Hotel intelligent service system based on AI big data

Abstract

The invention discloses a hotel intelligent service system based on AI big data, which relates to the technical field of intelligent service systems and comprises a signal processing module, a processing gain module, a conflict detection module, a conflict analysis module, a conflict regulation and control module and a database. According to the deviation rate of the processing gain value and the conflict deviation value, calculating a conflict value to obtain different types of specific instruction identification conflicts, and according to a conflict analysis result, performing corresponding selection operation for three different regulation and control modes. Through three different regulation and control modes of starting a distributed standby instruction recognizer, replacing a spreading code or starting different frequencies to receive an instruction signal command, adverse effects caused by instruction conflicts of the hotel intelligent service system are reduced, and the use effect and the popularization effect of the hotel intelligent service system based on the AI big data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent service systems, and in particular to an intelligent hotel service system based on AI big data. Background Art

[0002] As the core trend of the booming modern hotel industry, the hotel intelligent service system based on AI big data has cleverly integrated a series of cutting-edge technologies such as artificial intelligence, big data, and the Internet of Things. This intelligent service system aims to provide guests with a customized and efficient service experience by accurately grasping customer needs.

[0003] However, in actual operation, the system often needs to integrate multiple complex subsystems and devices, covering smart room control, front desk reception, security management and many other aspects. These subsystems and devices need to maintain a high degree of coordination to ensure efficient and smooth service processes. However, the complexity of system design sometimes leads to delays or deviations in the transmission and processing of instructions. Especially when faced with multiple concurrent instructions or requests, it may be difficult for the system to respond accurately and immediately to each instruction, which in turn causes service interruptions, delays or execution errors. =, that is, instruction recognition conflicts, which not only damages the customer's experience, but also hinders the widespread application and promotion of hotel intelligent service systems based on AI big data to a certain extent. Summary of the invention

[0004] In response to the above situation, the present invention can detect the command recognition conflict status in real time, and determine the different types of command recognition conflicts based on the multiple parameters of the detected command recognition conflicts, and make corresponding processing in time to reduce the adverse effects of the hotel intelligent service system caused by the command conflict.

[0005] The technical solution includes a signal processing module, a processing gain module, a conflict detection module, a conflict analysis module, a conflict control module, and a database. The signal processing module analyzes the command signal sent by the customer, calculates and extracts the spread spectrum code rate RC, data bit rate RB, channel utilization rate X, recognition response rate W, and error rate C in the command signal;

[0006] The processing gain module calculates the processing gain value PG, PG = 10Lg (RC / RB), and extracts the parameter range NPG ~ MPG corresponding to the processing gain value PG in the database, and then calculates the excess value PG1 of the processing gain value relative to the PG parameter range NPG ~ MPG, and finally calculates the deviation rate PG2 of the processing gain value, PG2 = PG1 / [(NPG + MPG) / 2];

[0007] The conflict detection module extracts the corresponding channel utilization threshold X1, recognition response rate threshold W1, and error rate threshold C1 from the database, and then calculates the deviation rate X2 of the channel utilization, X2 = (X1-X) / X1;

[0008] Identify the deviation rate of the response rate W2, W2 = (W1-W) / W1;

[0009] Deviation rate of error rate C2, C2 = (C-C1) / C1;

[0010] Finally, the conflict deviation value Q is calculated, Q = X2×0.3+W2×0.4+C2×0.3+ζ, where ζ represents the calibration value of the conflict deviation;

[0011] The conflict analysis module calculates the conflict value according to the deviation rate of the processing gain value and the conflict deviation value, and then compares the conflict value, the deviation rate of the processing gain value and the conflict deviation value with the corresponding threshold value, and finally obtains three different types of instruction conflict analysis results after comparison: instruction recognition conflict, signal transmission interference, and instruction recognition slight conflict;

[0012] The conflict control module receives three different types of conflict analysis results, and selects different control methods to alleviate conflicts according to the conflict analysis results. The different control methods include starting a distributed standby instruction identifier, changing a spread spectrum code, or enabling different frequencies to receive instruction commands.

