Enterprise information management method based on business and financial integrated processing

Abstract

The invention relates to the field of information management, in particular to an enterprise information management method based on business and financial integrated processing, which comprises the following steps: determining an operation danger state according to the request sensitivity and request relevancy of a target user, and determining a safety processing mode according to the operation danger state; in identity verification for a target user and related early-warning users, verification requests are sent to the target user and the related early-warning users according to a preset sequence, an identity verification optimization mode is adjusted under an early-warning response condition, and desensitization processing is carried out for requested enterprise information of the target user. Detecting keyword matching times and keyword sensitivity to determine keyword combination deviation degree, and determining that a desensitization mode is interval desensitization or combined desensitization according to the keyword combination deviation degree; the information management method is adaptively selected according to the actual conditions of the request data of different users, so that the safety management efficiency of enterprise information is improved.

Description

Technical Field

[0001] The present invention relates to the field of information management, and in particular to an enterprise information management method based on business-finance integrated processing. Background Art

[0002] Business and financial integration refers to the unified management of the business and financial activities of an enterprise in an information-based and systematic manner, and the seamless connection between business and finance is achieved through multi-process integration. Among them, the fast extraction and multi-sharing of information in the business and financial integration model are the key to improving the efficiency of enterprise decision-making, but the corresponding disadvantage is that the information security risk also increases accordingly, especially when multiple users request information at the same time, a single information security management method is less effective. Therefore, how to ensure the effective and secure management of enterprise information is a technical problem that technical personnel in this field need to solve urgently.

[0003] Chinese patent publication number CN116846673A discloses an enterprise-level network authorization security management method, including: collecting user behavior trust evidence data and performing standardized processing to obtain standardized user behavior trust evidence data; constructing an enterprise network authorization direct management trusted model, using the standardized user behavior evidence data as model input, and calculating the user behavior trust value weight and the user direct trust value; constructing an enterprise network authorization indirect management trusted model, using the constructed model to obtain the user's historical trust and historical interaction information, and calculating the user's indirect trust value; calculating the user's overall credibility based on the user's direct trust value and indirect trust value; classifying users based on the user's overall credibility value to achieve network authorization security management. It can be seen that the above technical solution has the following problems: it is impossible to adaptively select information management methods according to the actual situation of different users' request data, resulting in poor security management efficiency. Summary of the invention

[0004] To this end, the present invention provides an enterprise information management method based on integrated business and financial processing to overcome the problem in the prior art that it is impossible to adaptively select information management methods according to the actual conditions of different users' request data, resulting in poor security management efficiency.

[0005] To achieve the above-mentioned purpose, the present invention provides an enterprise information management method based on business and financial integrated processing, comprising: Determine the dangerous status of the operation based on the sensitivity and relevance of the target user's request, and determine the safe processing method based on the dangerous status of the operation, such as identity authentication for the target user and related warning users or desensitization of the target user's requested enterprise information; During identity authentication for the target user and the relevant warning user, a verification request is sent to the target user and the relevant warning users in a preset order, and the identity authentication optimization mode is adjusted under the warning response condition. The identity authentication optimization mode is to select the verification image according to the image complexity benchmark value or to adjust the verification image display mode according to the user selection status; In the desensitization process for the target user's requested enterprise information, the number of keyword combinations and keyword sensitivity are detected to determine the keyword combination bias, and the desensitization method is determined as interval desensitization or combined desensitization based on the keyword combination bias.

[0006] Furthermore, the request sensitivity is confirmed by detecting the abnormal value of the mouse operation of the target user and the sensitivity value of the request information; When the mouse operation abnormal value is within the first preset abnormal value range or the request information sensitivity value is within the first preset sensitivity value range, the request sensitivity is determined according to the valid reference difference value; When the mouse operation abnormal value is within the second preset abnormal value range and the request information sensitivity value is within the second preset sensitive value range, the request sensitivity is determined according to the enterprise information confidentiality coefficient.

