Risk control management method for malicious room occupation in hotel industry

Abstract

The invention discloses a risk control management method for malicious room occupation in the hotel industry, relates to the technical field of hotel risk management and control, and solves the technical problems that the existing malicious room occupation condition is difficult to identify, the identification difficulty is high, and identification defects exist. Various malicious room occupation crowds and behaviors are comprehensively analyzed and processed, pain points which are insufficient in artificial experience and cannot be completely recognized can be avoided, automatic recognition and interception are achieved through the technology, manpower is saved, missing interception and mistaken interception are prevented, malicious room occupation risk recognition and interception can be comprehensively implemented, malicious room occupation risk control can be well achieved, and the risk management efficiency is improved. According to the method, the room occupation judgment information is subjected to index evaluation, and the escape rate and the mistaken killing rate are calculated, so that problems in risk control management can be found in time, a basis is provided for adjusting risk control strategies, continuous optimization of malicious room occupation risk control management is realized, and the risk management capability and the operation efficiency of hotels are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hotel risk management and control, and in particular to a risk control management method for malicious room occupation in the hotel industry. Background Art

[0002] The hotel industry is a broad and popular industry with many brands and huge differences in levels. In the process of hotel management, it is often encountered that malicious users maliciously occupy rooms in a hotel. Through manual or technical means, a large number of reservations are made for the hotel, but the room fees are not paid and no check-in is performed, resulting in the hotel being unable to continue to be sold to other customers, thus preventing the hotel from operating.

[0003] At present, the hotel industry has no open and systematic method to prevent malicious room occupation, nor has it used technical and strategic means to control risks. Usually, the current commonly used means are "home-made methods" of manual identification. When hotel managers see a lot of reservation information for the same customer, they will call the customer to inquire. If the virtual number cannot be connected or the customer does not answer the phone, and cancels the reservation at the check-in time, it is considered a malicious room occupant, and the reservation is manually canceled, and the blacklist function is used if there is a blacklist function.

[0004] The current technical solutions cannot completely avoid the risk of malicious occupation of buildings, and they require a large amount of manpower and rich judgment experience to implement. They are difficult to operate, costly, and cannot completely solve the problem. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a risk control management method for malicious room occupation in the hotel industry, which solves the problem that it is difficult to identify existing malicious room occupation and secondly, the identification is difficult and there are identification defects.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a risk control management method for malicious room occupation in the hotel industry, the method specifically comprising the following steps: Step S001, obtaining historical data, and filtering the historical data to obtain malicious room occupation records; Step S002, analyzing malicious room occupation features according to the acquired malicious room occupation records, calculating malicious room occupation values ​​based on the malicious room occupation features, and generating a judgment interval; Step S003, obtaining user information of the current user, calculating the corresponding judgment value, making a comprehensive judgment based on the judgment interval and the historical data of the current user, and generating room occupancy judgment information, and the room occupancy judgment information includes normal information and risk information; Step S004, performing an index evaluation on the generated room occupancy judgment information, generating analysis results by separately analyzing the escape rate and the false-murder rate, and generating standard index information based on the analysis results.

[0007] As a further solution of the present invention: the specific method of analyzing the malicious room occupation characteristics according to the acquired malicious room occupation records in step S002 is: Obtain malicious room occupation records and label them as i, where i=1, 2, ..., j, where j represents the number of malicious room occupations. Then obtain the registration information corresponding to the malicious room occupation record i, where the registration information includes the completeness of the check-in information and the completeness of the mobile phone number. At the same time, assign values ​​to the two to obtain the check-in information value and the mobile phone number value respectively. Then calculate the sum of the check-in information value and the mobile phone number value and record it as the check-in value; Then, the order situation in the malicious room occupation record i is obtained, and the order value is calculated at the same time. The specific calculation method is: obtain the unit order quantity in the order situation, and obtain the completion degree corresponding to the unit order quantity, and record the obtained completion degree as the order value.