[0013] Further, the conflict analysis module calculates the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict, and Y is greater than 0.2, indicating that an instruction recognition conflict occurs, otherwise, it is normal;

[0014] If PG2 is greater than 0.1, it means that the signal transmission is disturbed, otherwise, it is normal;

[0015] On the premise that both Y and PG2 are normal, the conflict deviation value Q is greater than 0.3, indicating that the instruction recognition has a slight conflict; otherwise, it is normal.

[0016] Further, when the conflict control module receives a command to identify a conflict, it starts a distributed standby command identifier to perform distributed processing on the command signal;

[0017] When the signal transmission is interfered, the spread spectrum code is replaced, the reflected signal is spread spectrum processed, and the processing gain value PG is adjusted until it is normal;

[0018] When there is a slight conflict in command recognition, multiple different frequency channels are enabled to distinguish different command signals, and the corresponding recognition method is selected according to the different frequencies of the received command signals.

[0019] Furthermore, the steps for calculating the weight coefficient in the conflict deviation value Q formula are as follows;

[0020] S1, when other environmental factors remain unchanged, the channel utilization rate and recognition response rate factors are set unchanged, and the error rate is collected in groups of 10 data;

[0021] Similarly, two sets of data on channel utilization and recognition response rate are collected respectively;

[0022] S2, unify the three sets of data into the deviation rate of channel utilization, the deviation rate of recognition response rate, and the deviation rate of error rate;

[0023] S3, taking the instruction recognition efficiency of the system under normal operation in the same environment as the reference value, calculate the influence of the error rate deviation rate change on the instruction recognition efficiency when the deviation rate of the channel utilization rate and the deviation rate of the recognition response rate are the same; similarly calculate the influence of the deviation rate of the channel utilization rate and the deviation rate of the recognition response rate respectively;

[0024] A set of weight coefficients is initially set according to three different impact values;

[0025] S4, repeat steps S1-S3, the AI ​​big data model continuously iteratively adjusts the weight coefficient until the adjustment of the weight coefficient has little effect on the instruction recognition efficiency, and finally determines the weight coefficient and calibration value, and obtains the formula of the conflict deviation value Q, Q = X2 × 0.3 + W2 × 0.4 + C2 × 0.3 + ζ, where ζ represents the calibration value of the conflict deviation. Furthermore, the conflict analysis module processes the conflict value Y and the deviation rate PG2 with a higher priority than the conflict deviation value Q. Only when Y and PG2 are normal will the conflict deviation value Q be further judged. The conflict value Y and the deviation rate PG2 have the same priority.

[0026] Furthermore, when the processing gain value PG is within the parameter range NPG to MPG, the excess value PG1 is 0 and the deviation rate PG2 of the processing gain value is 0.

[0027] Furthermore, the channel utilization rate refers to the proportion of time that the channel between the hotel client and the command recognizer is effectively utilized within a certain period of time;

[0028] The recognition response rate represents the rate at which the command recognizer recognizes commands;

[0029] The error rate indicates the error rate of the command recognizer in recognizing the command signal.

[0030] Furthermore, the weight coefficient in the formula of the conflict value Y is obtained by collecting the deviation rate of the processing gain value and the conflict deviation value, simulating the impact of different weight coefficients on the instruction recognition performance through simulation experiments, and finally determining the weight coefficient and calibration value to obtain the formula of the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict.

[0031] Furthermore, when the conflict control module receives normal signals, the distributed instruction identifier does not work, and when working normally, one frequency channel can meet the demand.

[0032] Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art:

[0033] 1. By calculating the processing gain value and the conflict deviation value, and calculating the conflict value according to the deviation rate of the processing gain value and the conflict deviation value, the different types of specific command recognition conflicts are obtained, and corresponding selection operations are made for three different control methods according to the conflict analysis results. By starting the distributed standby command identifier, changing the spread spectrum code, or enabling different frequencies to receive command signal commands, the three different control methods can reduce the adverse effects of the hotel intelligent service system caused by command conflicts, and improve the use and promotion effects of the hotel intelligent service system based on AI big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a module diagram of the hotel intelligent service system based on AI big data of the present invention.

