An image recognition system for a point currency counter

Abstract

The present invention discloses an image recognition system for a currency verification machine. The present invention relates to the technical field of currency verification machines. The present invention realizes effective tracking of the position of counterfeit banknotes by setting a positioning chip inside the currency verification machine, uses a high-resolution camera to take pictures of banknotes from multiple angles, recognizes the information of banknotes based on optical character recognition technology, and uses a deep learning algorithm to deeply analyze the images to accurately judge the authenticity of banknotes. The counterfeit banknote information is integrated in a specific format to obtain sequence Q, Q is combined with the Henon map to obtain the initial encryption sequence Z, and Z is converted into image data, effectively reducing the risk of being tampered with during data transmission. Only when the administrator inputs the correct key can the original information be obtained. The counterfeit banknote information collected in a fixed area is sorted out, the urgent processing area is found from it, and the counterfeit banknote appearance frequency period R is obtained. The present invention effectively tracks the circulation track of counterfeit banknotes and takes appropriate measures.

Description

Technical Field

[0001] The present invention relates to the technical field of currency detectors, and particularly to an image recognition system for currency detectors. Background Art

[0002] With the increase in the currency circulation volume, the problem of counterfeit banknotes has become increasingly serious. Traditional currency detectors mainly focus on the identification of the authenticity of banknotes, lacking the effective collection, analysis, and processing of counterfeit banknote circulation information, unable to track the circulation trajectory of counterfeit banknotes in real time, and difficult to accurately prevent and control in high-incidence areas and time periods of counterfeit banknotes. At the same time, in terms of data security, when traditional currency detectors transmit counterfeit banknote information, the data is not effectively encrypted, resulting in the risk of being stolen and tampered with during the transmission process of counterfeit banknote information. This not only fails to ensure information security but may also lead to misleading of anti-counterfeiting work by incorrect information. Therefore, it is of great practical significance to develop an image recognition system for currency detectors that can solve the above problems. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides an image recognition system for currency detectors, which solves the problems of insecurity in the transmission process of counterfeit banknote information and the inability to track the circulation trajectory of counterfeit banknotes in real time.

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: An image recognition system for currency detectors, comprising:

[0005] A positioning module, which effectively tracks the position of counterfeit banknotes by setting positioning chips inside the currency detector and transmits the detailed position information of counterfeit banknotes to the data analysis module;

[0006] A data analysis module, which sorts out all counterfeit banknote information collected in a fixed area, divides the area into several regions of equal area, calculates the total face value of all counterfeit banknotes in these regions, screens out key areas of concern, calculates the share of counterfeit banknotes of different denominations in each key area of concern and sets the weights corresponding to counterfeit banknotes of different denominations, obtains the comprehensive value of each key area of concern, selects key areas according to the comprehensive value, puts them into the urgent processing area, divides the time period of the urgent processing area, analyzes the number of counterfeit banknotes appearing in different periods to obtain the counterfeit banknote appearance frequency period R, and transmits R to the protection execution module;

[0007] A protection execution module, which, for the received counterfeit banknote appearance frequency period R, takes a series of protection measures in the week before every R weeks hereafter. The protection measures specifically include comprehensively maintaining and upgrading the banknote verification equipment and training relevant staff on counterfeit banknote identification.

[0008] As a further solution of the present invention, before the data analysis module, there further includes an image data collection module, a data encryption module, and a data decryption module. The image data collection module uses a high-resolution camera to photograph banknotes from several set angles, recognizes information such as the denomination numbers and words on the banknotes based on optical character recognition technology, and uses a deep learning algorithm to perform in-depth analysis on the images to accurately determine the authenticity of the banknotes. The denomination, authenticity label, geographical location, specific time, and unique identification ID of the counterfeit banknotes are integrated into a sequence Q in a specific format and transmitted to the data encryption module; the data encryption module is used to combine Q with the Henon mapping to obtain an initial encrypted sequence Z, convert Z into image data, and transmit the image data to the data decryption module; the data decryption module is used to, after obtaining the image data, convert the image information into the original data information by inputting the correct key and the inverse mapping formula, and transmit the obtained original data information to the data analysis module.

