusted according to the actual situation, and can more accurately control the actual inspection ratio of passing vehicles.

[0053] The second verification probability correction decision is as follows:

[0054] b1. The gate scans the information of the i-th administrative vehicle waiting to pass through, and classifies the i-th administrative vehicle waiting to pass through into a low-risk administrative vehicle or a high-risk administrative vehicle based on the information in the database of administrative vehicles passing through the special customs supervision zone.

[0055] b2. When the i-th administrative vehicle waiting to pass is a low-risk administrative vehicle, the second inspection probability correction decision does not take effect.

[0056] When the i-th administrative vehicle waiting to pass is a high-risk administrative vehicle, the second inspection probability correction decision takes effect, and the second correction value p is set. b =λp, where λp is the punitive probability increment;

[0057] In this embodiment, low-risk administrative vehicles are those with no prior record of smuggling and whose affiliated companies are not key monitoring companies, while high-risk administrative vehicles are those with a prior record of smuggling or whose affiliated companies are key monitoring companies. Key monitoring companies include precious metal processing companies and state-controlled drug manufacturing companies. In addition, only vehicles whose information is in the customs special supervision zone vehicle database are eligible to pass, and vehicles without the right to pass cannot pass through the gate.

[0058] In this embodiment, when an administrative vehicle with a previous record of illegal smuggling and whose parent company is a non-key monitoring company fails to find any illegal goods smuggled after more than ten consecutive inspections, it is reclassified as a low-risk administrative vehicle.

[0059] In this embodiment, a camera assembly for scanning information of administrative vehicles waiting to pass is provided on the side of the gate. The gate and camera assembly adopt the gate and S1 camera, S2 camera and S3 camera in the invention patent with application number 202110313127.9, "Sampling Method and Device for Inspection of Cargo Smuggling by Administrative Vehicles in Special Customs Supervision Zones".

[0060] The third verification probability correction decision is specifically as follows:

[0061] c1. Divide a day into M time periods with time T as the interval, and mark each time period of the day as an idle time period, a moderately busy time period, and a busy time period according to the number of vehicles passing through the same time period of the previous workday.

[0062] In this embodiment, if the day is a holiday, the various time periods of the day are marked according to the busy level of the same time period of the previous holiday.

[0063] In this embodiment, M is a positive integer not less than 5;

[0064] c2. When the time for the i-th administrative vehicle to pass through the gate is within an idle period, the third verification probability correction decision takes effect, and the third correction value p is set. c =ωp, where ωp is the increment of the idle time period;

[0065] When the i-th administrative vehicle waiting to pass through the gate is in a busy period, the third verification probability correction decision takes effect, and the third correction value p is set. c =-ωp;

[0066] When the time for the i-th administrative vehicle to pass through the gate is during a moderately busy period, the third inspection probability correction decision will not take effect.

[0067] It should be noted that after implementing the third inspection probability correction decision, most of the inspections can be adjusted to less busy traffic periods while maintaining the overall inspection ratio, thus ensuring traffic efficiency during peak traffic hours.

[0068] Step 3: Make a decision on the sampling probability adjustment and determine the sampling probability p of the i-th administrative vehicle passing through the gate each day. i :

[0069] When the first verification probability correction decision takes effect, the first verification probability correction decision is executed. At this time, p i =p i-1 +p a ;

[0070] When the third verification probability correction decision takes effect and the first verification probability correction decision does not take effect, the third verification probability correction decision is executed. In this case, p i =P+p c ;

[0071] When the second verification probability correction decision takes effect and both the first and third verification probability correction decisions take effect, the second verification probability correction decision is executed. In this case, p i =P+p b ;

[0072] When none of the three verification probability correction decisions take effect, let p i =p i-1 ;

[0073] Step 4: Apply the sampling probability to p i A random inspection will be conducted on the i-th administrative vehicle waiting to pass.

[0074] Step 5: Repeat steps 3 and 4 until the daily random inspection of administrative vehicles passing through the gate is completed. At this point, the actual inspection ratio converges to the target inspection ratio.

[0075] In this embodiment, the random result program randomly selects the inspection results based on the probability of the i-th administrative vehicle passing through the gate each day. When the random result program obtains the first random result, it means that the administrative vehicle was not selected, and the gate opens to allow the vehicle to pass. When the random result program obtains the second random result, it means that the administrative vehicle was selected, and the gate will not allow the vehicle to pass in subsequent processes. At the same time, the server notifies the gate staff to conduct a cargo inspection on the administrative vehicle. After the inspection, the staff returns the inspection results to the server. If the administrative vehicle is not carrying cargo, the server controls the gate to open and allow the vehicle to pass. If the administrative vehicle is carrying cargo, the staff processes the vehicle and marks it as a high-risk administrative vehicle in the database of administrative vehicles passing through the customs special supervision zone.