[0008] As a further solution of the present invention: Step S002 calculates the malicious room occupation value based on the malicious room occupation feature, and generates the judgment interval in the following specific manner: The acquired check-in value and order value are summed to obtain the malicious room occupation value, and all malicious room occupation records i are calculated to obtain all malicious room occupation values, and a judgment interval is generated according to the obtained minimum malicious room occupation value and maximum malicious room occupation value.

[0009] As a further solution of the present invention: In step S003, a comprehensive judgment is made by combining the judgment interval and the historical data of the current user to generate the room occupancy judgment information in a specific manner: Obtain the user information of the current user, and calculate the malicious room occupation value of the current user based on the calculation method of step 2, and record it as the judgment value. Then judge the current user to obtain new users and returning users, and analyze the new users and returning users separately.

[0010] As a further solution of the present invention: the specific method of analyzing the new user in step S003 is: The obtained judgment value is matched with the judgment interval. If the judgment value does not exist in the judgment interval, a normal signal is generated. Conversely, if the judgment value exists in the judgment interval, a risk signal is generated.

[0011] As a further solution of the present invention: the specific method of analyzing the returning users in step S003 is: Obtain the historical judgment value corresponding to the returning user, and analyze and calculate the judgment value of the returning user according to whether there is malicious room occupation. If the historical judgment value of the returning user has the risk of malicious room occupation, obtain the number of malicious room occupation risks, calculate the proportion of risk times, and compare the proportion of risk times with the preset value; When the risk frequency ratio is less than the preset value, the analysis is performed based on the judgment value of the regression user. When the risk frequency ratio is greater than the preset value, the product of the risk frequency ratio and the judgment value is calculated to obtain the additional value. At the same time, the sum of the additional value and the judgment value is calculated to obtain the judgment standard, and the analysis is performed based on the judgment standard. If the historical judgment value of the regressed user does not indicate a risk of malicious room occupation, the judgment value is used as the standard for analysis, and the obtained judgment value is matched with the judgment interval, generating normal signals and risk signals at the same time.

[0012] As a further solution of the present invention: In step S004, the specific method of separately analyzing the escape rate and the manslaughter rate to generate the analysis results is: Obtain all room occupancy judgment information within time t, and separately calculate the escape rate and false positive rate within time t. The escape rate is calculated as follows: obtain the escape users per unit time, and obtain the corresponding reserved users per unit time. Substitute the obtained data into the formula: escape rate per unit time = escape users per unit time / all reserved users per unit time * 100%. Then calculate the average of all escape rates per unit time within time t and record it as the average escape rate. The calculation method of the false killing rate is as follows: obtain the false killing users per unit time and the false killing users per unit time, substitute the obtained data into the formula: false killing rate per unit time = false killing users per unit time / all booked users per unit time * 100%, then calculate the average of all false killing rates per unit time within time t and record it as the average escape false killing rate; The calculated mean values ​​of the escape rate and the mean values ​​of the false-murder rate are compared with the corresponding thresholds respectively, and it is determined whether the mean values ​​of the escape rate and the mean values ​​of the false-murder rate exceed the thresholds. If they exceed the thresholds, adjustments are required and adjustment signals are generated. Otherwise, if they do not exceed the thresholds, adjustments are not required and the adjustment signals are analyzed.

[0013] As a further solution of the present invention: the specific method of analyzing the adjustment signal in step S004 is: The corresponding abnormal parameters are obtained, and the corresponding abnormal conditions are obtained at the same time, and a fuzzy range is generated based on the abnormal conditions, and the fuzzy range is integrated with the judgment interval to obtain standard indicator information.