[0009] As a further solution of the present invention, the specific steps for combining Q with the Henon mapping to obtain the initial encrypted sequence Z are as follows:

[0010] According to the obtained pure digital sequence Q, calculate the sequence length of Q to be 14;

[0011] Use the Henon mapping to generate an encryption key with a length of 14. The specific formula of this mapping is: x(n + 1)=1 - a*(x(n)^2)+y(n), y(n + 1)=b*x(n);

[0012] Select the x(n) sequence as the encryption key;

[0013] Introduce an alternating sequence P={-1, 1, -1, 1,..., (-1)^t}, where t∈[1, 14];

[0014] Calculate the final encrypted sequence through the formula Z = Q + P*x(n).

[0015] As a further solution of the present invention, the specific steps for converting Z into image data are as follows:

[0016] For each number Ei in the encrypted sequence Z, i∈[1, 14], convert it into an RGB color value according to a specific formula. The specific formulas for calculating the r, g, and b values are as follows: (Ei)mod256 = ri...gi, bi=(31*ri + gi)mod256, where mod() represents the remainder function;

[0017] The encrypted sequence Z is collected with 14 elements. To ensure that the area of each color region is the same, a rectangular image with a width of 1400 pixels and a height of 100 pixels is selected, that is, the image is evenly divided horizontally into 14 regions with a width of 100 pixels and a height of 100 pixels;

[0018] Prepare a blank image. According to the order of the numbers in the encrypted sequence Z, fill the color corresponding to each number into the corresponding region in turn. Starting from the left side of the image, the i-th region is filled with the color (ri, gi, bi) corresponding to the number Ei, and so on until all 14 regions are filled. After filling, save the image in PNG format to ensure lossless image information.

[0019] As a further solution of the present invention, the specific steps to convert the image into the original information are as follows:

[0020] Open the saved PNG image and identify the color of each 100x100 pixel region in order from the left side of the image;

[0021] Select the pixel point at the center position of each region and obtain its RGB color value. If the color value of the center pixel of the region is (ri, gi, bi), calculate it through the inverse mapping formula from color to number. The inverse mapping formula is: numberi = ri × 256 + gi;

[0022] Arrange the obtained numbers in order to obtain the original encrypted sequence Num = {number1, number2,..., number14};

[0023] According to the obtained key x(n), obtain the original data sequence Q according to the inverse transformation formula Q = Num - P * x(n).

[0024] As a further solution of the present invention, the specific method to find the urgent processing area is as follows:

[0025] Sort out all the counterfeit money information collected in a fixed area and divide the area into m regions;

[0026] Calculate the total value Ni of all counterfeit money in each region, i ∈ [1, m], that is, add up the denominations of each counterfeit money in the region;

[0027] Set a total value threshold Nth. If Ni > Nth in a certain region, divide this region into the key attention regions Gj, j ∈ [1, p], where p is the number of key attention regions;

[0028] In the key attention regions, calculate the share Qjt of each denomination of counterfeit money in each region and assign corresponding weights Wjt to each denomination of counterfeit money, where t is the denomination of the counterfeit money;

[0029] Calculate the proportion of each denomination in each area according to the formula Hj = sum(QjtWjt);

[0030] Normalize each area according to the total value Nj of each area in the key area Gj, that is, Nj / sum(Nj);

[0031] Calculate the comprehensive value of each area in the key area of concern Gj according to the formula Vj = Hj*(Nj / sum(Nj));

[0032] If Vj > Vth, then determine this area as the area to be processed urgently.