[0007] Furthermore, the request relevance is confirmed in a manner of obtaining relevant users corresponding to the target user in a recent monitoring period, and recording relevant users whose corresponding request information similarity is greater than a preset request information similarity as relevant warning users; The request relevance is positively correlated with the number of relevant warning users.

[0008] Furthermore, when the operation danger state is that the request sensitivity is within a first preset sensitivity range and the request relevance is within a second preset relevance range, the security processing method is to perform identity authentication for the target user and the relevant warning user.

[0009] Further, the identity authentication optimization mode is adjusted under the early warning response condition. If the image similarity in the effective response verification is greater than the preset image similarity, the identity authentication optimization mode is adjusted to select the verification image according to the image complexity benchmark value; If the image similarity in the valid response verification is less than or equal to the preset image similarity, the identity authentication optimization method is adjusted to adjust the display method of the verification image according to the user selection state; The early warning response condition is that the number of valid responses is greater than the preset number of valid responses, and the image similarity is determined based on color uniformity and feature point distribution.

[0010] Further, detecting the mouse track deviation and the mouse dwell similarity to determine the user selection state; When the user selects that the mouse track deviation is less than or equal to the preset track similarity or the mouse dwell similarity is greater than the preset dwell similarity, the image display mode is adjusted for the display image placement form.

[0011] Furthermore, the verification image is selected according to the image complexity benchmark value, including: The method for determining the image complexity benchmark value is determined based on the area ratio of the verification area; If the area ratio of the verification area is less than the preset area ratio of the verification area, the image complexity reference value is determined by determining the image complexity reference value according to the difference between the verification area and the non-verification area; If the verification area ratio is greater than or equal to the preset verification area ratio, the image complexity reference value is determined by the number of feature points in the verification area. The verification images are selected in descending order according to the image complexity benchmark values.

[0012] Furthermore, when the operation danger state is that the request sensitivity is within the second preset sensitivity range and the request relevance is within the first preset relevance range, the security processing method is to perform identity authentication for the target user and the relevant warning user or to perform desensitization processing on the target user's request enterprise information. The keyword combination bias is determined based on the number of keyword combinations and keyword sensitivity; The keyword collocation times is the average value of the maximum collocation times corresponding to each keyword; Keyword sensitivity is the average number of keywords corresponding to each sub-paragraph.

[0013] Furthermore, when the keyword combination bias is less than the preset keyword combination bias, the desensitization method is interval desensitization; There are at least a preset number of characters between the keywords to be desensitized in the interval desensitization, and the preset number is negatively correlated with the keyword density.

[0014] Furthermore, when the keyword combination bias is greater than or equal to the preset keyword combination bias, the desensitization method is combined desensitization; In combined desensitization, keywords whose keyword matching times are greater than the preset keyword matching times are desensitized.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the operation danger state is determined according to the request sensitivity and request relevance of the target user, and the security processing method is determined according to the operation danger state. The operation danger state is used to reflect whether the target user currently has dangerous behavior, and the risk of multiple users stealing information is confirmed according to the request relevance, so that the selection of the security processing method is more in line with the actual work scenario, thereby improving the security management efficiency of enterprise information.

[0016] Furthermore, in the present invention, during identity authentication for the target user and the related warning users, verification requests are sent to the target user and the related warning users in a preset order. By setting the preset order, the identity authentication for possibly associated users is optimized, thereby avoiding the problem of poor verification efficiency caused by the prior art of only verifying the operation behavior of a single user.

[0017] Furthermore, in the present invention, a method for determining an image complexity benchmark value is based on the area ratio of the verification region of the verification image. The determination of the image complexity benchmark value is based on the characteristics of the actual verification image, thereby avoiding the problem of the single verification image selection method in the prior art being unable to meet actual verification needs, thereby improving the security management efficiency of the enterprise information of the present invention.