[0014] The present invention provides a risk control management method for malicious room occupation in the hotel industry. Compared with the prior art, it has the following beneficial effects: The present invention comprehensively analyzes and processes various groups of people and behaviors of malicious room occupation by utilizing data analysis and historical user behaviors, thereby avoiding the pain point of insufficient manual experience and inability to fully identify all of them. The technology automatically identifies and intercepts, saving manpower and preventing missed interceptions and erroneous interceptions. The present invention can comprehensively implement risk identification and interception of malicious room occupation, do a good job in risk control of malicious room occupation, perform indicator evaluation on room occupation judgment information, and timely discover problems in risk control management by calculating the escape rate and the false kill rate, thereby providing a basis for adjusting the risk control strategy, realizing continuous optimization of risk control management of malicious room occupation, and improving the risk management capability and operational efficiency of the hotel. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a step method diagram of the present invention; Figure 2 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] For example, see Figure 1 and Figure 2 The present application provides a risk control management method for malicious room occupation in the hotel industry, which specifically includes the following steps: Step S001, obtaining historical data, and filtering the historical data to obtain malicious room occupation records, wherein the malicious room occupation records here indicate that it has been determined that the user's malicious room occupation behavior.

[0018] Step S002: analyzing malicious room occupation features according to the acquired malicious room occupation records, calculating malicious room occupation values ​​based on the malicious room occupation features, and generating a judgment interval.

[0019] Obtain malicious room occupation records and label them as i, where i=1, 2, ..., j, where j represents the number of malicious room occupations. The specific malicious room occupation records are one-to-one corresponding, which means that one user corresponds to one room occupation record. Then obtain the registration information corresponding to the malicious room occupation record i, where the registration information includes the check-in information completion status and the mobile phone number completion status. At the same time, the two are assigned values ​​to obtain the check-in information value and the mobile phone number value respectively, and the assignment here is based on the completion progress. For example, if the check-in information completion progress is 80%, the corresponding assignment value is 8. The mobile phone number is expressed in two situations, one is a correct mobile phone number, which is assigned a value of 1, and the other is an incorrect mobile phone number, which is assigned a value of 2. The incorrect mobile phone number includes a virtual number or a partially virtual number, etc. Then calculate the sum of the check-in information value and the mobile phone number value as the check-in value. For example, the check-in information value is assigned a value of 9, and the mobile phone number value is assigned a value of 1. Then the check-in value obtained by summing the two is 10; Then, the order situation in the malicious room occupation record i is obtained, and the order value is calculated. The specific calculation method is: obtain the unit order quantity in the order situation, and the unit order quantity here represents the total number of orders in one day, and obtain the completion degree corresponding to the unit order quantity, and the completion degree here represents the total order quantity corresponding to the completed order business. For example, if the order quantity is 10 rooms, and the order business corresponds to 2 rooms, the corresponding completion degree is , and record the obtained completion degree as the order value; The acquired check-in value and order value are summed to obtain the malicious room occupancy value, and the malicious room occupancy records i are calculated in this way to obtain all malicious room occupancy values. At the same time, a judgment interval is generated according to the obtained malicious room occupancy values, and the judgment interval here consists of the minimum malicious room occupancy value and the maximum malicious room occupancy value.

[0020] In a specific example, let's take an example: suppose there are three malicious room occupation records, namely record A, record B and record C. For record A, the check-in information value is evaluated as 7 points, and the mobile phone number value is 8 points, so the check-in value is 7+8=15 points. In the order situation, the unit order quantity is 8 rooms, and the completed order business is 3 rooms. The completion rate is 0.375, that is, the order value is 0.375, and the malicious room occupation value is the sum of the check-in value and the order value, that is, 15+0.375=15.375; For record B: the occupancy value is calculated to be 12 points (assuming that the occupancy information value and the mobile phone number value are calculated to be certain values), the unit order quantity is 6 rooms, the completed order business is 1 room, the completion degree is 0.167, and the malicious room occupation value is calculated to be 12+0.167=12.167; For record C: the occupancy value is assumed to be 10 points, the number of unit orders is 12, the number of completed orders is 4, the completion rate is 0.333, and the malicious room occupation value is calculated to be 10+0.333=10.333; After obtaining the malicious room occupation values ​​of these three records, assuming that the minimum malicious room occupation value is 10.333 (record C) and the maximum malicious room occupation value is 15.375 (record A), then the judgment interval is [10.333, 15.375].

[0021] Step S003, obtaining the user information of the current user, and calculating the corresponding judgment value, making a comprehensive judgment based on the judgment interval and the historical data of the current user, and generating room occupancy judgment information, and the room occupancy judgment information includes normal information and risk information.