[0076] It should be noted that the number of high-risk administrative vehicles is relatively small, generally one-thousandth of the total number of administrative vehicles passing through the gate each day. Therefore, increasing the sampling probability of high-risk administrative vehicles will not have a significant impact on the overall actual sampling ratio.

[0077] In this embodiment, in step a2, p min =0.9P, p max =1.1P, Δp =0.02P.

[0078] In this embodiment, in step b2, λp = 0.3P.

[0079] In this embodiment, in step c2, ωp = 0.2P.

[0080] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic inspection method for goods concealed in administrative vehicles in special customs supervision zones, characterized in that, The method includes the following steps: Step 1: Determine the target sampling rate P for administrative vehicles passing through the gate of the special customs supervision zone each day; and set the sampling probability p1 = P for the first administrative vehicle waiting to pass through the gate each day. Step 2: Set the probability adjustment decision for administrative vehicle inspection: The administrative vehicle inspection probability correction decision includes a first inspection probability correction decision, a second inspection probability correction decision, and a third inspection probability correction decision; The first inspection probability correction decision is as follows: a1. According to formula p t =n / N, calculate the actual sampling rate p of the administrative vehicles that have passed through the gate after the (i-1)th administrative vehicle has passed through. t Where n is the number of administrative vehicles being inspected, N is the total number of administrative vehicles that have passed through the gate; i = 2, 3, ..., I, I is the total number of administrative vehicles passing through the gate each day; a2. Adjust the sampling probability according to the actual sampling ratio, and the specific rules are as follows: When p t >P and p min <p i-1 -Δp<p max At that time, the first verification probability correction decision takes effect, and the first correction value p is set. a = -Δp, where Δp is the adjustment increment for the sampling probability; p min p is the minimum sampling probability control value. max p is the maximum sampling probability control value. i-1 Let be the probability of sampling the (i-1)th administrative vehicle; When p t <P and p min <p i-1 +Δp<p max At that time, the first verification probability correction decision takes effect, and the first correction value p is set. a =Δp; When p t =P or p i-1 +p a ≤p min or p i-1 +p a ≥p max In this case, the first inspection probability correction decision does not take effect; The second verification probability correction decision is as follows: b1. The gate scans the information of the i-th administrative vehicle waiting to pass through, and classifies the i-th administrative vehicle waiting to pass through into a low-risk administrative vehicle or a high-risk administrative vehicle based on the information in the database of administrative vehicles passing through the special customs supervision zone. b2. When the i-th administrative vehicle waiting to pass is a low-risk administrative vehicle, the second inspection probability correction decision does not take effect. When the i-th administrative vehicle waiting to pass is a high-risk administrative vehicle, the second inspection probability correction decision takes effect, and the second correction value p is set. b =λp, where λp is the punitive probability increment; The third verification probability correction decision is specifically as follows: c1. Divide a day into M time periods with time T as the interval, and mark each time period of the day as an idle time period, a moderately busy time period, and a busy time period according to the number of vehicles passing through the same time period of the previous workday. c2. When the time for the i-th administrative vehicle to pass through the gate is within an idle period, the third verification probability correction decision takes effect, and the third correction value p is set. c =ωp, where ωp is the increment of the idle time period; When the i-th administrative vehicle waiting to pass through the gate is in a busy period, the third verification probability correction decision takes effect, and the third correction value p is set. c =-ωp; When the time for the i-th administrative vehicle to pass through the gate is during a moderately busy period, the third inspection probability correction decision will not take effect. Step 3: Make a decision on the sampling probability adjustment and determine the sampling probability p of the i-th administrative vehicle passing through the gate each day. i : When the first verification probability correction decision takes effect, the first verification probability correction decision is executed. At this time, p i =p i-1 +p a ; When the third verification probability correction decision takes effect and the first verification probability correction decision does not take effect, the third verification probability correction decision is executed. In this case, p i =P+p c ; When the second verification probability correction decision takes effect and both the first and third verification probability correction decisions take effect, the second verification probability correction decision is executed. In this case, p i =P+p b ; When none of the three verification probability correction decisions take effect, let p i =p i-1 ; Step 4: Apply the sampling probability to p i A random inspection will be conducted on the i-th administrative vehicle waiting to pass. Step 5: Repeat steps 3 and 4 until the daily random inspection of administrative vehicles passing through the gate is completed. At this point, the actual inspection ratio converges to the target inspection ratio.

2. The dynamic inspection method for goods concealed in administrative vehicles in special customs supervision zones according to claim 1, characterized in that: In step a2, p min =0.9P, p max =1.1P, Δp =0.02P.

3. A dynamic inspection method for cargo concealed in administrative vehicles in special customs supervision zones, as described in claim 1, characterized in that: In step b2, λp = 0.3P.

4. A dynamic inspection method for cargo concealed in administrative vehicles in special customs supervision zones according to claim 1, characterized in that: In step c2, ωp = 0.2P.

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