[0018] Furthermore, the keyword combination bias in the present invention determines the desensitization method as interval desensitization or combined desensitization, and the keyword combination bias is used to reflect the keyword status in the text of the enterprise information requested by the current user, and correspondingly desensitizes sensitive keywords to ensure the security of sensitive information. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the enterprise information management method based on business and financial integrated processing of the present invention; Figure 2 A flow chart of a method for confirming the sensitivity of a request for the present invention; Figure 3 This is a flow chart of adjusting the identity authentication optimization method under the early warning response conditions of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0023] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] See also Figures 1 to 3 As shown, the present invention provides an enterprise information management method based on business and financial integrated processing, comprising: Determine the dangerous status of the operation based on the sensitivity and relevance of the target user's request, and determine the safe processing method based on the dangerous status of the operation, such as identity authentication for the target user and related warning users or desensitization of the target user's requested enterprise information; During identity authentication for the target user and the relevant warning user, a verification request is sent to the target user and the relevant warning users in a preset order, and the identity authentication optimization mode is adjusted under the warning response condition. The identity authentication optimization mode is to select the verification image according to the image complexity benchmark value or to adjust the verification image display mode according to the user selection status; In the desensitization process for the target user's requested enterprise information, the number of keyword combinations and keyword sensitivity are detected to determine the keyword combination bias, and the desensitization method is determined as interval desensitization or combined desensitization based on the keyword combination bias.

[0025] The application scenario of the present invention is information security management of enterprise websites. The target users in the present invention are users who are performing information request operations on the enterprise websites. The present invention will determine the security processing method for each target user respectively. The present invention applies a cyclic monitoring period. At the end of each monitoring period, a security processing method is determined for the target user. The duration of a single monitoring period can be set by the user. The greater the user's efficiency in information security management, the shorter the duration of the monitoring period. In the specific implementation of the present invention, the monitoring period is 5 minutes.

[0026] The present invention applies several historical records, and any historical record records the request sensitivity, request relevance, mouse operation abnormality, request information sensitivity value, request information similarity, image similarity, number of valid responses, mouse track deviation, mouse dwell similarity, image complexity benchmark value, verification area ratio, keyword combination bias and various weight coefficients in an enterprise information management process, and each historical record corresponds to a qualified mark, which records whether the corresponding enterprise information management process meets the needs of the management personnel. The qualified mark is recorded manually, and the management personnel are the personnel who execute enterprise information management in the enterprise.

[0027] Specifically, the request sensitivity is confirmed by detecting the abnormal value of the mouse operation of the target user and the sensitivity value of the request information; When the mouse operation abnormal value is within the first preset abnormal value range or the request information sensitivity value is within the first preset sensitivity value range, the request sensitivity is determined according to the valid reference difference value; When the mouse operation abnormal value is within the second preset abnormal value range and the request information sensitivity value is within the second preset sensitive value range, the request sensitivity is determined according to the enterprise information confidentiality coefficient.

[0028] Among them, the mouse operation abnormal value = mouse search frequency × (mouse dwell time / duration of a single monitoring cycle), the mouse search frequency is the number of times the target user's mouse clicks the page hyperlink in the most recent complete monitoring cycle, the mouse dwell time is the sum of the time the mouse position remains fixed in the most recent complete monitoring cycle, and the requested information sensitivity value is the number of sensitive hyperlinks requested by the target user in the most recent complete monitoring cycle. The sensitive hyperlink can be freely determined in advance according to the confidentiality level of the information corresponding to the hyperlink. It can be understood that a hyperlink can be used to jump to a website or obtain information. It is easy for technicians in this field to understand whether the corresponding hyperlink is a sensitive hyperlink based on the confidentiality level of the website or information, and it will not be elaborated here.

[0029] The values ​​within the first preset abnormal value range are all greater than the preset abnormal value, the values ​​within the second preset abnormal value range are all less than or equal to the preset abnormal value, the values ​​within the first preset sensitive value range are all greater than the preset sensitive value, and the values ​​within the second preset sensitive value range are all less than or equal to the preset sensitive value. The preset abnormal value and the preset sensitive value can be set by the administrator according to the actual application scenario. It can be understood that the greater the administrator's demand for information security management efficiency, the smaller the preset sensitive value and the preset abnormal value. A value selection method is provided, and the administrator can extract the mouse operation abnormal value and the request information sensitive value corresponding to the historical records that meet the administrator's needs, calculate the average value of the mouse operation abnormal value and the average value of the request information sensitive value, and record them as the preset abnormal value and the preset sensitive value, respectively.