[0035] Figure 2 This is a module flow chart of the hotel intelligent service system based on AI big data of the present invention. DETAILED DESCRIPTION

[0036] The above and other technical contents, features and effects of the present invention are described in detail below with reference to the attached Figure 1 to Figure 2 The detailed description of the embodiments will clearly show that the structural contents mentioned in the following embodiments are all based on the drawings in the specification.

[0037] In order to detect the command recognition conflict status in real time, and determine the different types of command recognition conflicts according to various parameters of the command recognition conflict, and make corresponding processing in time, so as to reduce the adverse effects of the hotel intelligent service system caused by the command conflict, the existing hotel intelligent service system based on AI big data only uses one of the three different control methods to control the command recognition conflict, namely, starting the distributed standby command identifier, changing the spread spectrum code, or enabling different frequencies to receive command signal commands. However, different control methods have different effects on different degrees of command recognition conflict. The reason why it is impossible to carry out targeted control on the degree of command recognition conflict is that there is currently no specific indicator to measure the degree of command recognition conflict, and which of the three different control methods the degree of command recognition conflict corresponds to.

[0038] Therefore, the present invention includes a signal processing module, a processing gain module, a conflict detection module and a conflict analysis module, a conflict control module, and a database. The signal processing module parses the command signal sent by the client, calculates and extracts the rate RC of the spread spectrum code, the data bit rate RB, the channel utilization rate X, the recognition response rate W, and the error rate C in the command signal;

[0039] The processing gain module calculates the processing gain value PG, PG = 10Lg (RC / RB), and extracts the parameter range NPG ~ MPG corresponding to the processing gain value PG in the database, and then calculates the excess value PG1 of the processing gain value relative to the PG parameter range NPG ~ MPG, and finally calculates the deviation rate PG2 of the processing gain value, PG2 = PG1 / [(NPG + MPG) / 2];

[0040] The conflict detection module extracts the corresponding channel utilization threshold X1, recognition response rate threshold W1, and error rate threshold C1 from the database, and then calculates the deviation rate X2 of the channel utilization, X2 = (X1-X) / X1;

[0041] Identify the deviation rate of the response rate W2, W2 = (W1-W) / W1;

[0042] Deviation rate of error rate C2, C2 = (C-C1) / C1;

[0043] Since the processing gain value reflects the efficiency and quality of instruction recognition, its deviation rate PG2 measures the degree of deviation between the current PG value and the ideal range (NPG~MPG), so the size of PG2 directly affects the stability and reliability of instruction recognition. However, in actual applications, the error is large when the deviation rate PG2 of the processing gain value is used alone to evaluate the impact of instruction recognition conflicts. Therefore, the deviation rate of channel utilization, the deviation rate of query response rate, and the deviation rate of error rate are introduced. Among them, the change of the deviation rate of channel utilization reflects the fluctuation of the network performance of the instruction identifier, the deviation rate of the query response rate reflects the change of the transmission efficiency of the instruction identifier, and the deviation rate of the error rate reflects the reliability of the instruction identifier when processing data. The introduction of the three performance indicators has a great impact on the efficiency and quality of instruction recognition. In order to further find the degree of influence of the three performance indicators on instruction recognition, the three performance indicators are normalized and the conflict deviation value is defined. The weight coefficient calculation steps in the formula of the conflict deviation value Q are as follows;

[0044] S1, when other environmental factors remain unchanged, the channel utilization and query response rate factors are set unchanged, and the error rate is collected in groups of 10 data;

[0045] Similarly, two sets of data on channel utilization and query response rate are collected respectively;

[0046] S2, unify the three sets of data into the deviation rate of channel utilization, the deviation rate of query response rate, and the deviation rate of error rate;

[0047] S3, taking the instruction recognition efficiency of the system under normal operation in the same environment as the reference value, calculate the influence of the error rate deviation rate change on the instruction recognition efficiency when the deviation rate of the channel utilization rate and the deviation rate of the query response rate are the same; similarly, calculate the influence of the deviation rate of the channel utilization rate and the deviation rate of the query response rate respectively;