[0033] As a further solution of the present invention, the specific steps to obtain the frequency period R of counterfeit banknote appearance are as follows:

[0034] For the area to be processed urgently, divide the statistical time period into K equal time periods;

[0035] For each area, count the number of appearances Aijk of counterfeit banknotes of different denominations in each time period, where i represents the denomination type, j represents the area, and k represents the time period;

[0036] Calculate the total appearance frequency Fjk of counterfeit banknotes in this area in each time period = sum(Aijk);

[0037] According to the formula Calculate the autocorrelation function of the appearance frequency of counterfeit banknotes in each area, where is the average value of the appearance frequency of counterfeit banknotes in this area, l is the delay order, and K is the total number of time periods;

[0038] Obtain the frequency period R of counterfeit banknote appearance by calculating the autocorrelation function.

[0039] As a further solution of the present invention, set a threshold Tj for each key area of concern. If , it indicates that there is an obvious fluctuation in the appearance frequency of counterfeit banknotes in this area during this time period.

[0040] The present invention provides an image recognition system for a currency verification machine. Compared with the prior art, it has the following beneficial effects:

[0041] (1) By integrating a positioning chip in the currency verification machine, the present invention can effectively track the geographical location and time of the appearance of counterfeit banknotes, and encrypt the detailed information about counterfeit banknotes, effectively reducing the risk of the counterfeit banknote information being stolen or tampered with during the transmission process, and ensuring the security of information transmission;

[0042] (2) The present invention divides a region into multiple areas to find out the urgent processing areas, and in the urgent processing areas, the frequency period of counterfeit banknotes appearing in the area is found by calculating the autocorrelation function, which prepares for effective protection according to the period later. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] As Figure 1 , the present invention provides an image recognition system for a currency verification machine, including:

[0046] A positioning module, which is mainly used to accurately locate the position of the currency verification machine, and effectively track the position of counterfeit banknotes by installing a positioning chip in the currency verification machine;

[0047] The built-in chip can specifically consider GPS chips, Beidou chips, cellular network positioning chips, and Wi-Fi positioning chips;

[0048] GPS chips are suitable for open space scenarios and areas without obstacles. For example, in outdoor self-service banking halls and outdoor financial publicity activity sites of banks, at least four satellite signals can be received through GPS chips, and the position can be determined by trilateration. For example, in a bank branch in the suburbs of a city, a GPS chip is installed in the currency verification machine, and the longitude and latitude can be accurately obtained with an error within a few meters, providing a basis for counterfeit banknote positioning;

[0049] Beidou chips are suitable for currency verification machines with additional communication function requirements. For example, in some financial service points in remote areas, in addition to the function of positioning counterfeit banknotes, in case of emergencies such as the concentrated appearance of counterfeit banknotes and equipment failures, communication with the superior organization can be achieved through the Beidou short message function. The Beidou chip locates by receiving satellite signals;

[0050] The cellular network positioning chip is suitable for indoor locations or places with weak satellite signals, such as bank branches in underground shopping malls. In such cases, satellite signals usually have difficulty penetrating buildings, and it is impossible to achieve the purpose of positioning by receiving satellite signals. However, the cellular network positioning chip estimates the position by measuring signal parameters with the base station, avoiding the influence of large buildings on satellite signals. For example, at the self-service currency verification equipment of a bank in a large shopping mall, using base station positioning, although the accuracy is within dozens of meters, it can roughly determine the location where counterfeit banknotes appear, and combined with other information, the search scope can be narrowed;

[0051] The Wi-Fi positioning chip is suitable for places with stable Wi-Fi coverage indoors, such as the business hall of a bank. The Wi-Fi network is stable. The Wi-Fi positioning chip locates by scanning Wi-Fi hotspots and comparing the fingerprint database. For example, in the office area of the bank headquarters, the currency verification machine uses Wi-Fi positioning, and the accuracy can reach several meters, and it can accurately record the location where counterfeit banknotes appear within the office area.