[0022] The user information of the current user is obtained, and the malicious room occupation value of the current user is calculated based on the calculation method of step 2, and recorded as the judgment value. Then, the current user is judged, and the specific judgment method is as follows: If the current user is a new user, the obtained judgment value is matched with the judgment interval. If the judgment value does not exist in the judgment interval, it means that the current user does not have the risk of malicious room occupation, and a normal signal is generated. On the contrary, if the judgment value exists in the judgment interval, it means that the current user has the risk of malicious room occupation, and a risk signal is generated; If the current user is a returning user, and the returning user here indicates that there is a corresponding room booking record, the historical judgment value corresponding to the returning user is obtained, and the judgment value of the returning user is analyzed and calculated according to whether there is a malicious room occupation situation. If the historical judgment value of the returning user has a risk of malicious room occupation, the number of malicious room occupation risks is obtained, and the risk number ratio is calculated. At the same time, the risk number ratio is compared with the preset value, and the specific value of the preset value here is set by the operator. When the risk number ratio is less than the preset value, the judgment value of the returning user is used as the standard for analysis. When the risk number ratio is greater than the preset value, the product of the risk number ratio and the judgment value is calculated to obtain the additional value, and the sum of the additional value and the judgment value is calculated to obtain the judgment standard, and the analysis is performed based on the judgment standard; If the historical judgment value of the regressed user does not indicate a risk of malicious room occupation, the judgment value is used as the standard for analysis, and the obtained judgment value is matched with the judgment interval, generating normal signals and risk signals at the same time.

[0023] In a specific example, suppose there is a returning user A who has 10 hotel booking records in the past.

[0024] Case 1: Assume that A's historical judgment value shows that there is a risk of malicious occupation, and he has 3 malicious occupation risks. Further calculation shows that the corresponding risk frequency ratio is 0.3. If the preset value is set to 0.4, since 0.3<0.4, A's historical judgment value is used as the standard for analysis. For example, his historical judgment value is 8, and the historical judgment value here is the quantitative value. If the preset value is 0.2, since 0.3>0.2, the additional value is further calculated first, the additional value = 0.3×8=2.4, the sum of the additional value and the judgment value is calculated as 2.4+8=10.4, and then 10.4 is matched with the judgment interval; Case 2: Assume that user A's historical judgment value shows that there is no risk of malicious room occupation. Assume that his judgment value is 66. Match this judgment value 6 with the previously generated judgment interval (for example, the judgment interval is [4, 9]). If 6 is within this interval, a normal signal (indicating that the risk is low or within the normal range) and a risk signal (which may indicate that there are some potential small risk factors, but the overall risk is not large) may be generated based on the specific matching situation, so as to further evaluate and handle user A's situation.

[0025] Embodiment 2, as Embodiment 2 of the present invention, is implemented on the basis of Embodiment 1, and the differences from Embodiment 1 are as follows: Step S004, performing an index evaluation on the generated room occupancy judgment information, generating analysis results by separately analyzing the escape rate and the false-murder rate, and generating standard index information based on the analysis results.

[0026] Obtain all the room occupation judgment information within time t, and calculate the escape rate and false positive rate within time t separately. The calculation method of the escape rate is as follows: obtain the escape users per unit time, and the escape users here are defined as users who are actually malicious customers but have not been successfully intercepted. At the same time, obtain the corresponding booked users per unit time, substitute the obtained data into the formula: escape rate per unit time = escape users per unit time / all booked users per unit time * 100%, and the unit time here is represented by one day, then calculate the average of all escape rates per unit time within time t and record it as the escape rate average. The calculation method of the false positive rate is as follows: obtain the false positive users per unit time and the false positive users per unit time, and the false positive users here are defined as real booked customers but are mistakenly identified as malicious room occupants, substitute the obtained data into the formula: false positive rate per unit time = false positive users per unit time / all booked users per unit time * 100%, then calculate the average of all false positive rates per unit time within time t and record it as the escape false positive rate average. In a specific example, suppose that the statistics and analysis of room occupancy judgment information are conducted within a month. On a certain day, there are 100 booking users, among which there are 5 escape users who are actually malicious customers but have not been intercepted. The unit time escape rate of that day is 5%. Within this month, the unit time escape rates of each day are 3%, 4%, 5%, etc., respectively. Then the mean escape rate is the average of these 30 unit time escape rates, which is assumed to be 4.5% after calculation. Similarly, on a certain day, there are 100 booking users, among which 3 real booking customers are mistakenly identified as malicious room occupants, that is, users who are mistakenly killed. Then the unit time mistaken killing rate of that day is 3%. The unit time mistaken killing rate of each day in a month is 2%, 3%, 4%, etc. respectively. Assume that the calculated average mistaken killing rate is 3.2%.