[0048] A set of weight coefficients is initially set according to three different impact values;

[0049] S4, repeat steps S1-S3, the AI ​​big data model continuously iteratively adjusts the weight coefficient until the adjustment of the weight coefficient has little effect on the instruction recognition efficiency, finally determine the weight coefficient and calibration value, and obtain the formula for the conflict deviation value Q, Q = X2×0.3+W2×0.4+C2×0.3+ζ, where ζ represents the calibration value of the conflict deviation. The conflict analysis module calculates the conflict value based on the deviation rate of the processing gain value and the conflict deviation value, and then compares the conflict value, the deviation rate of the processing gain value and the conflict deviation value with the corresponding threshold value. Finally, after comparison, three different types of conflict analysis results are obtained: instruction recognition conflict, signal transmission interference, and instruction recognition minor conflict;

[0050] The conflict control module receives three different types of conflict analysis results, and selects different control methods to alleviate conflicts according to the conflict analysis results. The different control methods include starting a distributed standby instruction identifier, changing a spread spectrum code, or enabling different frequencies to receive instruction signal commands.

[0051] In order to obtain the most accurate conflict value and to achieve the most appropriate switching for the three different processing methods, a comparative analysis is performed on the deviation rate of the processing gain value and the conflict deviation value. Through the AI ​​big data model, continuous simulation experiments are conducted to simulate the impact of different weight coefficients on the instruction recognition performance. Finally, the weight coefficient and calibration value are determined, and the formula for the conflict value Y is obtained, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict.

[0052] The weight coefficient in the formula of the conflict value Y is obtained by collecting the deviation rate of the processing gain value and the conflict deviation value, simulating the impact of different weight coefficients on the instruction recognition performance through simulation experiments, and finally determining the weight coefficient and calibration value to obtain the formula of the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict.

[0053] Further, the conflict analysis module calculates the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict, and Y is greater than 0.2, indicating that an instruction recognition conflict occurs, otherwise, it is normal;

[0054] If PG2 is greater than 0.1, it means that the signal transmission is disturbed, otherwise, it is normal;

[0055] On the premise that both Y and PG2 are normal, the conflict deviation value Q is greater than 0.3, indicating that the instruction recognition has a slight conflict; otherwise, it is normal.

[0056] Further, when the conflict control module receives a command to identify a conflict, it starts a distributed standby command identifier to perform distributed processing on the command signal;

[0057] When the signal transmission is interfered, the spread spectrum code is replaced, the reflected signal is spread spectrum processed, and the processing gain value PG is adjusted until it is normal;

[0058] When there is a slight conflict in command recognition, multiple different frequency channels are enabled to distinguish different command signals, and the corresponding recognition method is selected according to the different frequencies of the received command signals.

[0059] Furthermore, the conflict analysis module processes the conflict value Y and the deviation rate PG2 with a higher priority than the conflict deviation value Q. Only when both Y and PG2 are normal will the conflict deviation value Q be further judged. The conflict value Y and the deviation rate PG2 have the same priority.

[0060] Furthermore, when the processing gain value PG is within the parameter range NPG to MPG, the excess value PG1 is 0 and the deviation rate PG2 of the processing gain value is 0.

[0061] Furthermore, the channel utilization rate refers to the proportion of time that the channel between the hotel client and the command recognizer is effectively utilized within a certain period of time;

[0062] The recognition response rate represents the rate at which the command recognizer recognizes commands;

[0063] The error rate indicates the error rate of the command recognizer in recognizing the command signal.

[0064] Furthermore, when the conflict control module receives normal signals, the distributed instruction identifier does not work, and when working normally, one frequency channel can meet the demand.