[0052] The image data collection module is mainly used to collect detailed information of counterfeit banknotes. When banknotes enter the currency verification machine, high-resolution cameras are used to take pictures of the banknotes from multiple angles, covering the front, back, and edge details of the banknotes to ensure that the obtained image information is complete and clear;

[0053] For the banknote image information obtained in real time, based on optical character recognition technology, information such as the denomination numbers and words on the banknotes is recognized. At the same time, deep learning algorithms are used to deeply analyze the images. These algorithms are trained with a large number of real and counterfeit banknote images and can accurately identify the authenticity of banknotes according to the image features of the input banknotes. For example, by learning multi-dimensional features such as the clarity of watermarks, the magnetic characteristics of security threads, and the printing quality of patterns of real and counterfeit banknotes, the trained deep learning model can accurately judge the authenticity of banknotes;

[0054] The above-mentioned deep learning algorithms include convolutional neural networks. This algorithm has a powerful ability to extract local features and can automatically learn features such as banknote textures and patterns. Its hierarchical structure can not only efficiently process images but also has a high training efficiency;

[0055] Recurrent neural networks and their variants, long short-term memory networks, and gated recurrent units. This algorithm has good effects in processing sequence data, can capture the order and context information of characters and patterns on banknotes, and can judge the authenticity of banknotes according to the specific arrangement rules of banknotes, improving the reliability of recognition;

[0056] Generative adversarial networks. This algorithm has good effects in data augmentation, can generate diverse simulated banknote images, increases the diversity of training data, enables the model to adapt to banknotes of different qualities and styles, and enhances the generalization ability of the model;

[0057] Residual network. This algorithm can train deeper networks to learn more complex and subtle features in banknotes. By accurately identifying the imperceptible differences in counterfeit banknotes, it improves the accuracy of identification. Especially for highly simulated counterfeit banknotes, the identification effect is remarkable.

[0058] If the output result obtained through the trained discriminant model is a counterfeit banknote, start collecting detailed information about the counterfeit banknote and combine it in a specific format.

[0059] The denomination information is directly taken from the optical character recognition result. For example, if "100" is recognized, the denomination is determined to be 100 yuan. The true / false label is represented by "F" for counterfeit banknotes, and can be represented by the serial number 6 in A-Z. The geographical location information is obtained by the positioning module. If the currency checker is equipped with a Beidou positioning chip, precise latitude and longitude coordinates can be obtained, such as "30.67°N, 104.06°E". The specific time is obtained through the high-precision clock built into the currency checker, accurate to the second, for example, "2024-11-10 14:25:30". The unique identification ID is composed of the device number of the currency checker and the serial number of this banknote verification. Assuming the device number is "5" and the serial number is "123", the unique identification ID is "5-123".

[0060] Integrate the collected counterfeit banknote information in a specific format to form a complete data record, such as "100-F-30.67°N-104.06°E-2024-11-10-14-25-30-5-123", and convert it into a pure digital sequence Q = {100, 6, 30, 67, 104, 6, 2024, 11, 10, 14, 25, 30, 5, 123}. This set of pure digital sequences will be transmitted to the data encryption module for encryption to prevent the leakage of counterfeit banknote information.

[0061] Data encryption module. According to the obtained pure digital sequence Q, calculate that the sequence length of Q is 14, and use the Henon map to generate an encryption key with a length of 14. The Henon map is a chaotic map, and the generated sequence has strong chaos. Due to the characteristics of chaotic sequences being sensitive to initial conditions and unpredictable in the long term, the generated encryption key has high randomness and security. The specific formula of this map is: x(n + 1) = 1 - a * (x(n)^2) + y(n), y(n + 1) = b * x(n). In order to obtain a more dispersed and disordered sequence, a is usually taken as 1.38 and b is usually taken as 0.2. Finally, two chaotic sequences x(n) and y(n) are obtained. Considering the three aspects of chaotic characteristics, key space, and computational complexity, the x(n) sequence is selected as the encryption key.