[0027] The calculated mean escape rate and mean false-murder rate are compared with the corresponding thresholds respectively, and the specific values ​​of the thresholds are set by the operator, and it is judged whether the mean escape rate and the mean false-murder rate exceed the thresholds. If they exceed the thresholds, adjustments are required and adjustment signals are generated. Otherwise, if they do not exceed the thresholds, no adjustments are required. For example, the operator sets the escape rate threshold to 4% and the false-murder rate threshold to 3.5%. After comparison, it is found that the mean escape rate exceeds the threshold, but the mean false-murder rate does not exceed the threshold, so an adjustment signal needs to be generated for the case where the escape rate exceeds the threshold.

[0028] The generated adjustment signal is analyzed to obtain the corresponding abnormal parameters, and the abnormal parameters here are expressed as the mean of the escape rate or the mean of the error rate, and can also be expressed as both exceeding the threshold, and the corresponding abnormal situation is obtained at the same time, and the abnormal situation here is expressed as an abnormality in the identification of escaped users or mistakenly killed users. For example, the current detection rules may not accurately identify certain malicious room occupation behavior characteristics, resulting in some malicious customers not being intercepted. Based on this abnormal situation, a fuzzy range of relevant features of escaped users is generated, for example, there may be a high degree of misjudgment or omission of users in certain specific areas and users using specific payment methods, and a fuzzy range is generated based on the abnormal situation, and the fuzzy range is integrated with the judgment interval to obtain standard indicator information.

[0029] Embodiment 3, as the embodiment 3 of the present invention, focuses on combining the implementation processes of embodiment 1 and embodiment 2 for implementation.

[0030] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0031] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A risk control management method for malicious room occupation in the hotel industry, characterized in that: The method specifically comprises the following steps: Step S001, obtaining historical data, and filtering the historical data to obtain malicious room occupation records; Step S002, analyzing malicious room occupation features according to the acquired malicious room occupation records, calculating malicious room occupation values ​​based on the malicious room occupation features, and generating a judgment interval; Step S003, obtaining user information of the current user, calculating the corresponding judgment value, and making a comprehensive judgment based on the judgment interval and the historical data of the current user to generate room occupancy judgment information, and the room occupancy judgment information includes normal information and risk information; Step S004, performing an index evaluation on the generated room occupancy judgment information, generating analysis results by separately analyzing the escape rate and the false-murder rate, and generating standard index information based on the analysis results.

2. A risk control management method for malicious room occupation in the hotel industry according to claim 1, characterized in that: The specific method of analyzing the malicious room occupation characteristics according to the acquired malicious room occupation records in step S002 is: Obtain malicious room occupation records and label them as i, where i=1, 2, ..., j, where j represents the number of malicious room occupations. Then obtain the registration information corresponding to the malicious room occupation record i, where the registration information includes the completeness of the check-in information and the completeness of the mobile phone number. At the same time, assign values ​​to the two to obtain the check-in information value and the mobile phone number value respectively. Then calculate the sum of the check-in information value and the mobile phone number value and record it as the check-in value; Then, the order situation in the malicious room occupation record i is obtained, and the order value is calculated at the same time. The specific calculation method is: obtain the unit order quantity in the order situation, and obtain the completion degree corresponding to the unit order quantity, and record the obtained completion degree as the order value.