[0065] When the present invention is used specifically, based on the prior art, the signal processing module parses the command signal sent by the client, calculates and extracts the spread spectrum code rate RC, data bit rate RB, channel utilization rate X, recognition response rate W, and error rate C in the command signal;

[0066] The processing gain module calculates the processing gain value PG, PG = 10Lg (RC / RB), and extracts the parameter range NPG ~ MPG corresponding to the processing gain value PG in the database, and then calculates the excess value PG1 of the processing gain value relative to the PG parameter range NPG ~ MPG, and finally calculates the deviation rate PG2 of the processing gain value, PG2 = PG1 / [(NPG + MPG) / 2];

[0067] The conflict detection module extracts the corresponding channel utilization threshold X1, query response rate threshold W1, and error rate threshold C1 from the database, and then calculates the deviation rate X2 of the channel utilization, X2 = (X1-X) / X1;

[0068] Deviation rate of query response rate W2, W2 = (W1-W) / W1;

[0069] Deviation rate of error rate C2, C2 = (C-C1) / C1;

[0070] Finally, the conflict deviation value Q is calculated, Q=X2×0.3+W2×0.4+C2×0.3+ζ, where ζ represents the calibration value of the conflict deviation.

[0071] The conflict analysis module calculates the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict, and Y greater than 0.2 indicates that an instruction recognition conflict occurs, otherwise it indicates normal;

[0072] If PG2 is greater than 0.1, it means that the signal transmission is disturbed, otherwise, it is normal;

[0073] On the premise that both Y and PG2 are normal, the conflict deviation value Q is greater than 0.3, indicating that the instruction recognition has a slight conflict; otherwise, it is normal.

[0074] When the conflict control module receives a command to identify a conflict, it starts a distributed standby command identifier to perform distributed processing on the command signal;

[0075] When the signal transmission is interfered, the spread spectrum code is replaced, the reflected signal is spread spectrum processed, and the processing gain value PG is adjusted until it is normal;

[0076] When there is a slight conflict in command recognition, multiple different frequency channels are enabled to distinguish different command signals, and the corresponding recognition method is selected according to the different frequencies of the received command signals. The above method can detect the command recognition conflict status in real time, and determine the different types of command recognition conflicts based on the various parameters of detecting command recognition conflicts, and make corresponding processing in time, thereby reducing the adverse effects of command conflicts on the hotel intelligent service system.

[0077] The above is a further detailed description of the present invention in combination with a specific implementation method, and it cannot be determined that the specific implementation of the present invention is limited to this; for technical personnel in the technical field to which the present invention belongs and related technical fields, based on the technical solution of the present invention, the expansion and replacement of operating methods and data should all fall within the protection scope of the present invention.

Claims

1. The hotel intelligent service system based on AI big data is characterized by: It includes a signal processing module, a processing gain module, a conflict detection module and a conflict analysis module, a conflict control module, and a database. The signal processing module parses the command signal sent by the customer, calculates and extracts the spread spectrum code rate RC, data bit rate RB, channel utilization rate X, recognition response rate W, and error rate C in the command signal; The processing gain module calculates the processing gain value PG, PG = 10Lg (RC / RB), and extracts the parameter range NPG ~ MPG corresponding to the processing gain value PG in the database, and then calculates the excess value PG1 of the processing gain value relative to the PG parameter range NPG ~ MPG, and finally calculates the deviation rate PG2 of the processing gain value, PG2 = PG1 / [(NPG + MPG) / 2]; The conflict detection module extracts the corresponding channel utilization threshold X1, recognition response rate threshold W1, and error rate threshold C1 from the database, and then calculates the deviation rate X2 of the channel utilization, X2 = (X1-X) / X1; Identify the deviation rate of the response rate W2, W2 = (W1-W) / W1; Deviation rate of error rate C2, C2 = (C-C1) / C1; Finally, the conflict deviation value Q is calculated, Q = X2×0.3+W2×0.4+C2×0.3+ζ, where ζ represents the calibration value of the conflict deviation; The conflict analysis module calculates the conflict value according to the deviation rate of the processing gain value and the conflict deviation value, and then compares the conflict value, the deviation rate of the processing gain value and the conflict deviation value with the corresponding threshold value, and finally obtains three different types of instruction conflict analysis results after comparison: instruction recognition conflict, signal transmission interference, and instruction recognition slight conflict; The conflict control module receives three different types of conflict analysis results, and selects different control methods to alleviate conflicts according to the conflict analysis results. The different control methods include starting a distributed standby instruction identifier, changing a spread spectrum code, or enabling different frequencies to receive instruction signal commands.