[0062] During the encryption process, an interleaved sequence P = {-1, 1, -1, 1,..., (-1)^t} is introduced, where t ∈ [1, 14]. The final encrypted sequence is calculated by the formula Z = Q + P * x(n). Here, the numbers corresponding to P and x(n) are multiplied, and the numbers corresponding to Q and P * x(n) are added;

[0063] The specific steps to convert the encrypted sequence Z into a picture are as follows:

[0064] For each number Ei in the sequence, i ∈ [1, 14], it is converted into an RGB color value according to a specific formula. The formulas for calculating the r, g, and b values are as follows: (Ei) mod 256 = ri... gi, bi = (31 * ri + gi) mod 256;

[0065] Determine the size of the image. Considering that the sequence Z has 14 elements, to ensure that the area of each color region is the same, a rectangular image with a width of 1400 pixels and a height of 100 pixels can be selected. That is, the image is evenly divided horizontally into 14 regions with a width of 100 pixels and a height of 100 pixels;

[0066] After preparing the blank image, in the order of the numbers in the sequence Z, fill the color corresponding to each number into the corresponding region in turn. Starting from the left side of the image, the i-th region is filled with the color (ri, gi, bi) corresponding to the number Ei, and so on until all 14 regions are filled. After filling, save the image in PNG format to ensure the lossless of image information.

[0067] Data decryption module. After transmitting the encrypted image information to the administrator, the administrator needs to input the correct key to convert the image information into the original data information;

[0068] Open the saved PNG image. Starting from the left side of the image, identify the color of each 100x100 pixel region in order; for example, select the pixel at the center position of each region to obtain its RGB color value. Assume that the color value of the center pixel of the first region is (ri, gi, bi). Calculate through the inverse mapping formula from color to number. The inverse mapping formula is: numberi = ri × 256 + gi; Arrange the obtained numbers in order to get the original encrypted sequence Num = {number1, number2,..., number14}; According to the obtained key x(n), obtain the original data sequence Q according to the inverse transformation formula Q = Num - P * x(n).

[0069] The data analysis module sorts out all the counterfeit currency information collected in a fixed area and divides the area into m regions; calculates the total value Ni of all counterfeit currency in each region, where i ∈ [1, m], that is, adds up the denominations of each counterfeit currency in the region; sets a total value threshold Nth. If Ni > Nth in a certain region, then divide this region into the key attention regions Gj, where j ∈ [1, p], and p is the number of key attention regions. In the key attention regions, calculate the share Qjt of counterfeit currency of each denomination in each region, where t is the denomination of the counterfeit currency; for example, in a certain key attention region, there are 10 counterfeit currency with a denomination of 100 yuan, 5 counterfeit currency with a denomination of 50 yuan, and 3 counterfeit currency with a denomination of 20 yuan, and the total number of counterfeit currency is 18. Then the share of counterfeit currency with a denomination of 100 yuan Qj100 = 10 / 18, the share of counterfeit currency with a denomination of 50 yuan Qj50 = 5 / 18, and the share of counterfeit currency with a denomination of 20 yuan Qj20 = 3 / 18. Assign corresponding weights Wjt to counterfeit currency of each denomination. The weights can be determined through expert evaluation combined with historical data statistical analysis. For example, according to historical data, counterfeit currency with a denomination of 100 yuan has a greater harm to the financial order, and the weight Wj100 = 0.5; the weight of counterfeit currency with a denomination of 50 yuan Wj50 = 0.3; the weight of counterfeit currency with a denomination of 20 yuan Wj20 = 0.2. Thus, the comprehensive situation of the proportion of each denomination in each region is obtained: sum(QjtWjt); then, according to the total value Nj of each region in these key regions Gj, and normalize each region, that is, Nj / sum(Nj); calculate the comprehensive value Vj of each region in the key attention regions Gj = sum(QjtWjt)*(Nj / sum(Nj)); sort these key attention regions according to the comprehensive value. The regions ranked higher not only have a larger total value but also have a larger proportion of counterfeit currency with larger denominations, such as counterfeit currency with a denomination of 100 yuan or 50 yuan. These regions are determined as the urgent processing regions;

[0070] For the urgent processing regions, divide the statistical time period into K equal time cycles. The division of the time cycles can be selected according to the actual situation, such as by week, month, quarter, etc. If it is in units of weeks, if the statistical time period is 3 months, about 13 weeks, then it can be divided into 13 time cycles; for each region, count the number of occurrences Aijk of counterfeit currency of different denominations in each time cycle, where i represents the denomination type, j represents the region, and k represents the time cycle. Calculate the total occurrence frequency Fjk of counterfeit currency in this region in each time cycle = sum(Aijk); calculate the autocorrelation function of the occurrence frequency of counterfeit currency in each region. The specific formula is as follows: where, is the average value of the occurrence frequency of counterfeit currency in this region, l is the delay order, and K is the total number of time cycles; obtain the period R of the occurrence frequency of counterfeit currency by calculating the autocorrelation function.