3. A risk control management method for malicious room occupation in the hotel industry according to claim 1, characterized in that: Step S002 calculates the malicious room occupation value based on the malicious room occupation feature and generates a judgment interval in the following specific manner: The acquired check-in value and order value are summed to obtain the malicious room occupation value, and all malicious room occupation records i are calculated to obtain all malicious room occupation values, and a judgment interval is generated according to the obtained minimum malicious room occupation value and maximum malicious room occupation value.

4. A risk control management method for malicious room occupation in the hotel industry according to claim 1, characterized in that: In step S003, a comprehensive judgment is made by combining the judgment interval and the historical data of the current user to generate the room occupancy judgment information in the following specific manner: Get the user information of the current user, and calculate the malicious room occupation value of the current user in the same way as the malicious room occupation value, and record it as the judgment value. Then judge the current user to obtain new users and returning users, and analyze the new users and returning users separately.

5. A risk control management method for malicious room occupation in the hotel industry according to claim 4, characterized in that: The specific method of analyzing the new user in step S003 is: The obtained judgment value is matched with the judgment interval. If the judgment value does not exist in the judgment interval, a normal signal is generated. Conversely, if the judgment value exists in the judgment interval, a risk signal is generated.

6. A risk control management method for malicious room occupation in the hotel industry according to claim 4, characterized in that: The specific method of analyzing the returning users in step S003 is: Obtain the historical judgment value corresponding to the returning user, and analyze and calculate the judgment value of the returning user according to whether there is a malicious room occupation situation. If the historical judgment value of the returning user has a malicious room occupation risk, obtain the number of malicious room occupation risks, calculate the proportion of risk times, and compare the proportion of risk times with the preset value; When the risk frequency ratio is less than the preset value, the analysis is performed based on the judgment value of the regression user. When the risk frequency ratio is greater than the preset value, the product of the risk frequency ratio and the judgment value is calculated to obtain the additional value. At the same time, the sum of the additional value and the judgment value is calculated to obtain the judgment standard, and the analysis is performed based on the judgment standard. If the historical judgment value of the regressed user does not indicate a risk of malicious room occupation, the judgment value is used as the standard for analysis, and the obtained judgment value is matched with the judgment interval, generating normal signals and risk signals at the same time.

7. A risk control management method for malicious room occupation in the hotel industry according to claim 1, characterized in that: The specific method of separately analyzing the escape rate and the manslaughter rate in step S004 to generate the analysis results is as follows: Obtain all room occupancy judgment information within time t, and separately calculate the escape rate and false positive rate within time t. The escape rate is calculated as follows: obtain the escape users per unit time, and obtain the corresponding reserved users per unit time. Substitute the obtained data into the formula: escape rate per unit time = escape users per unit time / all reserved users per unit time * 100%. Then calculate the average of all escape rates per unit time within time t and record it as the average escape rate. The calculation method of the false killing rate is as follows: obtain the false killing users per unit time and the false killing users per unit time, substitute the obtained data into the formula: false killing rate per unit time = false killing users per unit time / all booked users per unit time * 100%, then calculate the average of all false killing rates per unit time within time t and record it as the average escape false killing rate; The calculated mean values ​​of the escape rate and the mean values ​​of the false-murder rate are compared with the corresponding thresholds respectively, and it is determined whether the mean values ​​of the escape rate and the mean values ​​of the false-murder rate exceed the thresholds. If they exceed the thresholds, adjustments are required and adjustment signals are generated. Otherwise, if they do not exceed the thresholds, adjustments are not required and the adjustment signals are analyzed.

8. A risk control management method for malicious room occupation in the hotel industry according to claim 7, characterized in that: The specific method of analyzing the adjustment signal in step S004 is: The corresponding abnormal parameters are obtained, and the corresponding abnormal conditions are obtained at the same time, and a fuzzy range is generated based on the abnormal conditions, and the fuzzy range is integrated with the judgment interval to obtain standard indicator information.

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