[0030] When the request sensitivity is determined based on the effective reference difference, the absolute value of the difference between the mouse operation abnormality value and the preset abnormality value and the absolute value of the difference between the request information sensitivity and the preset sensitivity are detected, and the larger value is recorded as the effective reference difference. The effective reference difference is positively correlated with the request sensitivity.

[0031] When the request sensitivity is determined based on the enterprise information confidentiality coefficient, the enterprise information confidentiality coefficient is the maximum value of the request information sensitivity values ​​corresponding to the most recent three monitoring cycles, and the enterprise information confidentiality coefficient is positively correlated with the request sensitivity.

[0032] Specifically, the request relevance is confirmed in a manner of obtaining relevant users corresponding to the target user in the most recent monitoring period, and recording relevant users whose corresponding request information similarity is greater than a preset request information similarity as relevant warning users; The request relevance is positively correlated with the number of relevant warning users.

[0033] For a single target user, the corresponding related users are users who make information requests on the enterprise website during the same monitoring period as the target user; For the target user and another user, the corresponding request information similarity = (mouse search frequency difference / preset mouse search frequency) + (number of identical keywords / preset number of identical keywords), the mouse search frequency difference is the absolute value of the difference between the mouse search frequencies of the target user and another user, the number of identical keywords is the total number of identical keywords in the request information corresponding to the target user and another user in the most recent monitoring cycle, the request information is the text information requested by the user through the enterprise website, the preset mouse search frequency and the preset number of identical keywords can be set by the administrator according to the actual application scenario, and the mouse search frequency and the number of identical keywords corresponding to the historical records that meet the needs of the administrator are respectively cleaned to remove the outliers. The method for confirming the outliers can be the Z-Score method or the IQR method, and the average values ​​corresponding to the mouse search frequency and the number of identical keywords after removing the outliers are respectively recorded as the preset mouse search frequency and the value of the preset mouse search frequency, and the preset value can be optimized through a deep learning model, which is easy for technicians in this field to understand and will not be elaborated here.

[0034] Specifically, when the operation danger state is that the request sensitivity is within the first preset sensitivity range and the request relevance is within the second preset relevance range, the security processing method is to authenticate the target user and the relevant warning user; The preset order is to record the target user as the first verified user, and to record the related warning user whose user address difference corresponding to the i-th verified user is the largest and who is not recorded as a verified user as the i+1-th verified user, i=1, 2, 3, ..., N, where N is the total number of target users and related warning users.

[0035] For the target user and any corresponding related warning user, the method for confirming the user address difference between the two users is to obtain the IP addresses of the two users, resolve the actual address distance between the two users through the IP addresses, and record it as the user address difference. The values ​​within the first preset sensitivity range are all smaller than the preset request sensitivity, the values ​​within the second preset sensitivity range are all greater than or equal to the preset request sensitivity, the values ​​within the first preset relevance range are all smaller than the preset request relevance, the values ​​within the second preset relevance range are all greater than or equal to the preset request relevance, and the administrator can set the values ​​of the preset request sensitivity and the preset request relevance according to the actual application scenario. For the request sensitivity and request relevance corresponding to the historical records that meet the needs of the administrator, data cleaning is performed to remove outliers, and the average values ​​of the request sensitivity and request relevance after removing the outliers are recorded as the values ​​of the preset request sensitivity and the preset request relevance, respectively.

[0036] Specifically, the identity authentication optimization method is adjusted under the early warning response condition. If the image similarity in the effective response verification is greater than the preset image similarity, the identity authentication optimization method is adjusted to select the verification image according to the image complexity benchmark value; If the image similarity in the valid response verification is less than or equal to the preset image similarity, the identity authentication optimization method is adjusted to adjust the verification image display method according to the user selection state; The early warning response condition is that the number of valid responses is greater than the preset number of valid responses, and the image similarity is determined based on color uniformity and feature point distribution.