[0071] The protection execution module, after obtaining the frequency period R of counterfeit banknote appearance in the urgent processing area, takes a series of protection measures in the week before every subsequent R weeks. For example, financial institutions conduct comprehensive maintenance and upgrading of banknote verification equipment to ensure the normal operation of the equipment during the high-incidence period of counterfeit banknotes; provide counterfeit banknote identification training to relevant staff to improve their identification ability; to analyze whether there are obvious fluctuations in a specific time period, a threshold value Tj is set for each key area of concern. The setting of the threshold value can refer to factors such as the standard deviation of the historical data of the counterfeit banknote appearance frequency in this area. If , it indicates that there are obvious fluctuations in the counterfeit banknote appearance frequency in this area during this time period. At this time, it is necessary to strengthen the review of cash transactions, especially the review of counterfeit banknotes with large denominations, such as 100 yuan and 50 yuan, increase the manual review link, and use more advanced banknote verification equipment for secondary verification, etc., to effectively prevent the circulation of counterfeit banknotes.

[0072] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0073] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An image recognition system for a point counting and banknote checking machine, characterized in that, Including: A positioning module, which realizes effective tracking of the position of counterfeit banknotes by setting a positioning chip inside the spot-checking banknote counter and transmits the detailed position information of the counterfeit banknotes to the data analysis module; The data analysis module sorts out all the counterfeit banknote information collected in a fixed area, divides the area into several regions with equal areas, calculates the total face value of all counterfeit banknotes in these regions, screens out the key areas of concern. For each key area of concern, calculates the share of counterfeit banknotes of different denominations and sets the weights corresponding to counterfeit banknotes of different denominations to obtain the comprehensive value of each key area of concern. Selects the key areas according to the comprehensive value and puts them into the urgent processing area. Divides the time period of the urgent processing area, analyzes the number of counterfeit banknotes appearing in different periods to obtain the counterfeit banknote appearance frequency period R, and transmits R to the protection execution module. The specific steps to obtain the counterfeit banknote appearance frequency period R are as follows: For the urgent processing area, divide the statistical time period into K equal time periods; For each area, count the number of appearances Aijk of counterfeit banknotes of different denominations in each time period, where i represents the denomination type, j represents the area, and k represents the time period; Calculate the total appearance frequency Fjk of counterfeit banknotes in this area in each time period = sum(Aijk); According to the formula calculate the autocorrelation function of the counterfeit note appearance frequency in each region, where is the average value of the counterfeit note appearance frequency in this region, l is the delay order, and K is the total number of time periods; Obtain the counterfeit banknote appearance frequency period R by calculating the autocorrelation function; A protection execution module, which, for the received counterfeit banknote appearance frequency period R, takes a series of protection measures in the week before every R weeks in the future. The protection measures specifically include comprehensively maintaining and upgrading the banknote-checking equipment and training the relevant staff on counterfeit banknote identification.

2. The image recognition system for a point counting and banknote checking machine according to claim 1, characterized in that Before the data analysis module, there are also an image data collection module, a data encryption module, and a data decryption module. The image data collection module uses a high-resolution camera to take pictures of banknotes from several set angles, recognizes the denomination numbers and text information on the banknotes based on optical character recognition technology, and uses deep learning algorithms to deeply analyze the images to accurately judge the authenticity of the banknotes. Integrates the denomination, authenticity label, geographical location, specific time, and unique representation ID of the counterfeit banknotes in a specific format to obtain a sequence Q, and transmits Q to the data encryption module; The data encryption module is used to combine Q with the Henon map to obtain an initial encryption sequence Z, convert Z into image data, and transmit the image data to the data decryption module; The data decryption module is used to convert the image information into the original data information by inputting the correct key and the inverse mapping formula after obtaining the image data, and transmit the obtained original data information to the data analysis module.