2. According to claim 1, the hotel intelligent service system based on AI big data is characterized in that: The conflict analysis module calculates the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict, and Y greater than 0.2 indicates that an instruction recognition conflict occurs, otherwise it indicates normal; If PG2 is greater than 0.1, it means that the signal transmission is disturbed, otherwise, it is normal; On the premise that both Y and PG2 are normal, the conflict deviation value Q is greater than 0.3, indicating that the instruction recognition has a slight conflict; otherwise, it is normal.

3. According to claim 2, the hotel intelligent service system based on AI big data is characterized in that: When the conflict control module receives a command to identify a conflict, it starts a distributed standby command identifier to perform distributed processing on the command signal; When the signal transmission is interfered, the spread spectrum code is replaced, the reflected signal is spread spectrum processed, and the processing gain value PG is adjusted until it is normal; When there is a slight conflict in command recognition, multiple different frequency channels are enabled to distinguish different command signals, and the corresponding recognition method is selected according to the different frequencies of the received command signals.

4. According to the hotel intelligent service system based on AI big data according to claim 1, it is characterized in that: The steps for calculating the weight coefficient in the conflict deviation value Q formula are as follows; S1, when other environmental factors remain unchanged, the channel utilization rate and recognition response rate factors are set unchanged, and the error rate is collected in groups of 10 data; Similarly, two sets of data on channel utilization and recognition response rate are collected respectively; S2, unify the three sets of data into the deviation rate of channel utilization, the deviation rate of recognition response rate, and the deviation rate of error rate; S3, taking the instruction recognition efficiency of the system under normal operation in the same environment as the reference value, calculate the influence of the error rate deviation rate change on the instruction recognition efficiency when the deviation rate of the channel utilization rate and the deviation rate of the recognition response rate are the same; similarly calculate the influence of the deviation rate of the channel utilization rate and the deviation rate of the recognition response rate respectively; A set of weight coefficients is initially set according to three different impact values; S4, repeat steps S1-S3, and the AI ​​big data model continuously iteratively adjusts the weight coefficient until the adjustment of the weight coefficient has little effect on the instruction recognition efficiency. Finally, the weight coefficient and calibration value are determined to obtain the formula for the conflict deviation value Q, Q = X2×0.3+W2×0.4+C2×0.3+ζ, where ζ represents the calibration value of the conflict deviation.

5. According to claim 1, the hotel intelligent service system based on AI big data is characterized in that: The conflict analysis module processes the conflict value Y and the deviation rate PG2 with higher priority than the conflict deviation value Q. Only when both Y and PG2 are normal will the conflict deviation value Q be further judged. The conflict value Y and the deviation rate PG2 have the same priority.

6. The hotel intelligent service system based on AI big data according to claim 1 is characterized in that: When the processing gain value PG is within the parameter range NPG to MPG, the excess value PG1 is 0 and the deviation rate PG2 of the processing gain value is 0.

7. The hotel intelligent service system based on AI big data according to claim 1 is characterized in that: Channel utilization refers to the proportion of time that the channel between the hotel client and the command identifier is effectively used within a certain period of time; The recognition response rate represents the rate at which the command recognizer recognizes commands; The error rate indicates the error rate of the command recognizer in recognizing the command signal.

8. The hotel intelligent service system based on AI big data according to claim 2 is characterized in that: The weight coefficient in the formula of the conflict value Y is obtained by collecting the deviation rate of the processing gain value and the conflict deviation value, simulating the impact of different weight coefficients on the instruction recognition performance through simulation experiments, and finally determining the weight coefficient and calibration value to obtain the formula of the conflict value Y, Y=PG2×0.3+Q×0.7+ε, where ε represents the calibration value of the conflict.

9. The hotel intelligent service system based on AI big data according to claim 3 is characterized in that: When the conflict control module receives normal signals, the distributed instruction identifier does not work. When it works normally, one frequency channel can meet the demand.

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