[0037] Image similarity = color uniformity × α1 + feature point distribution × α2, wherein a verification image corresponding to a single valid response of a single user is extracted, and for each verification image, the pixel value of the verification image is mapped to a discrete interval, called a color space. Common color spaces include RGB, HSV, and Lab. The color space used in the present invention is RGB. A counter array with a length of 256 is created for each channel, and all values ​​are initialized to 0. Each pixel in the image is traversed to obtain the RGB value of the pixel. For each pixel's RGB value, the value of the corresponding index in the counter array of the corresponding channel is added by 1, and the color frequency is divided by the total number of pixels in the image to obtain a color histogram. Each verification image corresponds to a color histogram, and the Euclidean distance between each two color histograms is calculated. The average value of all the calculated Euclidean distances is recorded as the sub-color uniformity corresponding to the valid response, and the average value of the sub-color uniformity corresponding to each valid response is recorded as the color uniformity. The feature point distribution is confirmed by using an ORB detector to detect feature points in each verification image. For each verification image, an ORB detector is established that can include The smallest circular area that includes all feature points is recorded as the density value corresponding to the verification image. The distribution degree of feature points = the difference between the maximum density value corresponding to all verification images and the minimum density value. α1 is the first weight coefficient, and α2 is the second weight coefficient. The values ​​of α1 and α2 can be determined according to historical experience and actual work scenarios. A value selection method is provided to extract historical records that meet the needs of managers. The corresponding average value of α1 is recorded as the value of the current first weight coefficient, and the corresponding average value of α2 is recorded as the value of the current second weight coefficient. The value of the preset image similarity can be set according to the actual application scenario. The greater the manager's demand for the accuracy of enterprise security management, the smaller the value of the preset image similarity is, so as to improve the defense against relevant warning users and reduce the impact of using multiple IP addresses for information crawling on enterprise information security. A method for selecting the preset image similarity is provided. Data cleaning is performed on the image similarity corresponding to the historical records that meet the needs of managers to remove outliers, and the average values ​​corresponding to the image similarities after removing outliers are recorded as preset image similarities.

[0038] Specifically, the mouse trajectory deviation and mouse dwell similarity are detected to determine the user selection state; When the user selects that the mouse track deviation is less than or equal to the preset track similarity or the mouse dwell similarity is greater than the preset dwell similarity, the image display mode is adjusted for the display image placement form.

[0039] Adjust the display image placement, including: Detect the first difference and the second difference, the first difference = the preset track similarity - the mouse track deviation, the second difference = the mouse stay similarity - the preset stay similarity, take the larger value of the first difference and the second difference as the basis difference, and increase and adjust only the number of verification times and the number of verification images in a single image verification according to the basis difference; The increase in the number of verifications is positively correlated with the basis difference; The increase in the number of verification images is positively correlated with the basis difference.

[0040] In the present invention, enterprise personnel can set the initial value of the number of verification times and the number of verification images in a single image verification. The number of verification times is the number of image verifications for the target user, and the number of verification images in a single image verification is the total number of images that need to be identified by the management personnel and the images that interfere with the identification.

[0041] The method for confirming the mouse trajectory deviation of a single user is to detect the mouse execution trajectory of the user within the preset monitoring time, monitor the mouse movement and mouse clicks through JavaScript, and record the mouse movement trajectory corresponding to every two adjacent mouse clicks as the mouse execution trajectory. The mouse trajectory deviation is S, and the calculation formula of S is:

[0042] Wherein, Cu is the movement parameter corresponding to the u-th mouse execution trajectory, C0 is the average value of all movement parameters, u=1,2,3,...,M, M is the total number of mouse execution trajectories corresponding to the user, Cu=Vu×β1+Au×β2+Yu×β3, wherein Vu is the maximum mouse movement speed corresponding to the u-th mouse execution trajectory, Au is the maximum mouse acceleration corresponding to the u-th mouse execution trajectory, Yu is the maximum mouse movement area corresponding to the u-th mouse execution trajectory, and the confirmation method of the maximum mouse movement area is to establish a maximum number of mouse execution trajectories including the u-th mouse execution trajectory. A small circle is formed, and the area of ​​the circle is recorded as the maximum mouse movement area. β1 is the first movement weight coefficient, β2 is the second movement weight coefficient, and β3 is the third movement weight coefficient. For the values ​​of β1, β2 and β3, managers can extract historical records that meet the needs of managers. The corresponding average value of β1 is recorded as the current value of the first movement weight coefficient, the corresponding average value of β2 is recorded as the current value of the second movement weight coefficient, and the corresponding average value of β3 is recorded as the current value of the third movement weight coefficient. In the embodiment of the present invention, β1=0.4, β2=0.3, and β3=0.3.

[0043] The confirmation method of the mouse dwell similarity is to detect the mouse execution trajectory of the user within the preset monitoring time corresponding to the user, detect the maximum dwell time of the mouse corresponding to each mouse execution trajectory at a position, calculate the average value of all maximum dwell times, record it as a reference average value, detect the absolute value of the difference between each maximum dwell time and the reference average value, record it as the dwell difference, record the maximum value of the dwell difference as the mouse dwell similarity, and the preset trajectory deviation and the preset mouse dwell similarity values ​​can be set by the administrator according to the actual application scenario. Among them, for the mouse trajectory deviation and the mouse dwell similarity corresponding to the historical records that meet the needs of the administrator, data cleaning is performed to remove the outliers therein, and the average values ​​corresponding to the mouse trajectory deviation and the mouse dwell similarity after removing the outliers are respectively recorded as the preset trajectory deviation and the preset mouse dwell similarity values. In the embodiment of the present invention, the preset mouse trajectory deviation is 20% of C0, and the preset mouse dwell similarity is 25% of the reference average value.

[0044] Specifically, the verification images are selected based on the image complexity benchmark value, including: Determine the image complexity benchmark value according to the verification area ratio of the verification image; If the area ratio of the verification area is less than the preset area ratio of the verification area, the image complexity reference value is determined by determining the image complexity reference value according to the difference between the verification area and the non-verification area; If the verification area ratio is greater than or equal to the preset verification area ratio, the image complexity reference value is determined by the number of feature points in the verification area. The verification images are selected in descending order according to the image complexity benchmark values.

[0045] For any verification image, the verification area is the image area in the verification image that needs to be verified, and the non-verification area is the image area other than the verification area in the verification image. The verification area area ratio = the area of ​​the verification area / the area of ​​the verification area + the area of ​​the non-verification area. The value of the preset verification area area ratio is performed. The verification area area ratio corresponding to the historical records that meet the needs of management personnel is cleaned to remove outliers. After removing the outliers, the average values ​​corresponding to the verification area area ratios are recorded as preset values ​​of the verification area area ratios. In the embodiment of the present invention, the preset verification area area ratio is 25%.

[0046] Among them, the method for confirming the difference between the verification area and the non-verification area is to detect the feature points of the non-verification area, calculate the average value L1 of the minimum distance between each feature point and the verification area, and calculate the maximum distance L2 between the feature points. The difference between the verification area and the non-verification area = L1-L2, and the difference is recorded as the image complexity reference value. It can be understood that the smaller the value of L1 and the larger the value of L2, the greater the difficulty in identifying the feature points of the non-verification area. The present invention reflects the difficulty of identifying the non-verification area through the difference between the verification area and the non-verification area, so that the verification image determined according to the image complexity reference value can effectively enable malicious web crawlers to crawl corporate information.

[0047] When the image complexity reference value is determined according to the number of feature points in the verification area, the image complexity reference value = the number of feature points in the verification area; The recognition of feature points in the present invention is performed by an ORB detector, the image to be processed is loaded and converted into a grayscale format, the ORB detector is initialized, and the feature points in the image are detected using the detection parameters of the ORB detector that meet the needs of the management personnel. This is content that is easy to understand for those skilled in the art and will not be elaborated here.