3. An image recognition system for a point counting and banknote checking machine according to claim 2, characterized in that, The specific steps to combine Q with the Henon map to obtain the initial encryption sequence Z are as follows: According to the obtained pure digital sequence Q, calculate that the sequence length of Q is 14; Use the Henon map to generate an encryption key with a length of 14. The specific formula of this map is: x(n + 1) = 1 - a*(x(n)^2) + y(n), y(n + 1) = b*x(n); Select the x(n) sequence as the encryption key; Introduce the alternating sequence P = {-1, 1, -1, 1,..., (-1)^t}, where t ∈ [1, 14]; The final encrypted sequence is calculated by the formula Z = Q + P * x(n).

4. An image recognition system for a point currency counter according to claim 3, characterized in that, The specific steps to convert Z into image data are as follows: For each number Ei in the encrypted sequence Z, i ∈ [1, 14], convert it into an RGB color value according to a specific formula. The formulas for calculating the r, g, and b values are as follows: (Ei) mod 256 = ri... gi, bi = (31 * ri + gi) mod 256, where mod() represents the remainder function; Since the encrypted sequence Z has 14 elements, to ensure that the area of each color region is the same, select a rectangular image with a width of 1400 pixels and a height of 100 pixels, that is, divide the image horizontally into 14 regions with a width of 100 pixels and a height of 100 pixels; Prepare a blank image, and in the order of the numbers in the encrypted sequence Z, fill the color corresponding to each number into the corresponding region in turn. Starting from the left side of the image, fill the color (ri, gi, bi) corresponding to the number Ei into the i-th region, and so on until all 14 regions are filled. After filling, save the image in PNG format to ensure the lossless of image information.

5. An image recognition system for a currency counter according to claim 2, characterized in that, The specific steps to convert the image into the original information are as follows: Open the saved PNG image, and starting from the left side of the image, identify the color of each 100x100 pixel region in order; Select the pixel point at the center position of each region and obtain its RGB color value. If the color value of the central pixel of the region is (ri, gi, bi), calculate it through the inverse mapping formula from color to number. The inverse mapping formula is: numberi = ri × 256 + gi; Arrange the obtained numbers in order to get the original encrypted sequence Num = {number1, number2,..., number14}; According to the obtained key x(n), obtain the original data sequence Q according to the inverse transformation formula Q = Num - P * x(n).

6. The image recognition system for a point counting and banknote checking machine according to claim 1, characterized in that, The specific method to find the urgent processing area is as follows: Sort out all the counterfeit currency information collected in a fixed area, and divide the area into m regions; Calculate the total value Ni of all counterfeit currency in each region, i ∈ [1, m], that is, add up the denominations of each counterfeit currency in the region; Set a total value threshold Nth. If Ni > Nth in a certain region, then divide this region into the key attention regions Gj, j ∈ [1, p], where p is the number of key attention regions; In the key attention regions, calculate the share Qjt of each denomination of counterfeit currency in each region and assign the corresponding weight Wjt to each denomination of counterfeit currency, where t is the denomination of the counterfeit currency; Calculate the proportion of each denomination in each region according to the formula Hj = sum(QjtWjt); Normalize each region according to the total value Nj of each region in the key region Gj, that is, Nj / sum(Nj); Calculate the comprehensive value of each region in the key attention region Gj according to the formula Vj = Hj * (Nj / sum(Nj)); If Vj > Vth, then determine this area as the urgent processing area.

7. The image recognition system for a point counting and banknote checking machine according to claim 1, wherein A threshold Tj is set for each key area of concern. If , it indicates that there are significant fluctuations in the frequency of counterfeit banknotes in this area during this time period.

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