[0048] Specifically, the keyword combination bias is determined based on the number of keyword collocations and keyword sensitivity; The keyword collocation times is the average value of the maximum collocation times corresponding to each keyword; Keyword sensitivity is the average number of keywords corresponding to each sub-paragraph.

[0049] Keyword combination bias = keyword collocation times / preset keyword collocation times × ζ1 + keyword sensitivity / preset keyword sensitivity × ζ2; Among them, the values ​​of the preset number of keyword combinations and the preset keyword sensitivity can be determined by the management personnel according to the actual text length of the enterprise information request. The larger the text length, the greater the security efficiency of enterprise management, and the larger the values ​​of the preset number of keyword combinations and the preset keyword sensitivity. The average value can also be calculated through historical records to obtain the values ​​of the preset number of keyword combinations and the preset keyword sensitivity, or as a reference, ζ1 is the first keyword weight coefficient, ζ2 is the second keyword weight coefficient, and the management personnel can extract historical records that meet the needs of the management personnel, and record the corresponding average value of ζ1 as the value of the current first keyword weight coefficient, and record the corresponding average value of ζ2 as the value of the current second keyword weight coefficient. It can be understood that enterprise managers can also obtain the value of the preset threshold based on historical experience and model learning based on big data. This is content that is easy for technical personnel in this field to understand and will not be elaborated here. Provided are values ​​of ζ1 and ζ2, ζ1=0.7, ζ2=0.3.

[0050] The target user's requested enterprise information is the text data of the enterprise information requested by the target user. In the present invention, keywords are sensitive words in the text data that need to be protected. Keywords are set in advance by the company's management personnel. For a single request for enterprise information from the target user, all keywords corresponding to the requested enterprise information are recorded as keywords to be analyzed. For a single keyword to be analyzed, the number of times it appears in the same text data with other keywords to be analyzed is recorded as the number of matches. A sub-paragraph is a field where the characters between two adjacent separators are recorded as a separator, and the separator is a period or a comma.

[0051] Specifically, when the keyword combination bias is less than the preset keyword combination bias, the desensitization method is interval desensitization; There are at least a preset number of characters between the keywords to be desensitized in the interval desensitization, and the preset number is negatively correlated with the keyword density.

[0052] In interval desensitization, keywords are desensitized in the text reading order. If there is no keyword before a keyword in the text reading order or there are greater than or equal to a preset number of characters between it and the nearest desensitized keyword in the text reading order, the keyword is desensitized. The characters include Chinese characters, letters and symbols.

[0053] Specifically, when the keyword combination bias is greater than or equal to the preset keyword combination bias, the desensitization method is combined desensitization; In combined desensitization, keywords whose keyword matching times are greater than the preset keyword matching times are desensitized.

[0054] The value of the preset keyword combination bias is selected, and data cleaning is performed on the keyword combination bias corresponding to the historical records that meet the needs of management personnel to remove outliers, and the average values ​​corresponding to the keyword combination bias after removing the outliers are recorded as the preset keyword combination bias.

[0055] The desensitization method in the present invention is determined by the management personnel. The desensitization method may be replacement, deletion, rearrangement or noise addition, which is easily understood by those skilled in the art and will not be elaborated here.

[0056] If the operation risk status is that the request sensitivity is within the second preset sensitivity range and the request relevance is within the second preset relevance range, the enterprise performs manual review on the target user; If the operation risk state is that the request sensitivity is within the first preset sensitivity range and the request relevance is within the first preset relevance range, there is no need to determine the safe processing method.

[0057] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An enterprise information management method based on business and financial integrated processing, characterized in that: include: Determine the dangerous status of the operation based on the sensitivity and relevance of the target user's request, and determine the safe processing method based on the dangerous status of the operation, such as identity authentication for the target user and related warning users or desensitization of the target user's requested enterprise information; During identity authentication for the target user and the relevant warning user, a verification request is sent to the target user and the relevant warning users in a preset order, and the identity authentication optimization mode is adjusted under the warning response condition. The identity authentication optimization mode is to select the verification image according to the image complexity benchmark value or to adjust the verification image display mode according to the user selection status; In the desensitization process for the target user's requested enterprise information, the number of keyword combinations and keyword sensitivity are detected to determine the keyword combination bias, and the desensitization method is determined as interval desensitization or combined desensitization based on the keyword combination bias.

2. The enterprise information management method based on business and financial integrated processing according to claim 1 is characterized in that: The request sensitivity is confirmed by detecting the abnormal value of the mouse operation of the target user and the sensitivity value of the request information; When the mouse operation abnormal value is within the first preset abnormal value range or the request information sensitivity value is within the first preset sensitivity value range, the request sensitivity is determined according to the valid reference difference value; When the mouse operation abnormal value is within the second preset abnormal value range and the request information sensitivity value is within the second preset sensitive value range, the request sensitivity is determined according to the enterprise information confidentiality coefficient.

3. The enterprise information management method based on business and financial integrated processing according to claim 2 is characterized in that: The method for confirming the request relevance is to obtain relevant users corresponding to the target user in the most recent monitoring period, and record the relevant users whose corresponding request information similarity is greater than the preset request information similarity as relevant warning users; The request relevance is positively correlated with the number of relevant warning users.

4. The enterprise information management method based on business and financial integrated processing according to claim 3 is characterized in that: When the operation danger state is that the request sensitivity is within the first preset sensitivity range and the request relevance is within the second preset relevance range, the security processing method is to perform identity authentication for the target user and the relevant warning users.

5. The enterprise information management method based on business and financial integrated processing according to claim 4 is characterized in that: Adjust the identity authentication optimization mode under the early warning response condition. If the image similarity in the effective response verification is greater than the preset image similarity, the identity authentication optimization mode is adjusted to select the verification image according to the image complexity benchmark value; If the image similarity in the valid response verification is less than or equal to the preset image similarity, the identity authentication optimization method is adjusted to adjust the verification image display method according to the user selection state; The early warning response condition is that the number of valid responses is greater than the preset number of valid responses, and the image similarity is determined based on color uniformity and feature point distribution.

6. The enterprise information management method based on business and financial integrated processing according to claim 5 is characterized in that: Detect the mouse track deviation and mouse dwell similarity to determine the user selection state; When the user selects that the mouse track deviation is less than or equal to the preset track similarity or the mouse dwell similarity is greater than the preset dwell similarity, the image display mode is adjusted for the display image placement form.

7. The enterprise information management method based on business and financial integrated processing according to claim 6 is characterized in that: The verification images are selected based on the image complexity benchmark value, including: The method for determining the image complexity benchmark value is determined based on the area ratio of the verification area; If the area ratio of the verification area is less than the preset area ratio of the verification area, the image complexity reference value is determined by determining the image complexity reference value according to the difference between the verification area and the non-verification area; If the verification area ratio is greater than or equal to the preset verification area ratio, the image complexity reference value is determined by the number of feature points in the verification area. The verification images are selected in descending order according to the image complexity benchmark values.

8. The enterprise information management method based on business and financial integrated processing according to claim 7 is characterized in that: When the operation danger state is that the request sensitivity is within the second preset sensitivity range and the request relevance is within the first preset relevance range, the security processing method is to authenticate the target user and the relevant warning user or desensitize the target user's request enterprise information. The keyword combination bias is determined based on the number of keyword combinations and keyword sensitivity; The keyword collocation times is the average value of the maximum collocation times corresponding to each keyword; Keyword sensitivity is the average number of keywords corresponding to each sub-paragraph.

9. The enterprise information management method based on business and financial integrated processing according to claim 8 is characterized in that: When the keyword combination bias is less than the preset keyword combination bias, the desensitization method is interval desensitization; There are at least a preset number of characters between the keywords to be desensitized in the interval desensitization, and the preset number is negatively correlated with the keyword density.

10. The enterprise information management method based on business and financial integration processing according to claim 9 is characterized in that: When the keyword combination bias is greater than or equal to the preset keyword combination bias, the desensitization method is combined desensitization; In combined desensitization, keywords whose keyword matching times are greater than the preset keyword matching times are desensitized.

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